Many businesses are still relying on legacy Magento 1 setups that no longer meet the demands of modern eCommerce. Upgrading is critical, and rarely a simple task, especially when the store has custom features and connects to multiple external systems. Here’s how we helped Gear‑Up move to a fast, high-performing site on Magento 2 and Hyvä.

About

Gear‑Up is a long-standing electronics and computing retailer based in Dubai, serving everyday consumers and professionals across the Middle East. Since launching in 2013, the company has grown into a trusted online destination for tech products.

When they came to scandiweb, Gear‑Up was still running on a Magento 1 store that hadn’t been updated in over a decade. It was slow, outdated, and no longer fit for the scale of their operations. A previous attempt to migrate to Magento 2 with another partner had failed, so this time, the stakes were high. They needed a partner who could make the migration work.

Project goals

Gear‑Up came to us with a clear set of priorities:

• Migrate their store from Magento 1 to Magento 2 without losing any business-critical data
• Rebuild all existing custom logic and workflows to match how their team operated
• Deliver a modern, fast frontend with improved performance and UX
• Implement a multi-currency setup with store-specific tax and price rounding rules
• Ensure accurate pricing display and tax calculation across third-party systems

They also saw the migration as a chance to modernize their store’s look and feel, aligning the frontend with their brand and growing product catalog.

Also read:
From Slow to Sleek: A UI-Focused eCommerce Redesign

Challenges

The biggest challenge was the data itself. Gear‑Up had more than ten years of order history stored in Magento 1, much of it tied to custom fields and logic that were never part of the platform by default. Every order, product, and customer record carried legacy data the business relied on, and all of it had to make it through the migration without breaking.

The second challenge was architectural. Gear‑Up’s internal workflows were built around how Magento 1 handled order management, but Magento 2 works differently. To replicate those flows accurately, we had to reverse-engineer and rebuild the logic. It was also important to make sure old and new orders could be processed side by side without disrupting daily operations. That required close collaboration with the business team, testing across edge cases, and iterating until every part worked as expected.

Another complexity was pricing. The store operates in seven currencies, each with its own exchange rate, tax rules, and custom rounding logic. On top of that, the rounding had to be consistently reflected across all third-party systems: including Klevu search, Tabby and Tamara payment widgets, and Xero invoicing. Some store views required fully rounded prices, while others needed prices with decimals, depending on market conventions. This wasn’t something Magento handled out of the box, so we built a custom backend solution.

Approach

The goal was to build a store that felt familiar to the Gear‑Up team but performed like a modern eCommerce platform. To get there, we rebuilt their setup using Magento 2 as the foundation, creating a scalable architecture that could grow with the business.

On the frontend, we used Hyvä to deliver a faster, more responsive experience for users and developers. It gave the team more flexibility while significantly improving site speed.

Also read:
Hyvä Case Studies: Real Results and Success Stories in Web Performance and User Experience

We also recreated all of Gear‑Up’s custom logic from Magento 1. That included order flows, pricing rules, and backend processes that were essential to the team’s everyday work.

To handle the multi-currency pricing challenge, we built a custom backend module from scratch with its own database structure. This let us apply the right rounding and tax rules for each market and made sure the prices showed up correctly everywhere: Klevu, Xero, Tabby, and Tamara.

In order to move more than a decade of historical data, we developed advanced migration scripts that could extract, clean, and map thousands of records. Some of those entries went back over ten years.

The entire migration and launch took place during a carefully coordinated 8-hour window, with half that time spent on the data import alone.

Results

The migration wasn’t simple, but the outcome proved it was worth the effort. Gear‑Up’s new store launched without disruption and carried over all critical functionality. Here’s what the project achieved:

• Stable launch with full functionality preserved
• Seamless switch with no critical downtime
• +47.7% orders YoY
• +110.9% revenue YoY
• +124K clicks
• +9.68M impressions

The Gear‑Up team was satisfied with the results and continues working with scandiweb via Support Cloud and additional SEO services.

Still relying on Magento 1 or need a frontend overhaul? Scandiweb is trusted by electronics retailers and enterprise merchants worldwide to deliver secure, stable, and scalable Magento solutions. We’re also a proud Hyvä Platinum Partner. Let’s talk about how to build a future-ready store that doesn’t leave your legacy behind.

The post Case Study: Magento 2 Migration and Hyvä Frontend Improve YoY Sales by 111% appeared first on scandiweb.

Choosing the right Hyvä theme development company is key to improving the performance and user experience of a Magento (Adobe Commerce) store. Hyvä has quickly become the go-to frontend framework for Magento, giving merchants faster sites and a more efficient development workflow at lower cost.

In this article, we’ve gathered a list of the top Hyvä theme development companies offering Hyvä theme development services based on their expertise, client satisfaction, and service quality. Each has proven experience in Magento and Hyvä, with different strengths to match a variety of business needs.

Overview

• Opting for specialized Hyvä theme development companies enhances Magento (Adobe Commerce) store performance and user experience.
• Hyvä themes significantly reduce page load times and development costs, improving SEO and overall store success.
• Key criteria for selecting a Hyvä theme development company include technical expertise, client feedback, and ongoing support services.

The best Hyvä theme development companies for Magento stores

Partnering with a specialized Hyvä theme development company guarantees your Magento (Adobe Commerce) store benefits from the latest frontend practices. Here are 10 companies that excel in Hyvä frontend theme development:

1. scandiweb
2. MageComp
3. Webkul
4. BSS Commerce
5. Aheadworks
6. Mageplaza
7. Mageworx
8. Mirasvit
9. Swissuplabs
10. Amasty

These companies are recognized for their proven Hyvä expertise, contributions to the Magento ecosystem, and ability to deliver results for merchants worldwide.

scandiweb

Recognized as a Hyvä Platinum Partner and the world’s most certified Magento agency, scandiweb is considered among the global leaders in Hyvä theme development. With a dedicated Hyvä team and extensive Magento (Adobe Commerce) experience, scandiweb delivers high-performing, scalable storefronts and bespoke solutions that prioritize Core Web Vitals, SEO, and long-term maintainability.

Scandiweb’s expertise in implementing Hyvä themes is well-established, thanks to their deep understanding of Magento and eCommerce ecosystems and experienced Hyvä developers. They leverage the Hyvä framework to create high-performance, aesthetically pleasing storefronts that load faster and provide an engaging user experience. A team of certified developers ensures that every Hyvä theme implementation is tailored to the client’s unique needs.

Their track record includes a wide range of Hyvä implementations across industries:

• ATX Fitness: Full Magento 2 + Hyvä Commerce launch in 2.5 months, all Core Web Vitals green and 93–100 PageSpeed scores
• Läderach: +47.8% conversions, +39% revenue, and 93–99 PageSpeed scores after migrating to Hyvä + the Design Curve Award at the 2023 Meet Magento NYC
• Airthings: +56% user engagement and notable drop in cart abandonment + the Design Pioneer Award at the 2024 Meet Magento NYC
• JYSK: +58% unique purchases and +20% in checkout conversion
• Byggmax: PDP PageSpeed improved from 70 to 87; PLPs from 85 to 99
• Nicokick: Homepage PageSpeed increased by +60 points (up to 96); CWV improved by 4,000% on desktop

In addition to full builds, scandiweb offers extension compatibility audits, Hyvä Checkout implementations, and support for Hyvä CMS. Their collaborative process and focus on performance optimization make them the top choice for ambitious brands looking to scale with Magento and Hyvä.

scandiweb’s Magento community expertise and commitment to excellence make them a top choice for implementing a Hyvä storefront.

MageComp

MageComp specializes in Hyvä theme development, catering to a diverse clientele. Based in India, they offer cost-effective solutions and over 40 extensions to enhance eCommerce operations. They are known for their rapid project turnaround times and good customer service. MageComp also provides comprehensive training and documentation to help clients maximize the benefits of their Hyvä themes.

Webkul

Based in India, Webkul is known for its work in Hyvä theme development, offering over 5 Hyvä-compatible extensions and custom solutions. Their strong commitment to customer satisfaction makes them a preferred partner for optimizing eCommerce platforms.

BSS Commerce

BSS Commerce specializes in Magento extensions and custom development. They offer Hyvä-compatible modules tailored for Magento stores, highlighting their commitment to quality and compatibility. Their dedication to continuous improvement and innovation makes them a trusted partner for Magento store owners seeking to enhance their eCommerce platforms.

Aheadworks

Based in the United States, Aheadworks enhances eCommerce performance with Hyvä-compatible products. Their successful Magento projects and advanced extensions make them a go-to partner for optimizing Magento frontends. Aheadworks focuses on technical expertise to ensure your store operates at peak performance.

Mageplaza

Mageplaza offers services focused on SEO and conversion optimization with Hyvä-compatible extensions. In addition to their SEO-focused extensions, Mageplaza provides a wide range of tools designed to streamline store management and improve overall site performance. Their robust customer support ensures that any issues are promptly addressed, helping eCommerce businesses maintain optimal functionality.

Mageworx

Mageworx develops advanced eCommerce solutions to enhance store performance and user experience. They offer over 15 Hyvä-compatible products and have a team of seasoned developers that deliver solutions tailored to each client’s unique needs. Mageworx also provides commendable customer support, helping businesses maximize the benefits of their Hyvä themes.

Mirasvit

Mirasvit is known for high-quality Magento extensions and custom development services, including Hyvä-compatible extensions. Mirasvit’s dedication to quality and performance optimization makes them a preferred choice for Magento stores. Their continuous focus on innovation and improvement helps businesses stay ahead in the competitive eCommerce landscape.

Swissuplabs

Swissuplabs, based in Switzerland, provides extensions and customization options specifically for Hyvä themes, ensuring quality and innovation. Their extensive experience in the Magento ecosystem allows them to develop robust and reliable solutions tailored to individual business needs. Swissuplabs’ commitment to continuous improvement and customer satisfaction makes them a trusted partner for enhancing eCommerce platforms.

Amasty

Amasty provides comprehensive Hyvä theme development, integration, and support. Their tailored solutions ensure fast load times and Magento 2 compatibility. They also offer a wide range of extensions and modules that further enhance the functionality and performance of Hyvä themes, making them a versatile choice for any Magento store.

Benefits of choosing Hyvä for your Magento store

The Hyvä theme is transforming Magento (Adobe Commerce) with its modern frontend framework, significantly enhancing performance and aesthetics. Notably, it drastically reduces page load times, with some stores seeing reductions from over 16 seconds to under 3 seconds. This boost in performance leads to a better user experience, higher engagement rates, and increased conversion metrics for eCommerce businesses.

Additionally, the Hyvä theme offers cost-effective development, making it an attractive solution for optimizing Magento (Adobe Commerce) stores without high expenses. Its focus on performance and user experience also enhances SEO, which is crucial for maintaining a strong search engine presence.

Faster page load times and improved Core Web Vitals

Hyvä strips away heavy JavaScript and legacy code, replacing it with Tailwind CSS and Alpine.js. Stores often see PageSpeed scores jump from the 60–70 range to 90+ on both desktop and mobile. Faster load times support better SEO, lower bounce rates, higher conversion rates, and improved overall store performance.

Lower development and maintenance costs

Hyvä’s lean architecture speeds up development and simplifies future updates. Teams spend less time debugging complex frontend issues or dealing with bloated themes, reducing build time and ongoing costs, while creating exceptional user experiences.

Built for SEO and a better user experience

Improved performance leads directly to better rankings and engagement. With fewer layout shifts, faster interactivity, and smoother browsing across devices, Hyvä supports organic growth and on-site behavior metrics.

Criteria for selecting a Hyvä theme development company

Choosing the right Hyvä development company is crucial for aligning with your business objectives and technical needs. The Hyvä theme permits faster and more efficient development than traditional Magento (Adobe Commerce) themes, necessitating a company with proven expertise in Hyvä theme development.

Key criteria include technical expertise, client reviews and testimonials, and support and maintenance services.

Technical expertise

Many extension makers see Hyvä as a game-changing frontend theme for Magento 2, emphasizing the need for developers to stay updated on compatibility issues. Choosing a company with proven technical expertise in implementing Hyvä themes ensures effective eCommerce solutions.

Using TailwindCSS and AlpineJS to create visually appealing and efficient frontend themes underscores the importance of technical proficiency.

Client reviews and testimonials

Evaluating client feedback and testimonials is vital for understanding the reliability and quality of Hyvä theme development services. Companies like scandiweb are noted for their extensive eCommerce and Magento experience, tailoring bespoke solutions that enhance store performance and customer satisfaction. Positive reviews and high-quality work indicate a company’s ability to deliver exceptional results.

