Creating high-quality LinkedIn content regularly takes time—especially when aiming for posts that are research-backed, visually aligned with your brand, and consistent in tone. We wanted to eliminate the manual work and make the entire process operate from a simple Slack message.

How it works

This week, we built an AI Agent that automates LinkedIn post creation from a Slack channel using n8n to orchestrate the workflow. The agent combines:

• Research generation via Sonar (Perplexity API) for accurate topic context
• Post copy crafted by GPT-4o in brand voice and LinkedIn tone
• Visual assets created using GPT-Image-1 for on-brand representation
• Slack integration to deliver and iterate via chat

All of this happens from a single prompt inside Slack. The AI agent handles everything—from research to visuals—then sends back a ready-to-publish LinkedIn post, directly in the thread.

Processing and development

• n8n orchestrates API calls between Slack, Sonar, GPT-4o, and GPT-Image-1
• Sonar (Perplexity API) runs deep web research to extract current insights
• GPT-4o agents format the insights into a concise, engaging LinkedIn post and coordinate the Slack chat
• GPT-Image-1 generates visuals tailored to the post topic and tone
• Slack API provides chat interface for initiating and reviewing output

Example walkthrough

Impact

This AI agent turns a multi-hour content creation process into a 60-second Slack interaction:

• Eliminates manual research and design
• Delivers LinkedIn-ready content from Slack
• Maintains consistency in voice and branding
• Frees up creative teams for higher-level strategy

For teams seeking to scale their thought leadership without burning resources, this setup is a game-changer.

Built with

• n8n – AI automation and API workflow builder
• Sonar (Perplexity API) – web research
• GPT-4o – post copy generation
• GPT-Image-1 – image generation
• Slack API – prompt input and result delivery

Complexity

Medium

Looking to leverage modern AI tools within your company? Get in touch and explore next steps.

The post AI Agent Bites #9: AnswerRank LinkedIn post and image agent appeared first on scandiweb.

You don’t need a website visit to make a sale anymore.

More and more, shopping starts with a message: a quick question about sizing, a request for a gift recommendation, a reply to a product suggestion sent last week, or a follow-up from a customer wondering if something’s back in stock. And when the conversation feels natural and helpful, it often ends with a purchase.

That’s conversational commerce. 

It brings the buying experience into chat on any platform through assistants that engage in real time, respond with context, and guide customers toward what they’re likely to buy. Conversational commerce means dynamic conversations shaped by customer behaviour, preferences, and past interactions and overall context. 

Brands adopting this approach will reduce friction and convert interest into revenue without forcing users through long browsing sessions. Leading brands worldwide are already testing it – you can launch it! Let’s look closer at how conversational commerce works in practice, where it’s making the biggest impact, and what to consider if you’re considering launching your own.

Key takeaways

• Conversational commerce brings the shopping experience into real-time chat, helping customers find and buy products through natural 1:1 conversation.
• Success depends on clean product data, clear use cases, strong brand voice, real-time integrations, and continuous optimization.
• With the proper setup, brands can shorten buying journeys, increase AOV, and reduce support pressure without sacrificing user experience.
• scandiweb built an AI assistant for a leading Nordic DIY retailer to guide project-based shopping, leading to 95% higher AOV and a 68% increase in conversions.

What is conversational commerce, really?

Conversational commerce is a new way of selling through real-time dialogue. Instead of browsing a site, clicking, scrolling, and navigating between tabs, customers get help through a conversation. No less than one that feels personal and focused on what they actually need.

It can happen anywhere your customers already are: on your website, in a native app, over chat platforms, or even via SMS. What matters more than the channel is the experience. Your customer asks a question, gets a response, and moves forward with clarity.

Unlike traditional chatbots, modern conversational commerce tools don’t rely on scripts. Instead, they draw on customer data about past interactions with your brand and product details to guide the conversation. This makes the assistant more useful and part of the shopping journey. Done right, it’s fast, helpful, and completely integrated with how shoppers already prefer to communicate.

Why brands are investing in conversational commerce

91% of consumers globally say they’re more likely to shop with brands that offer real-time assistance. And according to industry experts, messaging will be the fastest-growing commerce channel by 2026.

Customers have grown used to getting fast, tailored responses across every platform they use. That expectation doesn’t stop at eCommerce. When someone is ready to buy or has a question holding them back, they want clear answers immediately without switching channels.

Conversational commerce shortens the time between interest and purchase and reduces drop-offs, especially for returning users. It also creates more opportunities to re-engage customers based on real data. Leading brands recognize that this means moving closer to the customer decision point without adding complexity to internal teams.

What makes it work (and what doesn’t)

AI-powered chat can increase conversion rates by up to 30% when used for guided selling. For conversational commerce to deliver such results, here’s what needs to be in place:

• The assistant has access to clean, connected product data and up-to-date info on inventory, pricing, and attributes; if it can’t answer basic questions or suggest available alternatives, the conversation breaks.
• Clear use cases and goals, like recommending products, handling repeat questions, or guiding through gift selection; otherwise, a broad, unfocused assistant often leads to more confusion.
• Assistants that respond based on real-time behavior, recent views, and purchases feel more helpful – context is everything.
• The assistant should speak in a voice that fits your brand and sound natural and human. 
• The experience has to feel instant, like chatting with someone who knows the answer before you finish asking and can suggest things you didn’t even think to ask.

Where things fall apart are generic bots with no access to product or user data and overcomplicated flows that trap the user between decisions.

Conversational commerce in practice

At scandiweb, we’ve built Concierge AI: a conversational digital assistant that feels like a boutique-style concierge. These are examples of how we see it working across different industries, based on real customer needs and typical customer profiles and shopping behaviors.

Curated product discovery

A digital beauty assistant curates full routines beyond answering your customer’s questions. After identifying the customer’s current preferences and routines, it can recommend contour, blush, lip, brush, and highlighter products that match tone and texture. Product education woven into every suggestion guides the customer through a feel-good, ultra-personalized routine, like chatting with a knowledgeable in-store rep, only faster.

Retention & gifting

When a returning customer asks for gift recommendations in his favorite watch store, the assistant instantly can reference previous orders and suggest alternatives, highlighting subtle style differences, showing product images, and offering advice on what to wear for a big event.

Multi-customer logic

A sports store customer chats in to refresh her son’s training gear. The assistant recalls previous purchases, adapts to sizing needs, and recommends tennis-appropriate shoes, even when faced with stock limitations. If a favorite item is out of stock, it can offer brand alternatives instead, tell about pricing options, and style-matching picks for mother and child, creating a helpful shopping experience (in any language, may we add).

Case study: scaling DIY sales with AI-powered guidance

Our client, a leading Nordic home improvement and DIY retailer with over 200 stores, over $1B in annual revenue, and a fast-growing eCommerce channel, serves customers across Sweden, Norway, Finland, and Denmark. 

They want to help customers who come in with a project idea, like building a deck or updating a patio, but don’t know what products, sizes, or quantities they actually need. 

Here’s the problem. 

Around 70% of annual revenue is concentrated between May and September. To support this seasonal spike, our client expands its workforce by up to 10x, primarily hiring temporary staff who need to quickly learn key product knowledge, how to talk to customers about their projects, and how to answer common in-store questions.

The training process was too slow and inconsistent to keep up. So we helped them introduce an AI-powered assistant trained on common DIY projects and linked to their full product catalog.

Project goal

Many customers walk in with a project idea but no clear understanding of what materials are required. They know what they want to build, but not the SKUs, sizes, or product combinations they need. To serve these customers better and increase basket size, this retailer needed a scalable solution to guide customers through their projects and toward purchase.