Support and maintenance services

Ongoing support and maintenance are vital for the long-term performance and updates of Hyvä themes. Choosing a company that prioritizes maintenance can result in continuous improvements and timely issue resolution, ensuring smooth operation of your eCommerce platform.

Companies with strong support and maintenance services enhance user experience and overall store performance.

Hyvä services offered by scandiweb

Scandiweb is a top choice for implementing the Hyvä theme, offering services that ensure high performance and a seamless user experience. They use a collaborative approach with certified developers to tailor the Hyvä theme implementation to your needs. This customization reflects your brand’s identity and design preferences, creating a unique and engaging storefront.

Scandiweb’s Hyvä theme development services

1. Hyvä theme installation and setup
2. Custom Hyvä theme development
3. Performance optimization
4. Migration to Hyvä
5. Continuous support and maintenance
6. Training and consultation
7. Integrations
8. SEO and digital marketing for Hyvä

After implementation, scandiweb offers ongoing support, updates, and enhancements to maintain optimal store performance with new advanced features. Their commitment to excellence and customer satisfaction ensures your Magento store remains competitive with improved page load times and overall performance. With scandiweb, your Hyvä theme project is in capable hands, delivering exceptional results.

Success stories of Hyvä theme implementations

Real-world success stories highlight the transformative impact of Hyvä themes on Magento stores. For instance, Läderach saw a remarkable 47.8% increase in conversions after migrating to Hyvä, showing how performance and user experience enhancements drive significant business results. Similarly, Airthings experienced a 56% rise in user engagement following their switch to Hyvä, underscoring the theme’s ability to captivate and retain users.

Read more about these projects and other successful Hyvä implementations here:

A contribution to the Hyvä ecosystem

Scandiweb’s team has developed Hyvä’s first-ever open-source theme: Satoshi. It’s a production-ready theme that sets a new standard for user experience while maintaining the unparalleled performance of the Hyvä frontend. Available to all Hyvä users, it offers a seamless way to enhance store navigation, interactions, and overall shopping flow without starting from scratch.

Conclusion

Selecting the right Hyvä theme development company is crucial for unlocking the full potential of your Magento (Adobe Commerce) store. You can benefit from enhanced site performance and cost-effective development, improved SEO, and more. If you choose a specialized company like scandiweb, you can ensure that your store looks great and performs exceptionally well.

Remember to consider technical expertise, client reviews, and support services when choosing a development partner. Success stories from companies like Läderach and Airthings illustrate the transformative impact of Hyvä themes on eCommerce businesses. With the right partner and a focus on performance and user experience, your Magento store can achieve outstanding results.

Interested in launching a fast, scalable Magento store with Hyvä theme or Hyvä Commerce? As the Hyvä Platinum Partner, scandiweb is among the top Hyvä development companies globally. Let’s connect and discuss building the right setup for your business.

Frequently Asked Questions

What are the key benefits of using Hyvä themes for my Magento store?

Utilizing Hyvä themes for your Magento (Adobe Commerce) store greatly enhances site performance and user experience, leading to improved SEO and reduced development costs. The improved performance of your site can lead to higher customer satisfaction and increased sales.

How do I choose the right Hyvä theme development company?

Choosing the right Hyvä development company involves evaluating their technical expertise, client reviews, and the support and maintenance services they offer. Companies like scandiweb have established a solid reputation in this area.

Why is scandiweb a top choice for Hyvä theme development?

Scandiweb’s Hyvä theme development services stand out due to their tailored solutions, certified developers, and commitment to ongoing Hyvä support, ensuring optimal performance and user experience for your Magento store. Their expertise guarantees a seamless transition to a Hyvä storefront, maximizing your eCommerce potential.

Can Hyvä themes really improve my store’s SEO performance?

Yes, Hyvä themes can significantly improve your store’s SEO performance by optimizing code and reducing load times, both of which are crucial for higher search engine rankings. This optimization can lead to increased visibility and engagement for your online store.

What kind of success can I expect from implementing a Hyvä theme?

Implementing a Hyvä theme can lead to substantial increases in conversions and user engagement, as evidenced by success stories from brands like Läderach and Airthings. This clearly demonstrates the positive impact Hyvä themes can have on eCommerce performance.

Will my existing store extensions be compatible with Hyvä?

Hyvä themes are designed to be compatible with most Magento (Adobe Commerce) extensions. However, some older third-party extensions may require updates or modifications to work seamlessly with the Hyvä framework. It’s advisable to consult with your Hyvä theme development company to assess the compatibility of your current extensions and make any necessary adjustments. Many leading Hyvä theme developers, like scandiweb, offer services to ensure smooth integration and optimal performance of your store extensions.

Why is Hyvä better than my current theme?

Hyvä themes offer a streamlined Magento frontend design that boosts performance through minimal use of JavaScript and modern tools like Tailwind CSS, enhancing site speed and user experience. Their simpler customization and reduced dependency on third-party extensions also lower overall maintenance costs, making them a cost-effective choice for store owners.

The post Top Hyvä Theme Development Companies for Magento Stores appeared first on scandiweb.

AI adoption is no longer a future consideration and it has also moved past experimentation. It’s here. Already transforming how companies function across marketing, sales, product development, and internal operations. As the role of AI agencies has become more strategic, knowing how to start or scale AI initiatives often depends on choosing the right partner.

What is an AI agency?

‘AI agency’ can mean anything from an R&D lab to a no-code tool vendor to a full-service implementation team. Some specialize in strategy and prototyping, others focus on deployment, integration, or use-case-specific applications like eCommerce or finance. With an AI agency, you can automate product data flows, develop internal agents, integrate LLMs, enhance AI search visibility, and so much more.

This article rounds up a list of top AI agencies building functioning AI solutions across industries. We hope this gives you a practical starting point for finding an agency that knows how to move from plan to production.

What AI services do AI agencies deliver?

AI agencies cover a broad spectrum of applied AI services, ranging from content generation to full-scale AI system deployment. Here’s a look at the most common areas where they’re helping businesses see results:

• Custom LLM applications – tools like internal copilots, product recommendation engines, and AI assistants tailored to your workflows
• Answer engine optimization (AEO) – structuring and optimizing content to appear in AI-generated answers across platforms like ChatGPT, Claude, and Perplexity
• AI-powered content operations, including automated SEO content, AI-assisted QA, and post-purchase flows
• Customer service automation: chatbots, GPT-powered agents, and hybrid support tools
• Product data automation with AI systems that clean, enrich, or classify product information across PIMs and marketplaces
• MLOps and infrastructure, building the pipelines, environments, and tools needed to get models into production
• Strategic AI consulting – roadmap design, use case prioritization, and hands-on workshops to move teams from ideas to working systems.

A list of the top 10 AI agencies

1. scandiweb

Scandiweb focuses on building real, operational AI solutions that live inside a business. Their specialty lies in uncovering hidden bottlenecks that teams have grown used to and accepted as “just how it is” and replacing them with AI systems that cut manual speed up processes and change how the job gets done entirely.

Rather than starting with tools or ideas, scandiweb starts with operational blockers and asks: what could we do now that wasn’t possible before? This mindset powers their framework called P.A.T.H., which has already helped over 30 companies in 15+ industries move from vague AI ambition to live production deployments in just 6–12 weeks. They’ve helped eCommerce brands automate product data enrichment and publishers replace thousands of hours of manual QA. Scandiweb’s AI solutions are engineered to run in real environments without getting stuck in slides or strategy decks.

Best for

Large enterprises ready to go beyond AI ideas and build working AI solutions embedded in their operations, especially in eCommerce, logistics, and retail.

Case studies

• Global retail brand used scandiweb’s P.A.T.H. method to identify and replace a 20,000-hour manual bottleneck with an AI-driven enrichment and QA system.
• DTC health & wellness brand deployed a 24/7 AI sales assistant that guided customers through complex DIY projects, leading to higher AOV, 5x longer engagement, and lower support load.
• Multinational retailer scaled live shopping with AI avatars, reducing campaign costs 10x while increasing conversion by 70% and add-to-cart rates by 192%.

2. BetterAnswer

BetterAnswer is the go-to AI agency for AEO (answer engine optimization) – a growing service focused on helping brands show up as the top result when people ask AI for recommendations. While most SEO strategies stop at Google, BetterAnswer optimizes inside large language models (LLMs).

Their process combines deep SEO knowledge, technical content structuring, LLM behavior research, and reputation building to ensure your brand shows up first with the kind of detail AI engines trust. BetterAnswer helps build LLM-friendly content, implement schema, earn citations from AI-trusted sources, and track rankings across AI engines using their proprietary tool AnswerRank.

Best for

Brands that want to gain visibility and traffic inside ChatGPT, Google AI Overviews, Perplexity, and other answer engines, especially in competitive industries.

Case studies

• A US artisan home decor brand went from zero visibility to top ChatGPT + Perplexity mentions, outperforming retail giants
  • +44% AI visibility
  • +54% AI referral traffic
  • +12% overall traffic. 
• A travel agency is now ranking first for AI prompts like “Which travel agency is the best?” across all 3 Baltic markets
  • +55% AI visibility
  • +140% AI referral traffic
  • +16% overall traffic.
• Enviropack now shows up as the top AI result for “eco-friendly food packaging suppliers in the UK”
  • +58% AI visibility
  • +82% AI referral traffic
  • +17% overall traffic.

Also read:
Best AEO Agencies to Help You Get Mentioned in AI-Powered Results

3. Underwaterpistol

Underwaterpistol is a Shopify Plus agency with a dedicated AI consultancy built for eCommerce. Their work combines AI with customer experience, including automated support flows, content generation, and fully custom GPT-powered chatbots for Shopify storefronts. The agency brings a creative-first mindset, with automation designed to serve conversion and brand storytelling.

Best for

DTC brands using Shopify and looking to enhance customer support, automate content ops, and explore conversational commerce with AI chatbots.

4. Charle

Charle is a UK-based Shopify agency that weaves AI into every layer of the eCommerce experience: frontend development, CRO, LLM integration, and post-purchase flows. Their AI services span automation consulting, prompt engineering, and AI search visibility optimization. What sets them apart is their focus on AI as part of creative direction, not just technical performance.

Best for

Shopify brands ready to apply AI across design, performance, and support.

5. Absolute Web

Absolute Web is a commerce agency with a hands-on approach to AI search optimization. Their team focuses on the technical side that helps AI engines parse and trust product data: structured content, schema, site architecture, and performance. They complement this with practical publishing guidance to help businesses get their content AI-ready.

Best for

eCommerce teams looking to strengthen their AI visibility through structured data, schema, and content architecture.

6. Object Edge

Object Edge is a digital consultancy specializing in enterprise-grade AI integration, particularly across Salesforce and large-scale commerce stacks. Their offering includes strategic planning, generative AI prototyping, and full deployment of AI agents within customer service, product data management, and personalization. 

Best for

Companies needing full-stack AI strategy and implementation across Salesforce and complex internal tools.

7. Avex

Avex is a Shopify Plus partner offering AI-powered personalization, SEO content generation, GPT-enabled customer service, and smart QA. They mainly focus on lifestyle, fashion, and beauty brands, combining high-end UX and improving customer interaction with operational automation. 

Best for

Shopify Plus brands in lifestyle, fashion, and beauty verticals seeking to scale customer experience and content production using AI.

8. Winder.AI

Winder.AI is an applied AI and MLOps boutique that focuses on one thing: getting models into production. They train, fine-tune, and build AI services that run at scale. Most of their collaborations are with teams that already have data and use cases but need help building robust, performant, and secure systems around them.

Best for

Engineering-led teams looking to deploy AI pipelines and services.

9. Datatonic

Datatonic is a specialist in Vertex AI, LLMOps, and GenAI deployment. They support data-heavy organizations, especially those committed to Google Cloud, with production-ready infrastructure and AI systems. Datatonic helps bridge data strategy and AI engineering using data pipelines and customer-facing agents.

Best for

Organizations using Google Cloud and looking to integrate LLMs or GenAI at scale with production-ready infrastructure.

10. ML6

ML6 is a European AI studio that combines strategy, delivery, and long-term support. They’re known for helping businesses translate high-level goals into measurable AI systems, like GenAI roadmaps and LLM feature deployment. ML6 guides companies through experimentation and production.

Best for

Companies that need a long-term AI partner to align strategy, roadmap, and implementation across departments.