Solution

We built an AI-based ShopBot, accessible on the website and through in-store kiosks. It’s trained on common DIY projects and connected to the retailer’s full product catalog. The assistant can:

• Advise on how to complete specific projects, step by step
• Recommend exact SKUs, sizes, and quantities for the task
• Process uploaded images to understand the construction area and suggest materials
• Provide consistent, 24/7 guidance without human staff.

Results

Since launch, users interacting with the assistant spent 5x longer engaged than on standard project content. Early results from the pilot:

• +68% conversion rate from users who engaged with the assistant
• +95% average order value
• Projected 50% reduction in seasonal staffing needs once fully rolled out.

This case is a perfect example of how conversational commerce can guide the entire decision-making process, even for complex purchases.

How to get started

You don’t need to launch on every platform or build a fully autonomous AI from day one. But if you want conversational commerce to drive results, it needs a solid foundation.

Here’s what the process looks like:

1. Prepare your data foundation for AI

Clean product data, enriched attributes, and organized customer behavior logs give your assistant something to work with. The better the input, the more useful the conversation.

2. Build your custom conversational agent and create the interface

The interface should fit naturally into your existing flow – on-site, in-app, or embedded in chat, and be able to handle your real use cases.

3. Train your assistant to sound human and on-brand

Scripted or AI-generated, your assistant needs to reflect your tone in how it responds, how much detail it gives, and so on.

4. Connect the agent with your eCommerce & other platforms

To stay helpful, the assistant needs access to current inventory, pricing, customer profiles, and order history. Integrations with your eCommerce platform, CDP, and analytics tools make this possible.

5. Track interactions to fine-tune offers and timing

Monitor how users engage with the assistant. Where do they drop off? What prompts convert? This feedback loop lets you adjust conversation flows, tweak recommendations, and improve overall performance over time.

Start small. Test a focused use case. Then scale once the assistant proves it can make the experience faster and more helpful.

Before you know it, the best salesperson will be in your customer’s pocket.

If you’re curious how conversational commerce could work for your brand, let’s schedule a free consultation! We’ll build a tailored demo with your products and typical customer profiles to show what’s possible.

The post Conversational Commerce Explained: Be Among the First Adopters appeared first on scandiweb.

A luxury fashion house in New York partnered with scandiweb to rebuild its customer data infrastructure and improve how it personalized experiences across channels. With boutiques, outlet locations, international stores, and a high-touch clienteling model, the brand needed a system that could unify fragmented data, support calculated traits, and power smarter segmentation. 

Across the 5 years of using a customer data platform (CDP), they’ve doubled revenue, automated hundreds of hours of reporting work, and now drive over 40% of online revenue through targeted email and SMS, reducing reliance on paid media, strengthening loyalty from high-value customers, and saving money while gaining complete control over their in-store and digital customer data.

About

Our client is a New York City-based fashion house that specializes in women’s apparel, footwear, and accessories, offering a range of modern and sophisticated designs. They sell in physical boutiques and online with an eCommerce store powered by Magento. In the boutiques, stylists and store clerks enhance the shopping experience by offering personalized recommendations based on customer preferences.

Their operations rely heavily on a CDP to support personalization and data-driven decision-making across channels.

Also read:
Webinar Recap: How to Increase Your eCommerce Revenue 2-3x with CDPs

Challenges

The brand’s data ecosystem was highly complex. With operations spanning flagship boutiques, outlet and international stores, stylists providing 1:1 guidance, and an eCommerce store powered by Magento, customer interactions were scattered across many systems. Each customer could be represented by multiple identifiers – email, phone number, loyalty account, purchase history, and in-store interactions, making it difficult to form a unified profile.

At the start of our collaboration, the client was already using an advanced customer data platform to support its personalization and marketing efforts. Over time, however, specific challenges, such as limited flexibility in managing identity resolution rules and increasing demands on reporting, led them to explore new solutions. With a focus on scalability, internal control, and deeper integration across its systems, we chose to migrate to Salesforce Data Cloud.

• Moving to a new platform without disrupting existing workflows
  A major concern during the migration was ensuring that existing workflows remained uninterrupted. The brand faced a strict deadline before their Acquia contract expired, which would have required committing to another year of service. Within this tight timeframe, we had to complete the migration to Salesforce Data Cloud, ensure all systems were integrated, and thoroughly test that data processes continued running smoothly without disruptions.
• Reporting inefficiencies and missed revenue opportunities
  The fashion brand needed deeper insights into customer purchasing behavior, but limited reporting tools required extensive manual effort to compile and analyze data. Since reports could not be generated automatically, teams had to manually export raw data, transfer files via SFTP (Secure File Transfer Protocol), and piece together information using pivot tables and VLOOKUP functions. This time-consuming work diverted resources away from tasks with higher ROI, like more time spent identifying upselling and cross-selling opportunities.
• Identity resolution limitations
  While the team had visibility into why customer profiles merged or didn’t merge, they could not modify the matching logic without support from Acquia. It was difficult to react quickly to data discrepancies or evolve identity resolution rules as business needs changed. 
• Limitations in digital marketing insights and customer acquisition tracking
  Acquia lacked the ability to link website visits, product interactions, and purchase intent to individual customer profiles, leaving marketing teams without clear visibility into customer journeys. Additionally, the platform did not support direct integration with advertising networks, meaning customer data could not be synced with Google Ads or Meta. Without this connection, the company could not run targeted ad campaigns or measure how ads influenced purchases. The transition to Salesforce needed to bridge this gap by integrating web event tracking and aligning customer interactions with marketing performance.
• Scalability concerns and long-term growth
  The company needed a system that could evolve with their customer data strategies without incurring high implementation costs. Already using Salesforce Marketing Cloud, they benefited from good data exchange within the Salesforce ecosystem, making Salesforce Data Cloud a future-proof choice for expanding capabilities and maintaining operational efficiency over time. 

Approach

scandiweb implemented a flexible, scalable solution to migrate to Salesforce Data Cloud, enabling improved customer segmentation and reduced reliance on external services.

Transitioning from Acquia to Salesforce Data Cloud

The previous system was deeply integrated into the brand’s data workflows, making it critical to preserve existing functionality during the migration. scandiweb implemented Salesforce Data Cloud to give the internal team greater control over data processes and simplify future changes.

A key part of the migration involved integrating Salesforce Data Cloud with their existing technology stack, including Magento, in-store customer relationship tools, and external data sources. 

Data validation and integrity checks were utilized throughout the migration to prevent discrepancies and ensure a clean dataset. We developed customized automation rules, which helped map Acquia’s existing data structures into Salesforce without data loss or corruption. 

The transition process included initial user training to help internal teams get up to speed with the new system. With the most technical onboarding still underway, the brand is building toward full in-house autonomy. The migration to Salesforce Data Cloud laid the groundwork for a scalable and future-ready customer data infrastructure.

New data infrastructure with AWS

As the project progressed, we encountered limitations in data ingestion and transformation capabilities within the initial setup. To address it, we explored external cloud solutions and expanded the existing AWS environment to support more flexible and scalable data operations.

The following AWS services were integrated into the new setup:

• AWS S3
  • Used to store most ingested input data sources
  • Also serves as the storage for processed files ready for ingestion into the Salesforce Data Cloud
• AWS Glue
  • Handles data transformations such as blacklisting, standardization, and API-based tasks
  • Combined visual ETL configuration for simpler workflows with custom code via the Script Editor for more complex processes
• AWS Glue DataBrew
  • Enables visual data profiling and initial data exploration to identify key issues and edge cases
  • Also used to validate outputs during pipeline quality assurance
• AWS Lambda
  • Used for real-time processing jobs triggered by specific events
• AWS Redshift Serverless
  • Implemented as a data warehouse to support structured data access and analysis without infrastructure management overhead.