How to choose the right AI agency for your business

With so many directions AI can take, choosing the right agency comes down to your goals and how deeply you want AI integrated into your day-to-day operations.

Ask yourself:

• Do they solve problems or just build features?
• Do they specialize in your industry or use case?
• Can they deploy not just prototype?
• Do they align with your existing stack?
• Do they have proof?

Answers to these questions will guide you to the right AI agency that helps you move fast, allows you to see value early, and scale what works.

If you’re ready to explore what AI could do for your brand, book a free AI workshop with scandiweb or request your free AEO audit. 

The post Top AI Agencies in 2025: Who’s Leading AI Strategy & Development appeared first on scandiweb.

AI agents are already solving real problems, handling product enrichment, support tickets, forecasting, and internal workflows. And while chatbots are what most people still associate with AI agents, the reality is different. A chatbot answers questions. An AI agent gets things done: inside your systems and across your data.

That’s why more companies are now turning to AI agents to reduce manual work, speed up decision-making, and scale operations without growing headcount. But where do you start?

In this guide, we’ll give you a vetted list of 16 companies that know how to build and implement real AI agents. You’ll also find practical examples across eCommerce, sales, logistics, HR, and analytics, so you can see exactly where AI agents are already making an impact.

Key takeaways

• An AI agent is software that acts toward a goal; it uses reasoning, memory, and tools to complete tasks inside your systems, not just respond with text.
• To get the most out of AI agents, start with business problems, not tools: ask for a pilot tied to one KPI, then scale what works.
• Favor agencies that ship integrated workflows across ERP, CRM, PIM, and data warehouses, not just chat UIs.

What is an AI agent?

An AI agent is software that pursues a goal and takes actions to achieve it. It reads context, plans the next step, calls tools or APIs, updates systems, and checks the result against a target. Think of it as an autonomous layer that turns instructions and data into completed tasks.

Unlike a static chatbot that only replies, an agent can work inside your stack. It can check stock, create a draft purchase order, enrich a product record, or route a support ticket with a suggested resolution. In eCommerce, this might mean turning a shopper’s idea into a product list, checking a product feed before launch, handling returns, or flagging something unusual in your daily sales.

Every reliable agent has a few common parts. Inputs can be text, files, events, or database rows. Reasoning and policies decide what to do next. Memory keeps useful context, so the agent does not repeat itself. Tool access lets it act in ERPs, CRMs, PIMs, WMS, or analytics. Monitoring records what happened, and handoff rules send edge cases to humans.

What matters most is that well-designed agents are built around a clear KPI, not just clever prompts. They must record what they do, and be regularly tested to see how well they hit their goal. Tracking things like accuracy, speed, and cost helps teams stay focused on business impact and makes the agent safe to scale.

Best AI agent development agencies and companies

We reviewed dozens of agencies across the US and Europe and selected those that go beyond chatbots to deliver real AI agents: tools that take action inside live business systems. We focused on those with proven results, not just frameworks, and highlighted their strengths by use case and industry.

Also read:
Best AEO Agencies to Help You Get Mentioned in AI-Powered Results

1. scandiweb: Custom eCommerce agents for measurable ROI

scandiweb has spent 22+ years building and optimizing large-scale eCommerce ecosystems, delivering 2,100+ projects with 600+ in-house specialists and the world’s most certified Adobe Commerce and Hyvä teams. That depth matters when your agent must talk to Magento or Shopify, or any other eCommerce platform, besides respecting catalog logic, and updating analytics cleanly.

What they build:

• AI agents for merchandising, content, feed QA, support triage, analytics summaries, and AEO
• Multi-agent workflows that coordinate product data, marketing ops, and BI dashboards.
• Analytics copilots that summarize GA data, surface anomalies, and recommend actions.

Best for: If you need agents that live inside eCommerce processes (catalog, PDP/PLP, checkout, PIM, OMS), plus the analytics to prove ROI.

2. STX Next: Python-first agent engineering for data-heavy use cases

STX Next builds agents with a Python core and a strong data spine, then wires them into your internal systems. Their team emphasizes human-in-the-loop controls, dashboards, and gated rollouts, which suits regulated workflows. Typical projects include agents that fetch from internal APIs, process files, and post results back into CRMs, ERPs, or data warehouses. If you need a partner that treats the agent as a production microservice with observability rather than a demo bot, this is a solid fit.

Best for: Enterprises that want senior Python talent to connect agents to internal data sources and APIs.

3. 10Clouds: LLM products with RAG and vector search

10Clouds delivers retrieval-grounded assistants that draw on private knowledge bases, with LangChain and vector databases as standard building blocks. They focus on secure deployments, cloud readiness, and the practical details of permissions, tool calls, and evaluation so the agent stays on-task and auditable. Their work spans knowledge copilots, automation bots that call business tools, and production RAG systems.

Best for: Research assistants, knowledge copilots, and task agents that depend on accurate retrieval.

4. Neoteric: Conversation and decision agents for growth teams

Neoteric ships conversational agents that plug into marketing, sales, and support stacks, then tunes them on brand data and product context. Expect careful attention to prompt strategy, fine-tuning, and analytics hooks so teams can see what the agent answered and why. They also support broader AI builds when the use case needs prediction or classification alongside chat.

Best for: Support, pre-sales, and marketing assistants tied to CRM and CDP data.

5. Vstorm: Agentic automation for ops and BI

Vstorm focuses on multi-agent workflows that move data across tools, close loops, and report outcomes. Their consultants bring MLOps habits to agent projects, which helps with testing, rollout, and ongoing evaluation. If your current process bounces between sheets, tickets, and dashboards, they will model each step as a tool-using agent and orchestrate the flow.

Best for: Replacing manual hand-offs across spreadsheets, tickets, and dashboards.

6. MindTitan: Applied NLP and computer vision

MindTitan builds practical AI for the public and private sectors, with experience in NLP, CV, and robust data pipelines. Their work often targets service operations where quality of service and traceability matter, such as telecom and government use cases. They pair model work with the plumbing that feeds and monitors it.

Best for: Ticket deflection, routing, and classification with measured QoS.

7. Eleks: Enterprise AI with governance and integration depth

Eleks approaches agents as part of a larger transformation: data platforms, security, and integration patterns sit alongside the agent logic. They support agentic systems across finance, healthcare, energy, and manufacturing, and provide the governance scaffolding enterprises expect. If you need a partner that can manage multi-system rollouts with documentation and change control, add them to the shortlist.

Best for: Enterprises that need strong PMO, documentation, and compliance.

8. DataRoot Labs: R&D-grade LLM and multimodal builds

DataRoot Labs takes on higher-ambiguity work, including custom LLMs, RAG pipelines, and multimodal agents. They design evaluation harnesses so teams can measure progress and failure modes rather than rely on gut feel. When the problem requires research rigor and fast prototyping, their model-plus-engineering approach helps de-risk the path to production.

Best for: High-ambiguity projects and prototypes that need research rigor.

9. Systango: Analytics-led ML and anomaly detection agents

Systango builds agents that sit on top of predictive and anomaly-detection models to support finance, marketplace, and ops decisions. They combine data engineering with ML so agents can pull fresh signals, score risk, and trigger actions or human reviews. This is useful when outcomes must be tied to quantified thresholds rather than open-ended chat.

Best for: Finance, marketplaces, and ops teams that need data-first agents.

10. ITRex: Broad agent catalog and system design

ITRex offers a full catalog of agent types, from rule-based and goal-driven to learning agents, and handles the lifecycle from strategy to support. They are comfortable integrating with enterprise stacks and setting up the monitoring and guardrails needed for long-running automations. If you want one vendor to cover several departments, their scope fits.

Best for: Firms seeking a single partner for multiple agent types across departments.

11. HatchWorks: Strategy to shipped agents for product teams

HatchWorks pairs product strategy with delivery using a method they call Generative-Driven Development. Agents and autonomous workflows are woven into sprints, with clear ROI checkpoints and governance baked in. This suits product organizations that want working software on a predictable cadence, not a one-off experiment.

Best for: Product organizations that need a pragmatic path from idea to pilot to scale.

12. LeewayHertz: Build-to-spec agents using popular frameworks

LeewayHertz assembles task-focused agents with tools like AutoGen Studio, Vertex AI Agent Builder, and crewAI, then connects them to enterprise systems. Their value is speed: they map the use case to a known pattern and stand it up with logging, testing, and versioning so teams can iterate safely.

Best for: Rapid sprints to stand up specialized agents with known tools.

13. Markovate: Compact team for LLM agents and voice interface

Markovate develops LLM agents alongside voice interfaces for call handling, order capture, and field workflows. They can deliver a chat or voice layer and the integrations needed to fetch data, update records, and confirm actions in real time. That mix is useful when users are on the move or when phone traffic remains high.

Best for: Support and field workflows where voice matters.

14. ML6: Applied AI consultancy

ML6 is a European consultancy with deep data engineering and MLOps capabilities. They help teams move from models to reliable services and partner closely with major cloud providers. If your agents need strong pipelines, monitoring, and cost control on cloud, they bring the required discipline.

Best for: Enterprises that need reliable delivery and governance.

15. Xomnia: Data engineering and AI

Xomnia combines data engineering with agent builds so teams can stand up agents while modernizing ELT and monitoring. They frame the agent as a data product with iterative delivery and shared metrics, which helps cross-functional adoption. This approach suits organizations that need to fix data foundations while they ship value.

Best for: Organizations that must fix data foundations while rolling out agents.

16. Intercom: Customer service agents built for real-time support

Intercom focuses on AI agents that go beyond simple chatbots. Instead of just answering questions, these agents resolve issues, suggest resources, and escalate to humans when needed. They integrate with CRMs and help desks so every interaction is tied to customer history. For teams under pressure to shorten response times and reduce drop-offs, Intercom’s agents provide consistent, always-available support across chat, email, and in-app messaging.

Best for: Companies that want customer service agents to deliver instant, context-aware support across multiple channels.

How AI agents deliver real impact across different business functions

When connected to the right data and tools, AI agents can act, adapt, and support multiple parts of your operation, without needing a human to hand-hold every step. Below are practical examples of how AI agents are already helping teams get more done with less manual work.

Product information and content

Keeping catalogs accurate is a constant challenge. A product data agent reduces that burden by validating specs, correcting inconsistencies, and syncing with PIM or ERP feeds.

On the customer-facing side, content enrichment assistants step in to improve titles, descriptions, and feature lists while also helping with localization and SEO.

Merchandising and category management

Merchandising decisions often rely on guesswork, but a merchandising agent changes that by monitoring PLP data, margins, and stock levels, then suggesting new sort rules or groupings based on performance.

Campaigns also benefit from support: a promotion optimizer detects broken promo logic or overlapping discounts and recommends corrections, ensuring that promotions drive sales without undermining profit margins.

Customer service and support

In customer service, repetitive queries quickly overwhelm teams. Support copilots provide relief by answering FAQs, suggesting help articles, and routing complex issues to the right person.

Returns and complaints, another high-volume area, can also be automated: agents categorize incoming tickets, check order histories, and initiate refund or replacement workflows. Some systems also go a step further by turning email exchanges into a searchable knowledge base, making it easier for support teams to find answers and act quickly.

See a practical customer service agent walkthrough that shows how agents handle FAQs and escalate complex cases.

Sales and personalization

Personalization has become essential for digital sales. An onsite personalization agent adapts recommendations, banners, and messaging in real time by drawing on CRM and CDP data.

At the same time, cart abandonment agents reduce drop-offs by detecting exit signals and following up with tailored nudges through chat or email, escalating to a human when purchase intent is especially high. Together, these agents help recover lost revenue and make shopping experiences more relevant.

Explore how HubSpot form enrichment with AI improves lead quality and saves sales teams time.

Analytics and performance monitoring

Staying on top of performance requires constant vigilance. An analytics copilot scans sales, traffic, and conversion rates for spikes or drops, then summarizes anomalies in plain language and suggests next steps.

Looking further ahead, a forecasting assistant studies historical patterns in sales, returns, and customer behavior. Its projections inform inventory planning, marketing budgets, and staffing decisions, helping businesses prepare instead of react.

Operations and fulfillment

Finally, logistics also gain from agentic support. Supply and fulfillment agents keep an eye on carrier SLAs, stock levels, and delivery times, flagging issues and proposing alternatives such as split shipments or alternate routes.

On the other hand, order flow monitors ensure nothing gets stuck by checking statuses in real time and triggering alerts or fallback actions when errors appear.