Identity resolution system update

Identity resolution is the process of matching and unifying customer data across multiple touchpoints to create a single, accurate customer profile. Each individual is recognized consistently across online, in-store, and marketing interactions. Without a proper identity resolution system, businesses face issues like duplicate records and a fragmented view of their audience.

To support unified customer profiles, scandiweb configured Salesforce Data Cloud’s identity resolution framework to fit the existing data logic and structure. Our setup included tools for testing and validating match rules in a controlled environment, ensuring confidence in profile merges before any changes were applied. The migration process also included knowledge transfer and onboarding for internal teams to take ownership of the identity resolution logic moving forward.

Calculated traits and segmentation

With unified customer profiles in Salesforce Data Cloud, the team gained the ability to work with calculated traits – derived attributes that go beyond standard fields from source systems. They gave a more detailed view of customer behavior and preferences.

Examples of calculated traits

• Order metrics: lifetime order count, time since first and most recent purchase, purchase frequency by period (last 12 months, 13–24 months, 24+ months)
• Revenue patterns: lifetime revenue, decile rankings, trend segments
• Engagement: email and SMS sends, clicks, opens, CTR by timeframe
• Web activity: total sessions, last session date
• Preferences: sizes, colours, product types, styles, channels, preferred stylist or store.

Segmentation strategies based on enriched customer profiles

• Full-price vs off-price buyers
• Lifecycle stages (new, inactive, lapsed, reactivated, unidentified)
• Revenue trend groups
• Location-based segments for in-store events
• Product-based segments for tailored communications.

Automated analytics with Tableau

With the implementation of Tableau as the primary business intelligence tool, we created an automated analytics framework. Tableau’s integration with Salesforce Data Cloud enabled the company to automate reporting processes and centralize performance tracking across teams.

Enriching customer segmentation with external data

Customer segmentation plays a key role in our client’s personalization efforts. The previous platform made integrating new data sources difficult, limiting the segmentation depth. With the new system, adding third-party data became significantly easier, enabling more refined and flexible audience segmentation.

With Salesforce Data Cloud, we ensured they gained the ability to ingest and unify external data sources to support more advanced customer segmentation. For example, Census Data enriched segmentation efforts by adding socioeconomic indicators.

Results

1. 85% of revenue generated by re-engaging existing customers

With better visibility into customer history and preferences, the brand focused more on retention and lifecycle-based engagement. Personalized campaigns, proactive churn prevention, and curated in-store experiences helped make the most of their existing customer base.

2. Highly personalized in-store customer experience

The brand had existing customer profile visibility in their clienteling tool, which stylists use to enhance the in-store experience. scandiweb supported migrating this data to Salesforce Data Cloud and preserved the export process to Proximity. We built a new dashboard in Tableau, offering greater flexibility to access and customize customer insights. Unlike frontend changes in the previous clienteling tool, Tableau dashboards could be quickly updated internally to expand available data to stylists without relying on external development.

scandiweb’s implementation of Salesforce Data Cloud enabled them to deliver an even more personalized in-store experience. Staff can access detailed profiles before appointments, including transaction history and preferred product categories, to tailor recommendations more precisely, create a curated shopping experience, and anticipate customer needs.

3. Effective email and SMS marketing communication

Email and personalized SMS marketing contribute over 40% of online sales. By contrast, brands without a CDP often generate just 1–3% of revenue from these channels.

The brand leveraged our consolidated customer data to implement more precise email and SMS marketing segmentation. To engage customers, personalized offers and targeted messaging were sent based on purchase history, browsing behavior, and engagement patterns, delivering promotions aligned with their shopping habits and preferences. 

4. Enhanced identity resolution

Salesforce Data Cloud allows them to test, compare, and deploy identity resolution rules. Instead of relying on predefined, unchangeable rules, they can now adjust identity resolution settings based on their specific needs, giving them greater control in managing customer data. 

5. Targeted advertising spend and ROAS improvements

With customer data more accessible and organized in Salesforce Data Cloud, we improved how audience segments were activated within Google Ads and Meta Ads. Building on its existing personalization efforts, they can deliver more personalized ads to existing customers and high-intent prospects, potentially reducing wasted ad spend and increasing ROAS. Additionally, third-party data enrichment enhanced customer lifetime value modeling, helping the team better prioritize high-value audiences.

6. Advanced business reporting and BI solutions

Our integration of Salesforce Data Cloud and Tableau enabled a centralized and automated business intelligence system. Teams can now track KPIs automatically, measure the effectiveness of customer segments, and analyze user journeys.

Interactive dashboards allow deeper customer data exploration, with Tableau’s drill-down capabilities for reviewing behavior by segment, transaction history, or web engagement. This has reduced reporting time and improved the speed and accuracy of business decision-making. With access to more reliable data, our client can plan long-term strategies based on customer behavior.

Looking to transform your customer data management? scandiweb can help you leverage Salesforce Data Cloud or any suitable platform to streamline reporting, optimize marketing, and enhance customer engagement. Get in touch today to learn how we can help!

The post Luxury Retail Meets Data-Driven Personalization: Salesforce Data Cloud Success Story appeared first on scandiweb.

When product information is scattered across platforms, it slows everything down, especially at scale. For Rocket Industrial, managing tens of thousands of SKUs across ERP and Magento 2 created inefficiencies and gaps. See how we helped them bring it all together in one place, creating a clean, organized system their teams can rely on every day.

About

Rocket Industrial is a US-based packaging distributor with roots dating back to 1957, when it began as a family-owned packaging tape company. Over the decades, it has evolved into a nationwide operation that helps businesses solve complex packaging challenges across various industries.

Rocket Industrial serves a diverse range of B2B clients, offering a comprehensive suite of solutions that includes packaging materials and automation systems. Their eCommerce presence is a key part of their operations, supporting thousands of SKUs and an expanding catalog powered by reliable digital tools.

Project goals

Our mission was to build a single source of truth for Rocket Industrial’s large product catalog and make product management faster, more accurate, and enjoyable. We defined the following goals:

• Establish a single source of truth for product data across internal systems
• Centralize SKU management to eliminate inconsistencies
• Reduce manual data entry and associated errors through automation
• Build a structured, scalable product catalog capable of supporting 80K+ SKUs
• Improve the internal UX for managing product attributes and relationships.

Approach

We implemented Pimcore as their product information management system, connecting and consolidating product data from ERP and Magento 2. Our focus was data unification, custom import development, and building a clean foundation for efficient product publishing and management.

Integrating product data with ERP and Magento 2

We started with a full audit of product data across both systems to identify mismatches and incomplete records in SKUs. Since SKUs weren’t always reliable, we created matching rules based on additional product attributes to identify and merge records correctly. 

We also fixed the broken parent-child product relationships. This regrouped related products correctly under one structure.

Custom import logic development for handling scale and limitations

The client’s existing system (AP) limited access to only recently updated products. To work with the full 70,000-item product catalog, we exported the complete dataset from their ERP via CSV and built a dedicated import tool directly within Pimcore.

This workflow found and combined duplicate product entries. It also reconciled records from both the ERP system and Magento 2. As a result, Rocket Industrial has a clean, unified product catalog with 81,000 structured entries managed directly in Pimcore.

Controlled publishing workflow creation for Magento 2

The central Pimcore catalog also held products not intended for display on the Magento 2 storefront. To manage this, we built a custom publishing workflow. This gives internal teams control over product visibility. 

Teams can now review items, mark them, and publish only those that meet the specific business criteria. 