Case study: How a Nordic DIY retailer scaled project guidance with a 24/7 sales assistant

A leading home-improvement chain in Sweden, Norway, Finland, and Denmark runs 200+ stores and exceeds $1B in annual revenue. Roughly 70% of annual revenue is concentrated between May and September, and customers come in with project ideas but no product lists. Staff expands by up to 10x, mostly temporary workers, and training is too slow and inconsistent.

In collaboration with Algoritma, we built a web-based ShopBot trained on common DIY projects and connected to the full product catalog. The assistant can advise on projects step by step and recommend SKUs, sizes, and quantities. Acting as a form of conversational commerce, it guides shoppers, generates a clear plan for customers, and supports store teams with consistent answers.

Also read:
How AEO Helped Enviropack Become a Top AI Pick for Sustainable Packaging

How to pick the best partner for AI agent development

Not every agency that builds with LLMs can deliver a reliable, goal-driven AI agent. The difference comes down to whether they treat your project as a production system or a demo. Choosing the right partner means asking the right questions—not just about model choice, but about systems, measurement, and long-term fit.

The strongest vendors will build around your KPI, understand how to work with your stack, and deliver something your team can trust, own, and improve over time. Use this checklist to compare vendors objectively and keep the focus on outcomes.

What to look for

Score each vendor from 1 to 5 on the items below.

• Business impact: Are success metrics clearly defined? Do they track accuracy, latency, cost per task, or similar KPIs?
• Data foundation: Can they access and work with data from your PIM, ERP, CRM, WMS, or analytics stack?
• Agent design: Do they explain how the agent handles memory, tool use, planning, fallbacks, and safety checks?
• Integration depth: Are webhooks, queues, API rate limits, and error handling built into the plan?
• Operations: Can they support CI/CD, secrets management, observability, and rollback? Is there a human-in-the-loop for edge cases?
• Governance and compliance: Do they have a plan for handling sensitive data (PII), red-teaming, approvals, and audit trails?
• Post-launch support: Can they monitor drift, fine-tune agents over time, and manage hosting costs?

Before you commit, ask every vendor to provide the following:

• A 3-week pilot with one clear KPI and a target success rate
• A handover pack that includes prompts, architecture diagrams, datasets, and runbooks
• An evaluation harness with example tasks, success thresholds, and test coverage
• A review process where a human approves outputs until performance is proven.

Expected future trends in AI agent development (2026–2027)

AI agents are maturing quickly. What started with single-task bots is evolving into systems that can plan, adapt, and collaborate across workflows. As adoption grows, we’re seeing clear patterns emerge around how agents are built, deployed, and governed. Here’s what AI experts are predicting for the future.

Rise of multi-agent systems

AI is moving from single-task assistants toward multi-agent systems that collaborate, critique each other, and divide complex goals into smaller steps. Forrester highlights this as part of the broader “AI agent pivot,” noting that real autonomy in production is still rare, but adoption is accelerating as frameworks mature. These systems are particularly valuable in scenarios that require planning, tool coordination, and oversight across multiple workflows.

RAG, self-hosting, and evaluation as the default stack

Retrieval-Augmented Generation (RAG) is becoming the baseline for grounded, context-aware agents. At the same time, many enterprises are turning to self-hosted or sovereign deployments to maintain data privacy, reduce latency, and control costs. This shift drives demand for evaluation and monitoring platforms such as HoneyHive, which help teams measure accuracy, safety, and cost-effectiveness before scaling agents.

Emphasis on safety, transparency, and regulation

Governance is no longer optional. The EU AI Act has entered force with obligations rolling out through 2026, while frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001 provide structured approaches to transparency, safety, and lifecycle control. Vendors that ship agents with audit trails, permission scoping, and measurable evaluation policies will face fewer roadblocks during compliance reviews.

Security as a new proving ground

Cybersecurity is becoming a key domain for AI agents. Startups like Qevlar are developing autonomous responders and analyst copilots, attracting strong investor interest. While Forrester notes that adoption in security operations is still in early stages, the potential is significant – especially for threat detection, incident triage, and automated response under human oversight.

Final thoughts

AI agents are already working behind the scenes: fixing product data, routing support tickets, flagging issues before they become problems. What’s changing is how well they’re integrated, how reliable they are, and how fast companies are putting them into production.

If you’re here, you’re not looking for another chatbot. You’re looking for something that solves real problems, connects with your systems, and supports your KPIs.

Start with one use case. Tie it to one metric. Scale what works.

And if you want a second opinion or need help getting started, we’re happy to share what we’ve learned building agents that actually ship.

Want help building your first AI agent? Join our AI Accelerator to define the use case, align on KPIs, and build a working agent in weeks. Includes workshop, roadmap, and up to 30% co-funding.

Frequently Asked Questions

What is a “real” AI agent?

A real AI agent is software that pursues a goal by planning steps, calling tools or APIs, and keeping context. Unlike a chatbot, it takes action inside systems and is measured against clear outcomes such as accuracy, speed, or cost per task.

How long does an agent take to ship?

With a clear KPI and existing APIs, a simple AI agent can go live in a few weeks. Broader, multi-system agents take longer once data work and governance are included.

What should I budget?

Expect an initial pilot in the tens of thousands, then ongoing spend for hosting, evaluation, and tuning. Data work often drives the timeline and cost.

How do we keep agents safe?

Agents are kept safe by using scoped permissions, applying rate limits, running tests and red-teaming, and adding a human-review loop. Teams also track failure modes and use an evaluation harness to monitor performance over time.

Will agents replace teams?

AI agents won’t replace teams. They take over repetitive tasks and speed up routine decisions, while people remain essential for exceptions, complex cases, and higher-value work.

About scandiweb

scandiweb is a full-service eCommerce agency helping brands build, scale, and optimize across platforms like Adobe Commerce, Shopify, BigCommerce, and commercetools. We design and implement AI agents that work inside real systems, supporting merchandising, support, analytics, and ops with measurable results.

The post Top 16 AI Agent Development Companies in 2025 appeared first on scandiweb.

Billions of questions are answered by AI engines every single day. ChatGPT, Perplexity, Claude, Gemini (large language models – LLMs) are quickly becoming the first place people turn for answers that they draw from articles, blogs, product pages, and forums to generate personal and authoritative responses. 

But that doesn’t mean your site is always linked, and your brand is always part of those answers. If it’s not, it’s invisible. And your content might be impacting those responses without you even knowing it.

Tracking how your brand shows up in AI-generated responses is a growing priority for digital marketers. With SEO, it was about ranking on page one, but now it’s become about being present (and the way of being present) in the answer itself.

Our guide will show you how to track your brand’s visibility inside today’s leading AI engines. Learn how different tools generate responses, how to test prompts your audience might use, and how to interpret what shows up and what doesn’t. With the right approach, tracking AI visibility will uncover new profitable opportunities.

Key takeaways

• LLM visibility means your brand appears in AI-generated answers, whether through citations, summaries, or paraphrased content, and often without attribution.
• Prompt testing helps you measure your presence by simulating real user queries across tools like ChatGPT, Perplexity, Claude, Gemini, and others.
• Structured, clearly written content increases your chances of inclusion, especially when using FAQs, comparisons, schema markup, and consistent naming.
• Tracking over time reveals patterns, helping you refine content and stay visible.

What is LLM visibility?

LLM visibility is how your brand, content, or products show up in the answers that AI tools generate. It can be citations or direct quotes, but also summaries, comparisons, recommendations, or explanations that shape what users see and believe.

There are different levels of visibility across these tools. Sometimes, your brand is cited directly with a source link, or your product or a snippet of your content is paraphrased without attribution. And in many cases, your competitors might be included while you’re left out entirely.

Tracking LLM visibility means looking at:

• Presence – are you mentioned at all?
• Position – are you the first result or buried in a list?
• Format – is the reference a direct quote, citation, paraphrase, or something else?
• Context – is your brand portrayed accurately, or are outdated or misleading descriptions being pulled in?

These signals, first and foremost, tell you if you’re part of the conversation. They also show how AI frames your brand in relation to the topic, the user’s intent, and your competitors.

Unlike traditional SEO, where page rankings are easy to benchmark, LLM visibility requires a more nuanced, hands-on approach. But it also opens the door to new insights about your content’s clarity and authority.

How AI models decide what to show

When you ask an AI tool a question, instead of pulling the answer from a single source, it builds one by scanning, combining, and rewriting information based on what it’s been trained on or what it can retrieve in real time. The sources behind those answers determine what gets included and how your brand is presented, if at all.

Here’s what influences that process:

1. Pre-trained data

Some models (like ChatGPT in default mode or Claude without search enabled) rely on datasets collected during their last training cycle. These are snapshots of the internet taken months or even years ago: a mix of public websites, articles, forums, product listings, and general web content. If your content wasn’t widely available or well-structured at the time, it likely won’t be referenced.

2. Real-time search results

With browsing enabled, tools like Perplexity, Gemini, and ChatGPT can pull in live results. Their responses often include citations and reflect what’s currently published on the web. Your content needs to be recent and structured to make it easy for the AI to extract and reuse.

3. Structured data and authority signals

Content that includes schema markup, labeled sections, clear titles, or FAQs is easier for LLMs to parse and reuse. Structured content increases the chance your information will be used in answers, even without attribution. Domain authority, link profiles, and how often others reference your content also factor in. That’s why the same directories and marketplaces keep getting mentioned.

4. Context reinforcement

If your brand is consistently mentioned across multiple sources (third-party sites, forums, comparison articles), especially about specific topics, AI tools are more likely to include it. Even if the mention isn’t on your own site, it contributes to your presence in AI-generated answers.

Building a prompt testing strategy

Before you can measure how often your brand appears in AI-generated answers, you need to think like your audience. They’re probably not searching for your brand name but asking questions and looking for recommendations. Your goal here is to simulate those queries and see how AI tools respond. Here’s how to approach it!

Start with real-world questions

Consider what your customers might type into an AI engine when trying to solve a problem you/your products help with. Avoid branded queries unless you’re testing for navigational intent. What matters most is how you appear in the generic, high-intent prompts your competitors are also trying to win. Prompt examples:

• Informational prompts, e.g., “What’s the best CRM for small B2B teams?”
• Comparative prompts, e.g., “HubSpot vs Salesforce for growing startups”
• Category-based prompts, e.g., “Top Shopify agencies for fashion brands”
• Niche use cases, e.g., “Eco-friendly packaging suppliers in Canada”

Create your prompt bank

Build a simple spreadsheet or doc where you store your full list of prompts, the intent behind each (informational, commercial, etc.), and the ideal outcome (brand mention, product positioning, etc.). Aim for 10–20 prompts to start, covering different funnel stages and customer segments. You can expand later once you see which patterns are worth tracking more closely.

Account for different phrasing

LLMs respond differently depending on how a question is asked, and small changes can shift which sources get pulled and how your brand is framed. Test variations like:

• “What are the best…”
• “Who are some recommended…”
• “Can you compare…”
• “What company offers…”

AI platforms to track visibility

Not all AI engines work the same way, so understanding how each one behaves helps you decide what to track and how to interpret the results.

ChatGPT (OpenAI)

• Free version (GPT-3.5) runs on older, static data, no browsing.
• Paid version (GPT-4) may have browsing turned on, depending on your settings.
• Citations are inconsistent – sometimes you’ll get links, other times only a summary.
• Useful for testing how well your brand is understood and paraphrased.

Perplexity

• Uses live search results by default.
• Citations are visible and clickable for nearly every answer.
• Great for identifying exactly which pages are influencing the AI’s output.
• Follow-up questions test brand recall and consistency over multiple steps.

Claude (Anthropic)

• Strong at giving well-organized answers, often with clear sourcing.
• Tends to cite sources for factual or comparison-based prompts.
• A good option if your site includes how-to content, guides, or research-driven material.

Gemini (Google)

• Pulls from Google Search, YouTube, and other live content.
• Prioritizes sources that already perform well in Google’s ecosystem.
• Structured content and schema are especially helpful here.

Microsoft Copilot (Bing)

• Built on OpenAI models, integrated with Bing.
• Uses live web content with hoverable citations.
• Especially strong in product-related queries, local business searches, and technical FAQs.

DeepSeek

• Rising open-source model with strengths in technical and multilingual prompts.
• Some interfaces show citations, but behavior can vary.
• Useful if your brand targets academic, global, or niche expert audiences.

Logging and analyzing LMM visibility

Once you’ve tested your prompts, it’s time to collect what shows up. You’ll need to be able to spot patterns, find weaknesses, and understand how your content performs across different models. A simple spreadsheet or Notion board works fine as long as it’s consistent.

Why this matters

LLMs don’t return the same result every time. Even with the same prompt, you might show up once and disappear the next. Logging multiple sessions across tools gives you a clearer signal about frequency and positioning over time.

What to track for each prompt

• Prompt tested
• AI tool used
• Date tested (LLMs change constantly, so timestamp everything)
• Brand mentioned? Yes/No. If yes, how is it phrased?
• Type of visibility – citation, paraphrase, brand name only, other
• Competitors mentioned
• Source links to your pages
• Notes – was your messaging accurate? Anything misrepresented or outdated?

Optional extras

• Prominence score/how high was your brand featured in the response?
• Sentiment and tone of the mention
• Consistency when testing the same prompt multiple times

Tools to track brand mentions in AI answers

Tracking visibility manually is a solid way to start. But if you manage many prompts while tracking competitors, etc., you’ll quickly outgrow spreadsheets. Here’s a look at some of the leading tracking tool options and what teams they suit best.

Remember! Even with the right tool, you’ll still need a strong testing process. But these platforms will save hours of manual work and help you track real change over time.

AnswerRank

Best for: SEO leads, content strategists, performance marketers
Team size: Medium to large agencies or in-house SEO teams

Purpose-built for LLM visibility. Lets you test real prompts and track how often your brand or competitors show up. Includes dashboards for prompt performance, position tracking, and trend overviews. Helpful in identifying gaps and making decisions across multiple content teams.

LLMrefs

Best for: Technical marketers and SEO analysts
Team size: Mid-market to enterprise

Covers a wide range of AI tools, including ChatGPT, Claude, Gemini, Perplexity, and Grok. Tracks your brand’s presence and also highlights model biases. Includes an LLMrefs Score – a proprietary metric that helps you benchmark visibility over time.

Scrunch AI

Best for: Marketing teams focused on brand consistency and messaging
Team size: Agencies and growing DTC brands

Positions itself as a brand presence monitor in AI. Tracks how your company is described across tools, with a focus on sentiment and accuracy. Suitable for brands with strict messaging guidelines or ongoing PR/communications monitoring.

Peec AI

Best for: SMBs and small marketing teams
Team size: Startups, boutique agencies, consultants

Simple and user-friendly, with strong coverage across major AI engines. Tracks mentions, sentiment, scoring, and citation patterns. A good entry-level tool for teams that want better visibility without dealing with steep learning curves.

Profound AI

Best for: Enterprise SEO departments and data-heavy organizations
Team size: Enterprise

Offers deep customization, high-volume prompt testing, and integration into existing analytics workflows. Ideal for teams with internal analysts or BI resources who need granular tracking across large sets of prompts.

Semrush (AI Visibility Suite)

Best for: Existing Semrush users expanding into AEO
Team size: All

Semrush now includes AI visibility tracking as part of its broader SEO toolkit. It’s useful if you already rely on Semrush for keyword tracking, competitor benchmarking, or content audits and want to add AI response tracking to your workflow.

Patterns to look for in LMM tracking results

Once you’ve collected enough data from your prompt testing, the goal is to understand why your brand does or doesn’t show up, and what to do next. Watch for the following patterns!

Consistent competitors

If the same 2–3 brands appear across multiple prompts, tools, and sessions, that’s a signal. They’ve likely published the kind of content LLMs prefer or been cited enough across the web to earn a default spot. Study what they’re doing: where they’re mentioned, what content formats they use, how they structure information, and which domains they appear on. You’ll want to do something similar.

Content types that get reused

Are the AI tools pulling from blog posts, product pages, comparison charts, directories, or community forums most? If your site isn’t being cited but others are, check how those formats differ from yours. In many cases, structured content like FAQs, “best of” lists, and definition-based pages is easier for LLMs to parse and reuse.

Platform-specific behavior

ChatGPT might paraphrase your blog post without citing it. Perplexity might show a competitor’s site five times in a row. Claude might prefer cleaner documentation or research-heavy articles. Each tool has its own tendencies, and tracking across platforms helps you understand what kind of content performs where.

Incorrect or outdated information

Sometimes your brand does appear, but AI uses old messaging, legacy/sold-out product names, or inaccurate positioning, often lifted from third-party sites or cached pages that are still referenced. Ensure visibility and quality control overlap.

Paraphrase without credit

You might find that your messaging or product descriptions are being summarized or reworded without a link or mention. The good news is that the content is working, but not in a way that builds brand equity. This is common with unstructured blog content or generalist explainer pages.

To spot these patterns clearly, it helps to:

• Re-run the same prompt 3–5 times across tools
• Log every unique mention or phrasing
• Track what source types show up most often
• Compare your content format and wording with the pages that do get cited.

Soon enough, you’ll move from reactive “We showed up once!” to strategic “We’re being positioned this way across three tools, but competitor X has a stronger presence because they’re ranking in source Y.”

Read some of our client success stories after answer engine optimization (AEO):

• AEO Makes Novatours #1 Travel Agency in AI Search
• How AEO Helped Enviropack Become a Top AI Pick for Sustainable Packaging
• AI Search Optimization Helps Kouboo Break Into the Top 3 AI Answers

How to improve your LLM visibility

Once you know how AI tools are using your content, you can start adjusting your strategy. Here’s how to improve your chances of being included in the answers that matter:

1. Strengthen clarity because AI tools often skip over vague descriptions. Instead of writing “the best hiking gear,” say “TrailFlex waterproof hiking boots for wet mountain trails.” Name products, categories, use cases, and locations clearly, so that AI doesn’t have to guess.
2. Target known citation sources, as LLMs frequently reference content from trusted aggregators, directories, media sites, and high-authority blogs. If your competitors are getting cited, find a way into those pages or create something similar that earns backlinks and visibility.
3. Use structured formats – content in clean, labeled sections (like FAQs, comparison tables) is more likely to be used in an answer; the same goes for H2s/H3s, bullets, and schema markup to make your content easier to interpret.
4. Monitor and refresh outdated content if AI tools are pulling from legacy pages or misrepresenting your current offering. Set up regular reviews of key URLs, especially those tied to high-intent queries or competitive categories.
5. Cover specific queries, not just keywords, and don’t skip the real-world questions people ask (“best email marketing software” → “what’s better for small teams: Mailchimp or Klaviyo?”) The more directly your content answers those kinds of prompts, the more likely it is to show up in AI-generated responses.

Conclusion

Expect LLM visibility to shift as sources get refreshed and prompts change. One week, your brand might appear in 7 out of 10 tests, while the next, you’re out entirely. That’s normal. Tracking regularly matters more than chasing quick wins.

If you’re running SEO or content strategy, you need to treat AI-generated answers the same way you treat SERPs: as a space where your brand needs to compete, using clarity, presence, and consistency.

Start by tracking how your brand shows up today by running prompts that reflect real customer questions. Log the data and spot the gaps, then make your content easier to find and use and harder to ignore.

AEO and AI visibility work together, so getting ahead of it now puts you in a stronger position as AI continues to shape how customers make decisions.

Need help finding out how visible your brand really is? Our team offers a free AEO audit that highlights where you are and where you can improve. We also build AI solutions for eCommerce brands, anything you can imagine and needs fixing – let’s work on something great together! 

The post Tracking Brand Mentions in LLMs: ChatGPT, Perplexity, and Claude AI-Generated Answers appeared first on scandiweb.

Every so often, the same question makes the rounds in the eCommerce universe: “Is Magento dying?”

We’ve asked it ourselves. Some of our clients have too, especially when evaluating replatforming. The name ‘Magento’ doesn’t show up in top eCommerce trend reports as often as it once did, and SaaS platforms like Shopify are gaining ground fast. Feels like everyone’s moving on.

But the reality in 2025 is more nuanced. 

Yes, Magento’s active store count is shrinking. Yes, merchants are leaving, some for lower costs, others for faster iteration or fewer technical dependencies. But Magento also continues to serve those with complex product logic and integrations. And even now, it’s gaining users from legacy platforms and holding its own.

Let’s unpack where this concerning narrative comes from, what the numbers show about platform switching, which businesses still choose Magento, where the platform stands now, and what’s likely to come next.

For those looking to learn more general information about what Magento is, its features, pros and cons, and pricing, we’ve put together the complete guide to the Magento (Adobe Commerce) eCommerce platform.

Where the “Magento is dying” narrative comes from

The idea that Magento is on its way out isn’t coming out of nowhere. It’s a mix of ecosystem shifts and some stats that, when taken together, can paint a misleading picture.

1. Magento → Adobe rebranding

Adobe acquired Magento in 2018. What used to be one platform and a familiar name, Magento, now refers to two separate products: Magento Open Source (free) and Adobe Commerce (enterprise edition). Adobe’s branding leans heavily toward the paid version, and many product updates and official documentation focus there, which creates uncertainty for merchants still using or considering the free version.

Also read:
Adobe Commerce vs Magento Open Source: Detailed Features Comparison

2. High-profile replatformings

When well-known brands move from Magento to Shopify, Salesforce, commercetools, or another platform, those stories get shared widely. Meanwhile, success cases going the other way rarely make headlines, making it seem that everyone is leaving Magento, even if many businesses still use it.

3. Net losses to SaaS

Recent data backs up the perception, as Magento’s store count is trending down. BuiltWith shows Magento powering just over 100,000 live sites in August 2025, down from previous years. Shopify alone gained 520 stores from Magento in the past 90 days, while Magento lost more stores than it gained.

4. Operational costs

Magento requires more technical expertise to run and maintain. Unlike Shopify or BigCommerce, Adobe Commerce still requires merchants to manage hosting, security patches, PCI compliance, and other key tech tasks. While some enjoy the flexibility, for others, it’s too demanding and a reason to leave.

5. Magento 1 (and Magento 2) EOL?

Magento 1 reached end-of-life in 2020, and many merchants remember the disruption it caused. Magento 2 is on a different trajectory, with Adobe continuing to release new versions, invest in the platform, and provide a rolling support lifecycle that extends with each release.

The latest version, 2.4.8, is supported through April 2028. But that’s not the end of Magento 2. Future versions are expected to follow, with new support timelines extending further. For teams planning long-term, Adobe Commerce, Magento Open Source, and the global developer community are all contributing to its stability and longevity.

Magento vs. Shopify

Shopify has become the most common destination for merchants leaving Magento. In the last 90 days alone, 1,765 stores left Magento, and 520 of them moved to Shopify. (source: BuiltWith)

As of Q2 2025, there are over 2.66 million live Shopify stores, and that number continues to grow (+3.0% QoQ, +9% YoY), according to Store Leads. Magento, in contrast, has 126,000 live stores, down 2.3% QoQ and 11% YoY. It still has a strong open-source legacy and foothold in complex setups, but its active store count is decreasing.

If we look across industries, Shopify dominates D2C categories:

• Apparel (28.6%)
• Home & Garden (12.1%)
• Beauty & Fitness (11.0%)

Magento remains more common in sectors with complex catalogs or B2B needs:

• Home & Garden (14.9%)
• Apparel (10.7%)
• Business & Industrial (8.9%)

This tracks with what each platform prioritizes: Shopify simplifies go-to-market, while Magento gives dev teams more control over catalog structure, pricing logic, and integration flexibility.

Why merchants switch from Magento to Shopify

• Total cost of ownership and speed to market – Shopify’s predictable monthly pricing and fully managed infrastructure make it especially appealing for brands without large technical teams
  • Shopify Plus starts at $2,300/month
  • Adobe Commerce licensing can run from $40K–$200K+ per year, depending on hosting, etc.
• Built-in checkout – fully hosted and optimized by default, and while Magento merchants using custom or optimized checkout flows can match or even exceed Shopify’s performance, it requires hands-on development and optimization
• App integrations – Shopify has over 16,000 apps, compared to roughly 5,700 for Magento, with shorter time-to-value and integrations that are easier to implement
• Lower operational overhead – unlike Magento, Shopify handles hosting, security patching, and PCI compliance at the platform level, often cited as a key reason for switching
• B2B support catching up – recently improved features like company profiles, custom catalogs, payment terms, and order management have made Shopify a more viable replacement for Adobe Commerce in mid-market B2B use cases.

When Shopify fits best

Shopify is often the right choice for brands that prioritize speed and a fully managed service model with limited in-house development resources. Its streamlined backend and built-in checkout make it easy to iterate quickly for DTC-first businesses focused on maximizing conversion. It also works well for merchants looking to consolidate retail and wholesale operations within a single admin environment, especially with its expanding native B2B features.

When Magento is still the better choice

Magento continues to stand out in setups that demand complex product data, advanced pricing rules, or custom business logic, like multi-store setups with regional differences in catalog and currency. It is also chosen by brands that need full control over infrastructure and ERP, PIM, or other system integrations, and remains the go-to platform for projects that depend on custom middleware.

Magento vs. other eCommerce platforms

Shopify may be the most visible alternative to Magento, but it’s not the only one. According to BuiltWith, platforms like commercetools, WooCommerce, Salesforce Commerce Cloud, BigCommerce, and VTEX continue to pull away Adobe Commerce clients. Magento has become one of several options in the market, increasingly defined by composable architecture, open SaaS, and integrated CRM/marketing stacks.

Here’s how Magento compares to the key enterprise players in 2025.

Magento vs. Salesforce Commerce Cloud

Based on BuiltWith data, Adobe Commerce lost 320 customers to Salesforce Commerce Cloud as of August 2025, more when factoring in its Demandware alias. 

Salesforce Commerce Cloud continues to be a go-to platform for global brands already invested in the broader Salesforce ecosystem, which is known for CRM integration, personalization through Salesforce Marketing Cloud, and unified customer data across platforms. It continues to attract Magento merchants looking to trade backend flexibility for marketing automation, but at a high cost and with less backend control compared to Magento. 

Magento vs. WooCommerce

Adobe Commerce has lost over 3x as many customers as it gained from WooCommerce Checkout, according to BuiltWith.

WooCommerce appeals to small and mid-sized merchants, especially those already using WordPress, as it offers a familiar CMS environment and lower maintenance. It’s highly extendable via plugins and can be customized by non-technical teams with minimal effort. For businesses that don’t require advanced catalog rules or heavy backend integrations, WooCommerce often feels less resource-intensive than Magento.

Magento vs. BigCommerce

BigCommerce positions itself as an open SaaS alternative – less restrictive than Shopify, but more manageable than Magento. It appeals to merchants who want API access and multi-store support, without the infrastructure demands of open-source platforms.

Magento still offers more backend control, deeper customization, and a mature ecosystem for B2B features and multi-store complexity. However, BigCommerce has gained adoption among merchants migrating off Magento to reduce operational overhead and licensing costs, especially in the mid-market. 

While precise net switching data isn’t available at the enterprise level, BigCommerce frequently appears in Magento replacement RFPs for businesses that want flexibility without full stack ownership. In these cases, Magento loses customers not because of missing features but because teams no longer want to maintain those features themselves.

Magento vs. commercetools

According to BuiltWith data (cumulative through August 2025), Adobe Commerce has lost 11 customers to commercetools while gaining just one, resulting in a net loss of 10. 

Known for its headless-first, API-native model, commercetools is often chosen by large teams designing multi-brand ecosystems where catalog, checkout, CMS, and PIM are independently managed. While Magento allows custom-built frontends and backend logic, it’s not composable by default. For enterprise teams building exactly what they need using a suite of independently deployable services, commercetools can be the cleaner starting point.

Also read:
commercetools vs Adobe Commerce: Which eCommerce Platform is Best for You?

Other enterprise platforms gaining Magento clients

SAP Commerce Cloud, often selected by companies already running SAP ERP systems, offers tight integration with backend finance, logistics, and procurement workflows. According to BuiltWith, Adobe Commerce has a net loss of 48 customers to SAP Commerce Cloud, typically those for whom ERP alignment and overall system consolidation outweighs frontend flexibility.

Similarly, BuiltWith data shows Adobe Commerce with a net loss of 36 customers to VTEX, which positions itself as a composable, API-first platform with strong marketplace functionality and presence in Latin America and parts of Europe. For brands pursuing unified commerce, VTEX’s bundled marketplace and headless tools can be compelling.

Magento’s position in this entire group is defined by its extensibility and ability to handle deeply customized workflows. It’s best suited for teams that want to build tailored experiences across frontend and backend, and are prepared to manage infrastructure, security, and upgrades. Adobe Commerce also benefits from integration with Adobe Experience Cloud products, which can be a differentiator for content-heavy or design-led brands.

Magento platform status in 2025

As of August 2025, BuiltWith tracks roughly 105,000–106,000 live Magento sites, with over 639,000 total known historically. Magento 2 makes up the majority of live usage, with about 72,700 stores, and Magento 2.4 accounts for ~40,000 of those. The older Magento 1 footprint, while still recorded, is no longer active in any supported capacity.

Magento’s share in the ‘Top 1 Million websites’ currently sits at 1.05%, making it the 4th most popular open-source eCommerce platform in that tier, still ahead of many niche stacks but down from previous years. In the U.S. alone, 23,538 sites run on Magento 2.

Active releases and long-term support

Magento is still under active development: 

• Magento Open Source 2.4.8 was released in April 2025, with support for PHP 8.4 and MariaDB 11.4
• Security-only patches for 2.4.7 and 2.4.8 continue to ship on Adobe’s monthly Patch Tuesday cycle

Adobe Commerce follows the same core versioning and receives additional enterprise modules and service integrations. Both benefit from shared security infrastructure, but support models and SLAs differ depending on license type.

Growing Magento ecosystem and community

BuiltWith trend data shows that Adobe Commerce losing net customers is a gradual but consistent movement, reflecting a broader shift toward composable and SaaS models, especially at the enterprise tier.

That said, Magento is evolving in a more focused lane:

• Hyvä continues to gain traction as a preferred frontend stack for Magento 2 merchants, improving performance and developer experience
• Adobe is actively investing in Edge Delivery Services, a composable storefront architecture powered by Adobe Experience Manager and built for faster content delivery
• Magento continues to offer deep integration flexibility, allowing complex ERP, PIM, OMS, and custom middleware setups that many SaaS platforms cannot match.

The ecosystem around Magento has stayed active and, in some areas, it’s grown stronger. Regular meetups, code contributions, and independent vendor innovation have helped keep Magento’s community tightly connected, even as its global footprint shrinks. For merchants who prioritize flexibility and want to stay connected to a hands-on, developer-driven community, Magento continues to offer a home.

How is Magento changing (and why that’s good)

Magento in 2025 is deliberately improving around real merchant and developer needs: frontend performance, AI-driven tools, community control over the open-source future, and continued backend flexibility for complex builds.

Frontend modernization

One of Magento’s most visible shifts is how merchants are rebuilding their storefronts. Traditional Luma-based themes are being phased out in favor of faster alternatives. The most popular of these is Hyvä, now widely adopted by Magento 2 stores across the mid-market and enterprise segments. Hyvä significantly improves performance scores and reduces complexity for frontend teams without requiring a full composable setup.

At the enterprise level, Adobe’s Edge Delivery Services introduces a new headless storefront option, built on Adobe Experience Manager and designed for teams invested in Adobe’s content workflows. Though still early in adoption, it signals Adobe’s continued investment in Magento’s frontend modernization.

AI-powered features

AI is already embedded in Adobe Commerce. Live Search and Product Recommendations, powered by Adobe Sensei, use real-time shopper behavior to personalize search results and product carousels. These services run on Adobe’s cloud and are available to merchants using the Commerce edition.

While Magento Open Source doesn’t include native AI tools, some merchants integrate third-party AI services or custom models into search, product upsell, customer service, and other features. The platform’s open nature allows for deep AI experimentation, though setup and scaling require technical involvement.

Who Magento still serves best

Magento solves specific, high-value problems for businesses with complexity at their core. While many merchants have shifted to SaaS platforms to simplify operations, Magento continues to serve those with use cases that demand flexibility and architectural control.

It remains a strong choice for businesses with large or multi-layered catalogs, custom product logic, or non-standard pricing models that require more than plug-and-play functionality. Brands with multi-store, multi-region setups where catalog structure, language, tax, and currency rules vary by market often stick with Magento because of its native capabilities in these areas.

Magento also holds its ground in B2B commerce, where workflows like company accounts, quoting, tiered pricing, and custom catalogs are needed. While platforms like Shopify and BigCommerce have introduced B2B features, Magento’s model still offers deeper configuration options and more control when building custom user experiences.

For teams with strong development support or agency partners who can manage deployments, integrations, and patches (scandiweb ), Magento offers an advanced stack that doesn’t restrict how the business grows over time. It’s not the fastest or cheapest to launch, but it remains one of the most adaptable, and we believe that still counts for something.

Why some merchants are leaving

Most merchants leaving Magento are likely leaving because it no longer fits the way they want to operate.

For many, the tipping point comes down to internal capacity. Maintaining Magento means handling patches, upgrades, QA, extension compatibility, and performance tuning, which is feasible with a dedicated team or a retained agency. But with tight budgets, merchants often reassess whether they want to own that layer of complexity at all. Besides, skilled and certified Magento developers are hard to find.

For others, long release cycles and heavy customization requirements cause friction. While Magento offers complete control, even small changes can require developer input, slowing down marketing teams and time-to-iteration compared to platforms with drag-and-drop workflows and native app ecosystems. 

How to decide if Magento is for you

Magento is still a smart investment:

• If you’re dealing with non-standard product types, complex checkout flows, regional logic, or deep ERP/PIM integrations
• If you want to future-proof your architecture with a customizable backend stack.

But if your team values speed and simplicity, or if you’re trying to reduce reliance on dev resources, Magento might feel heavy. In the end, it’s about whether Magento still matches what your business needs to run and grow today and in the years ahead.

Magento is NOT dying

Magento Open Source and Adobe Commerce are both still very much alive in 2025. Releases are shipping on schedule, security patches are routine, modern frontends are gaining traction, and the platform still powers thousands of stores that have the ability to customize everything from checkout to product logic to infrastructure.

Magento might be losing some customers to SaaS, but for the businesses that need it and for the developers who continue to build on it, Magento isn’t going anywhere.

At scandiweb, we’ve loved Magento since 2009, and since then, we have gathered the largest certified Magento developer team in the world. Contact us today for a free consultation, and we’ll help evaluate if Magento still fits your needs or build you a better alternative.

The post Is Magento Dying? Here’s What the Data Says in 2025 appeared first on scandiweb.

You can find plenty of articles about exporting tracking extract data from Salesforce Marketing Cloud (SFMC) online. I relied on a few of them in the beginning. 

However, the default export option to S3 features the export of one .zip file. That’s not very usable if you need to use those files in further pipelines. You would need to unzip the data. That was a bit puzzling for me at that moment. 

Eventually, I came up with a solution where, upon exporting a .zip file to AWS S3, a Lambda function triggers, unzips the file, and then sorts each file into the right folder by its name. It also adds a date suffix to each file, so you always know when it was exported.

If you’re running into the same issue, I’ve written down all the steps I took – and included the full Lambda code – so hopefully it will save you some time.

After going through additional pipelines, this data was fed into streams in the Salesforce Data Cloud CDP, thus allowing for a better understanding of customer email interactions. 

Also read:

• The Complete Guide to Data Ingestion in Salesforce Data Cloud
• A Practical Guide to Identity Resolution in Salesforce Data Cloud

What are tracking extracts in SFMC?

Tracking extracts are raw, event-level exports from Salesforce Marketing Cloud. Each one is a flat .csv that shows your email opens, link clicks, bounces, unsubscribes, and so on.

You can schedule these exports to land in S3 (or another external store), then pipe them into analytics platforms, data warehouses, or Salesforce Data Cloud.

Here’s a view of how these tracking extract tables connect: 

Why export tracking extracts?

Three reasons:

1. You want more control over the data. SFMC’s reporting tools are limited, while external exports let you clean, filter, and join data your way.
2. You need more extract types. The built-in Starter Bundles only cover a handful of feeds, but tracking extracts open access to everything available, so you can pick and choose what to load into Salesforce Data Cloud.
3. You want to backfill history. Bundles can’t reach past recent activity. Tracking extracts let you go back 30 days per batch.

How the automated export works

Here’s the flow you’ll build:

1. SFMC automation schedules a tracking extract and outputs a .zip file to a designated S3 “input” bucket
2. A Lambda function is triggered when a new .zip arrives
3. The Lambda:

• Unzips the archive
• Renames each CSV with a date suffix
• Sorts files into S3 destination folders by filename

In the end, you have clean, clearly labeled .csv files in your S3 bucket ready for use in pipelines, dashboards, or customer data platform (CDP) ingestion.

Could you customly use SFMC’s SOAP/REST APIs instead? Sure. But unless you need dynamic logic or tight integration, Automation Studio provides a simpler, low-code way to handle this.

Sounds good? Let’s proceed!

Step 1: Set up your export in Salesforce Marketing Cloud

Use Automation Studio to run the export

Automation Studio is SFMC’s low-code workflow engine. It allows you to string together assets like data extracts, imports, SQL queries, scripts, and file transfers and schedule them to run automatically.

For this setup, you’ll need to configure an automation:

• Data Extract to generate the .zip file
• File Transfer to send that file from SFMC’s internal storage (Safehouse) to S3

You’ll also need to set up a connection between SFMC and your AWS S3 bucket before the export can happen.

A note on Safehouse: It’s SFMC’s secure internal staging directory, encrypted at rest and separate from your FTP, where your export files are held before transfer.

Create AWS credentials for SFMC

1. In your AWS console, go to IAM → Users → Create user (set a user name, e.g., sfmc-tracking-extract-connector or anything descriptive)

2. Under Permissions, choose Attach policies directly → then Create policy

Use the JSON editor to define what this user can do. Grant read/write access to the specific S3 bucket and folder where your exports will land.

Here’s the code to use (change your bucket/folder data):

{
  // The policy language version
  "Version": "2012-10-17",

  // A list of individual permission statements
  "Statement": [
    {
      // A unique identifier for this statement; you can name it whatever you like
      "Sid": "AllowMCS3AccessToBucket",

      // Specifies whether to allow or deny the actions in this statement
      "Effect": "Allow",

      // Which S3 actions are permitted
      "Action": [
        "s3:ListBucket"    
      ],
      // Which resources these actions apply to
      "Resource": [
        "arn:aws:s3:::your-bucket-name"    // Your S3 bucket’s ARN
      ]
    },

    {
      // Another identifier for grouping object-level permissions
      "Sid": "AllowMCS3AccessToBucketContent",
      "Effect": "Allow",

      "Action": [
        "s3:PutObject",        
        "s3:GetObjectVersion",  
        "s3:GetObject",          
        "s3:DeleteObject"     
      ],
      "Resource": [
        "arn:aws:s3:::your-bucket-name/zip-files/*",   // All files inside zip-files/ (adjust to your folder)
        "arn:aws:s3:::your-bucket-name/zip-files"      // The folder path itself
      ]
    }

  ]
}

Tip! You can find the bucket ARN if you go to the Bucket → Properties.

3. After creating the policy, review the settings, attach tags if needed, and Create user

4. Generate access keys by clicking Create access key, choose Third party, and acknowledge the warning

Important! Save the Access Key ID and Secret Access Key now – this is the only time you’ll see the secret; store them securely.

Add your S3 bucket to SFMC

1. In Salesforce Marketing Cloud, go to Setup → Administration → Data Management → File Locations. Click Create and choose Amazon S3
2. Fill in the form with the credentials you just generated:

• Access Key ID
• Secret Access Key
• Bucket Name
• (Optional) Prefix or folder path
• AWS Region

Things to double-check:

• Your access key, secret key, or role ARN for typos – invalid credentials will block all exports
• The region must match exactly; otherwise, SFMC won’t find your bucket
• Only enable Transfer Acceleration if you’ve enabled it in your bucket settings
• Make sure your IAM policy includes the required S3 permissions for listing, reading, and writing files.

Step 2: Set up the automation

1. In Automation Studio, click New Automation, name it something like SFMC Tracking → S3, and add these two activities in order:

• Data Extract
• File Transfer

2. Configure Data Extract

• Name: sfmc_te_%%Month%%%%Day%%%%Year%%.zip (Date tokens auto-insert the run date into the filename)
• Type: Tracking Extract
• Range: Rolling 1-day window (to grab the previous day’s data)
• Format: CSV
• Encoding: Choose what fits your system (UTF-8 is standard)
• Options: Select the tracking types you want: opens, clicks, bounces, etc.
• When you’ve double-checked everything, click Finish

3. Configure File Transfer

• Action: Move file from Safehouse
• File Pattern: Use the same name as above (e.g., sfmc_te_%%Month%%%%Day%%%%Year%%.zip)
• Destination: Choose the S3 file location you set up
• Click Finish to save

4. Once both steps are configured, add a Schedule to run the automation daily (or your preferred interval) and click Save. Flip the toggle to activate the schedule if it is paused and inactive; optionally, click Run Once to test and ensure your configurations are valid.

Step 3: Set up the AWS Lambda function to unzip and organize your export

AWS Lambda is a serverless compute service that runs your code in response to events, like a new file landing in S3. 

You could also use AWS Glue (with its visual ETL interface or custom scripts) for batch data jobs, but Lambda excels at real-time, event-driven tasks.

Create a Lambda function

1. In your AWS Console, go to Lambda → Functions → Create Function
2. Select Author from Scratch
3. Set a name (e.g., unzip-sfmc-tracking-exports)
4. Runtime: Python 3.12
5. Create or choose an execution role with permissions to access S3

You can attach these custom policies and then proceed to create a function:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:ListBucket",
                "s3:GetObject",
                "s3:GetObjectVersion",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::bucket", //adjust the bucket name
                "arn:aws:s3:::bucket/*" //adjust the bucket name
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": "*"
        }
    ]
}

Set up the trigger

This part is what makes the Lambda function run automatically.

1. In your Lambda, scroll to the Triggers section → Click Add trigger

2. Choose S3 as the source
3. Specify the input bucket
4. Select the file to react to all the object creation actions
5. Suffix filter: .zip, so that the function reacts only to ZIP files in the location
6. Click Save

Add the Python code

1. Go to the Code tab in your function, where you’ll see a built-in code editor – paste in your unzipping logic here

Here’s what the code does:

• Detects the new zip file
• Unzips the contents into memory
• Renames each file with a run-date suffix
• Sorts the files into folders based on the filename

2. Add the following Python code. The structure of your extracted files depends on which tracking extract types you selected (e.g. _click.csv, _open.csv, etc.). If you use different naming conventions, adjust the logic as needed.

import boto3
import zipfile
import os
import tempfile
import re

s3_client = boto3.client('s3')

# Complete mapping of file names to folders
folder_mapping = {
    "bounces": "tracking-extracts/bounces",
    "click-impression": "tracking-extracts/click-impression",
    "clicks": "tracking-extracts/clicks",
    "complaints": "tracking-extracts/complaints",
    "conversions": "tracking-extracts/conversions",
    "list-membership": "tracking-extracts/list-membership",
    "lists": "tracking-extracts/lists",
    "not-sent": "tracking-extracts/not-sent",
    "opens": "tracking-extracts/opens",
    "send-impression": "tracking-extracts/send-impression",
    "send-job-impression": "tracking-extracts/send-job-impression",
    "send-jobs": "tracking-extracts/send-jobs",
    "sent": "tracking-extracts/sent",
    "status-change": "tracking-extracts/status-change",
    "subscribers": "tracking-extracts/subscribers",
    "surveys": "tracking-extracts/surveys",
    "unsubs": "tracking-extracts/unsubs",
    "multiple-data-extension-send-lists": "tracking-extracts/multiple-data-extension-send-lists"
}

def extract_date_suffix(filename):
    # Pattern for _8digits_8digits or _8digits at the end before .zip
    pattern_range = re.compile(r'_(\d{8})_(\d{8})\.zip$')
    pattern_single = re.compile(r'_(\d{8})\.zip$')
    pattern_any = re.compile(r'_(\d+)\.zip$')
    
    # Check for range pattern first
    match = pattern_range.search(filename)
    if match:
        return f"{match.group(1)}_{match.group(2)}"
    
    # Check for single date pattern
    match = pattern_single.search(filename)
    if match:
        return match.group(1)
    
    # Check for any number of digits pattern
    match = pattern_any.search(filename)
    if match:
        return match.group(1)
    
    return None

def transform_filename(file_name):
    # Add hyphen between lowercase and uppercase letters and convert to lowercase
    transformed = re.sub(r'(?<=[a-z])(?=[A-Z])', '-', os.path.splitext(file_name)[0]).lower()
    return transformed

def lambda_handler(event, context):
    bucket_name = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']
    date_suffix = extract_date_suffix(key)
    
    if not date_suffix:
        print(f"Invalid filename format: {key}")
        return {
            'statusCode': 400,
            'body': 'Invalid filename format'
        }
    
    with tempfile.TemporaryDirectory() as tmpdir:
        download_path = os.path.join(tmpdir, os.path.basename(key))
        
        s3_client.download_file(bucket_name, key, download_path)
        
        with zipfile.ZipFile(download_path, 'r') as zip_ref:
            zip_ref.extractall(tmpdir)

        for file in os.listdir(tmpdir):
            if file != os.path.basename(key):  # Don't upload the original zip file
                file_path = os.path.join(tmpdir, file)
                
                # Transform the filename and check against the folder mapping
                base_name = os.path.splitext(file)[0]
                transformed_file_name = transform_filename(base_name)
                folder = folder_mapping.get(transformed_file_name)
                if not folder:
                    print(f"Skipping file: {file} (no matching folder)")
                    continue
                
                # Rename the file to include the date(s)
                new_file_name = f"{base_name}_{date_suffix}{os.path.splitext(file)[1]}"
                
                # Upload the renamed file to the correct folder
                s3_client.upload_file(file_path, bucket_name, f"{folder}/{new_file_name}")

    return {
        'statusCode': 200,
        'body': 'Files unzipped and uploaded successfully'
    }

3. When you’re done, click Deploy to save and activate the function
4. Check permissions if the function fails to access the bucket (missing GetObject, PutObject, or ListBucket)

Great! Next time your SFMC automation runs:

• A .zip file lands in the input folder in S3
• Lambda triggers automatically
• Files are extracted, renamed, and sorted
• Output files show up in clean, ready-to-use folders

Once the export and unzip process is running smoothly, there’s plenty more you can automate:

• Stream cleaned files into your data warehouse
• Add a second pipeline to load files into your CDP or other destination
• Set up notifications via SNS or Slack when files are ready.

This setup works well if you’re building a modular analytics pipeline, one where each part of the process (export, unzip, validate, ingest) is traceable and easy to debug.

If you’re facing issues with Salesforce Marketing Cloud exports, want to simplify your analytics setup, integrate a CDP, or need a hand building out real-time pipelines like this, scandiweb can help. Reach out to us today, and we’ll get back to you in 48h!

The post How to Automate SFMC Tracking Extracts with Unzipping in AWS [Code Included] appeared first on scandiweb.

The way people search for information online has changed. With tools like ChatGPT and Google AI Overviews now providing direct answers to questions, it’s no longer enough for your content to simply rank high. Your content needs to be part of these AI-generated answers to be truly visible. Answer engine optimization (AEO) helps with that. AEO ensures your content is optimized not just for search engines but also for AI tools, reshaping how users get answers.

Choosing the right agency to guide your AEO strategy is key to staying relevant in this AI era. In this article, we’ll highlight the top AEO agencies around the world – those that can help optimize your content for AI visibility and keep your brand front and center.

What is an AEO agency?

An AEO agency helps businesses optimize their content for AI-driven tools like ChatGPT and Google AI Overviews by focusing on clarity, structure, and schema markup. Unlike traditional SEO, AEO ensures content is easily understood and reused by AI, increasing the chances of being featured in AI-generated answers or summaries. 

AEO agencies support eCommerce, B2B, and any content-rich brands. They identify key entities, improve on-page formatting, and build off-site authority, making sure your content stays visible and relevant as AI search continues to grow.

What to look for in a top AEO agency

• Understands how AI models behave and uses prompt testing to see how content is interpreted and summarized by tools like ChatGPT, Gemini, Perplexity, etc.
• Focuses on entities (people, products, concepts) instead of just keywords to make content more recognizable and usable by AI systems.
• Applies advanced schema markup that accurately reflects on-page content and enhances AI understanding of context and relationships.
• Has proven success getting content featured in AI-generated results across platforms.
• Offers specialized AEO audits, testing tools, and real case studies that show clear impact on AI visibility.

Also read: successful AEO use cases

• AEO Makes Novatours #1 Travel Agency in AI Search Across the Baltics
• AI Search Optimization Helps Kouboo Break Into the Top 3 AI Answers
• How AEO Helped Enviropack Become a Top AI Pick for Sustainable Packaging

Best AEO agencies for post-SERP success

1. scandiweb

A leader in AI search optimization, scandiweb stands out for its deep integration of SEO and AEO, especially for eCommerce and B2B businesses. Their team focuses on making content AI-friendly, ensuring it gets picked up by tools like ChatGPT, Google AI, and Perplexity. By optimizing for structure, clarity, and relevance, scandiweb helps brands appear in AI-generated results and drive meaningful traffic. Their AEO strategies are designed for visibility and increasing on-site conversions and revenue.

2. BetterAnswer

BetterAnswer focuses on positioning brands as trusted sources within AI-powered search environments. Their SEO and AEO experts work to improve visibility across primary AI tools by structuring content for maximum clarity and authority. With a strong emphasis on measurable results, they aim to turn AI exposure into real business growth.

3. Arc Intermedia

Arc Intermedia specializes in creating AI-friendly and user-focused content and offers tailored strategies to improve AI visibility across platforms.

4. OMR Digital

OMR Digital offers AEO services to ensure content performs well across AI tools and search engines. They focus on increasing content visibility within AI-driven answers to help brands gain recognition and authority in AI-generated answers.

5. Gorilla Web Tactics

Gorilla Web Tactics specializes in AEO for legal and healthcare clients, helping them adapt content for AI platforms. With 15+ years of experience, the agency aligns niche business content with how AI tools process and deliver information to improve visibility in AI-generated responses.

6. Perrill 

Perrill offers generative engine optimization (GEO) services to help businesses optimize their content for generative AI tools. Their strategy ensures content is structured and clear, allowing it to be easily extracted and used by AI models like ChatGPT and others.

7. ThatWare

ThatWare leverages advanced AI and automation to optimize content for AI-driven search platforms. Their approach combines semantic engineering, NLP, and proprietary algorithms to enhance content visibility and relevance in AI-generated answers.

8. Dreikon

Dreikon focuses on optimizing content for ChatGPT, ensuring that it’s structured to be fully compatible with AI assistants. They offer tailored strategies that help businesses stay competitive in AI-driven search.

How to choose the right AEO agency

Internal readiness: audit, CMS flexibility, content team

Before choosing an AEO agency, assess your internal capabilities. Ensure your website is ready for optimization with a flexible CMS that can handle schema updates and content restructuring. Also, evaluate your content team’s ability to collaborate with the agency on refining and producing AI-friendly content.

Tailored approach for different industries

When choosing an AEO agency, consider the specific needs of your industry. eCommerce brands often require a strong focus on optimizing product content for AI-driven shopping assistants and recommendations, while service-based industries or B2B businesses may need to prioritize optimizing technical content and thought leadership for AI-generated responses in industry-specific queries.

Short-term testing vs. long-term structured strategy

Some agencies may focus on short-term testing and quick wins, while others offer comprehensive, long-term AEO strategies. If you’re looking for sustainable results, choose an agency that can provide a structured, ongoing approach to AEO, ensuring your content stays relevant and visible as AI tools evolve.

Budget considerations

When selecting an AEO agency, align your budget with the scope of services you need. Some agencies offer lower rates of basic optimization for AI tools, while others provide more comprehensive, long-term strategies with ongoing audits and optimization efforts. Assess the level of service required for your business and choose an agency that offers a clear, transparent pricing structure based on your needs.

Conclusion

As more people turn to AI tools like ChatGPT, Gemini, and Google AI Overviews for quick, reliable answers, visibility in AI platforms has become essential. Optimizing your content for these tools helps you guarantee that your brand shows up where users are already searching today.

AEO goes beyond trend-following to ensure your content is structured, discoverable, and trusted by AI. If staying visible in a changing search environment is your priority, partnering with the right AEO agency can make all the difference.

Ready to take the next step? Explore scandiweb’s AEO services, and let’s connect and start building a stronger presence in the AI-powered search experience.

The post Best AEO Agencies to Help You Get Mentioned in AI-Powered Results appeared first on scandiweb.

If you operate in multiple markets with separate platforms, managing content, product data, and customer flows likely becomes harder with every update. In a similar case, we collaborated with BK-Group to bring two markets under a single Magento 2 store built with Hyvä, integrated with their ERP and PIM systems.

About

BK-Group is a private company specializing in electronic alarm systems and equipment distribution. As part of the Hikvision family, BK-Group provides high-quality security tech across the Baltics and Poland. Founded in 1994, they’ve earned a strong reputation for reliability and innovation in the security technology sector.

Project goals

The main goal of this project was to consolidate BK-Group’s fragmented eCommerce infrastructure and build a strong foundation for future expansion. This involved:

• Merging two sites – Latvia (WordPress + WooCommerce) and Estonia (Magento 2) into a single Magento 2 instance, hosted on ReadyMage
• Storefront redesign using Hyvä
• Building a fast, secure B2B store with tailored workflows
• Integrating Pimcore and the client’s ERP system to sync and manage product and customer data.

Also read:
Magento B2B Case Study: Building a B2B eCommerce Ecosystem for an IT Supplier

Challenges

Working closely with BK Group’s ERP team required flexibility and multiple testing iterations to finalize integration logic.

Additionally, the production database was hosted on the client’s servers, limiting access and requiring custom deployment workflows with strict DevOps coordination. 

Real-time pricing and stock visibility varied by customer and market (fetched from Pimcore), and the registration/approval process had to align with internal rules and sync properly between Magento, ERP, and other systems.

Approach

We rebuilt BK-Group’s online presence for Latvia and Estonia using Magento 2 with the Hyvä frontend, replacing both legacy setups with a single store. At the time of redesign and development, Hyvä was already widely trusted across scandiweb projects as a reliable frontend choice. 

A core part of the project was connecting Magento 2 with BK Group’s internal systems. Product data, pricing, and customer access rules were all synced via Pimcore and ERP. Magento communicates with Pimcore via APIs, while Pimcore itself acts as middleware between Magento and ERP, allowing product and pricing updates to sync dynamically across storeviews.

Custom B2B features and integrations

BK-Group required a customized B2B solution to ensure that every customer only sees the pricing, inventory, and content relevant to them. To support market-specific workflows, we implemented:

• Real-time pricing per customer, synced from Pimcore
• Customer-specific stock visibility
• Custom registration and approval flow integrated with ERP.

Pricing logic

The pricing logic works as follows: ERP pushes updated B2B prices to Pimcore every few minutes, using the customer code and SAP material code. When a logged-in customer browses product listings or product detail pages, Magento sends a request to Pimcore with those same identifiers to fetch their customer-specific pricing in real time.

Stock is checked at two points: once when a customer views a product detail page, and again during checkout – each via a direct API call to ERP to confirm current availability.

Customer approval process

A customer submits the registration form on the site. After admin approval, Magento checks whether the customer already exists in ERP using the company registration number or email. If not, Magento sends the customer record to ERP to create a new entry. If a match is found, ERP returns the full customer data to Magento.

Magento handles four store views: LV (LV), LV (EN), EE (EE), and EE (EN). Pimcore pushes localized data like pricing and attributes per storeview, while some content, such as brand translations, is maintained directly in Magento admin.

Because the production environment was hosted directly by the client, we worked closely with their DevOps team to define a deployment and access strategy, including limited external access, custom deployment scripts, and secure handling of testing and go-live conditions.

Results

BK Group now operates a single, centralized Magento 2 store for the Latvian and Estonian markets. The unified setup provides a strong foundation for Lithuanian and Polish sites and other markets.

The custom-built B2B logic gives each business customer access to real-time pricing pulled from Pimcore, stock availability tailored to their account, and a registration and approval process fully synced with ERP. 

ERP and Pimcore integrations have streamlined how product, pricing, and customer data flow between systems. With automation in place, internal teams can now easily manage customer-specific logic.

The new store is hosted on ReadyMage and runs on Hyvä, providing a fast and flexible frontend that can grow with the business.

Core Web Vitals and overall frontend performance have already improved and are expected to improve further following the next Hyvä update.

The platform is set up to continue evolving under scandiweb’s Service Cloud support:

• Expanding Pimcore usage across more websites
• Improved performance in pricing sync and loading speed
• Broader use of Pimcore for managing user roles and access control.

Planning a similar migration or facing complex B2B requirements? Scandiweb is the official Platinum Partner of Hyvä and Pimcore. Reach out to us, and we’ll help build the right setup for your business.

The post Case Study: Merging Two Markets on Magento+Hyvä for a Multinational B2B Supplier appeared first on scandiweb.

You really went the extra mile, especially during these last hectic 1-2 weeks. We had an awesome first day at the convention today, and the website was a great help for the team to show people products and prices. You guys delivered, and we are grateful for it!

Marvin, CEO of ATX Fitness USA

Our client, ATX Fitness, saw an opportunity to grow in the U.S. market and moved fast to make it happen. We planned to launch a fully transactional Magento 2 store with the Hyvä theme by June 2025 – just in time for the Home Gym Expo. 

About

ATX Fitness is a European brand specializing in professional equipment for gyms, semi-professionals, and home users. ATX products are distributed by Fitness Seller, a company active in the Benelux region and other European markets. This project marks the launch of ATX’s first direct-to-consumer eCommerce store in the U.S.

The U.S. Home Gym market is one of the largest globally, with high demand for durable, space-efficient equipment suited for hybrid training habits. ATX aimed to stand out with quality, performance, and a full-featured eCommerce experience ahead of Home Gym Expo, their first major U.S. industry event.

Project goals

• Launch a U.S.-specific eCommerce store under the ATX brand
• Build a custom-designed, optimized storefront on Magento 2 using Hyvä Commerce
• Prepare the platform for long-term growth, including ERP integration and a U.S.-based sales and customer support team.

Read more scandiweb x Hyvä case studies here.

Approach

We kicked off the project with a focused UI and design sprint, aligning quickly with the client to avoid rework later. Once design approval was secured, we moved into development without delay, starting from global elements and core templates, then layering in features across product, cart, and checkout flows.

This was our first Hyvä Commerce implementation at scandiweb. As one of the earliest adopters doing hands-on exploration, it introduced technical risk, but the payoff was worth it. Using Hyvä CMS, ATX Fitness got improved site maintainability, a cleaner content editing experience, and more flexibility for marketing pages, while staying within Hyvä’s high-performance frontend environment.

We also implemented Hyvä Checkout, Hyvä’s lightweight alternative to Magento’s Luma checkout, to give the store a faster and more responsive checkout experience.

We covered a full end-to-end Magento 2 build, starting from base setup and extending through every core page and user interaction. The scandiweb team designed and developed all global and market-specific elements, including the homepage, PLP, PDP, cart, Hyvä checkout, account, blog, and CMS content pages. 

Performance optimization, accessibility compliance, technical SEO setup, GA/GTM tracking, transactional emails, and shipping/delivery logic were also part of the scope from day one. Given the pace of the project, we delivered many of these tasks in parallel with design alignment and development sprints and documented deferred requirements for the future roadmap.

Extensions integration

Throughout the project, we worked closely with the client to evaluate extensions for SEO, product feeds, analytics, and custom product logic. We also supported ongoing comparisons between potential alternatives to ensure that every third-party module aligns with business goals and performance requirements.

Key integrations

• Yotpo Reviews, Magefan Blog, Amasty Gift Card, Amasty Product Labels, Amasty Elastic Search, and Mageworx SEO Suite Ultimate for marketing and engagement
• Xtento Product Feed Export to handle product feeds
• Simple Bundle Product extension along with Grouped Product Custom Module, Google reCAPTCHA, and Amasty Rewards for supporting product configuration, security, and user retention.

In addition to the core build, we handled a set of custom requirements identified during the project:

• Product data import and attribute migration
• Store selector, Q&A sections on PDP/PLP, and PLP layout adjustments
• Wishlist customization and custom PDP image roles.

Results

In just 2.5 months, the new ATX Fitness U.S. store went live in a custom design, with full feature coverage, fully optimized, ready for demos:

• Magento 2 + Hyvä store launched on time and within scope
• First Hyvä CMS implementation successfully delivered
• All green Core Web Vitals
• 93–100% PageSpeed scores for accessibility across key pages, to be further optimized following the next Hyvä update
• 15+ additional features implemented before launch
• Product, checkout, and content flows ready to support ATX Fitness’ U.S. entry.

Interested in launching a fast, scalable Magento store with Hyvä Commerce? As the Hyvä Platinum Partner, scandiweb is among the world’s top agencies working with Hyvä. Let’s connect and discuss building the right setup for your next market.

The post Hyvä Commerce Case Study: A High-Performance Store for ATX Fitness’ U.S. Launch appeared first on scandiweb.