Also read: 

• Case Study: The Power of Centralized Product Data Management in eCommerce
• Revolutionizing Travel with Novaturas: A Pimcore-Powered Innovation
• Pimcore PIM Powers Online Vehicle Sales Microservice Platform

Results

Pimcore implementation delivered structural improvements to Rocket Industrial’s product management processes:

• The unified product catalog grew from 70,000 to 81,000 complete records
• Duplicate entries from ERP and Magento 2 were identified and merged
• SKU inconsistencies were resolved across systems
• Broken parent-child relationships were successfully rebuilt
• Manual data entry was significantly reduced through automated imports
• Products are now published to Magento 2 through a controlled workflow
• Internal teams can effectively manage and filter product data within Pimcore.

Need a better way to manage product data across platforms? scandiweb is a certified Pimcore Platinum Partner with a team of experts ready to tackle your product data challenges. Reach out to us, and see how we can help you solve any challenges with PIM.

The post Case Study: Centralizing Rocket Industrial’s 80K+ Products with Pimcore appeared first on scandiweb.

Managing content across a multi-region, multi-language eCommerce platform requires structure. It’s the only way to scale. See how we created a reliable system to manage our client’s global content without slowing down regional teams or creating new dependencies.

About

OM System is a digital imaging and audio brand operated by OM Digital Solutions Corporation. Their global Adobe Commerce platform powers eCommerce for a wide range of high-end cameras, lenses, and accessories. With 70 store views and customers in over 20 languages, content consistency and control were becoming more complicated to manage.

Project goals

• Integrate a headless CMS to manage content delivery across 20 languages
• Replace scattered content logic with structured, reusable blocks
• Support a parallel frontend migration to the Hyvä theme
• Enable API-driven dynamic rendering inside Magento (Adobe Commerce)
• Improve content publishing workflows
• Set up an editor-friendly CMS studio for regional teams.

Approach

OM System’s platform is anything but simple. With three Adobe Commerce installations, 35 live store views, and another 35 in staging, plus content in 20 languages, their content architecture needed a clear structure. 

We chose Sanity CMS because of its headless, API-first approach, built to handle exactly this kind of multilingual, multi-site complexity. Unlike traditional CMS platforms, Sanity gave us the flexibility to define and scale content independently from the frontend.

The first step was defining and structuring all content blocks. We began by working closely with the client to map all design-approved content blocks before implementation across regions, categories, and page types. Some of them were simple, like banners or callouts, while others involved more complex logic and variations based on language, device, or campaign. In total, we built over 55 modular blocks, replacing at least four different methods the OM System’s content team previously used to create tiles in Magento Page Builder.

Dynamic rendering logic

On the backend, we developed custom logic in Magento (Adobe Commerce) to dynamically fetch and render Sanity content, block by block, across all 70 store views. A single structured content source could now power every site variant, whether for the UK, Japan, or the US, while respecting region-specific language and layout differences. This setup also supports flexible scheduling and localization, with editors able to control which content appears where and when, without touching the codebase or involving developers.

Sanity Studio

We configured the Sanity Studio interface to mirror real editorial workflows. This has reduced content publishing time from hours to minutes, while improving consistency across markets. Instead of a cluttered CMS with unnecessary fields and scattered logic, editors now have a clear, clean interface. They can:

• Update content across 20 languages in one session
• Reuse structured blocks across campaigns and countries
• Preview changes in context
• Push updates live without waiting on dev cycles

Collaboration and documentation

This project ran in parallel with a Hyvä frontend migration, while other teams worked on support, SEO, and analytics, with regular internal syncs.

For SEO, we added custom fields in Sanity for meta titles, descriptions, hreflang, canonical tags, and OG tags. These are fully manageable by editors and backed by automatic fallback logic to Mirasvit SEO rules when no custom value is set. 

After go-live, we held several workshops and supported OM System’s internal teams with how-to documentation and user guides, giving them confidence in the system from day one.

Also read:
Cervera’s Successful Digital Turnaround with Sanity CMS

Results

With 70 store views connected to a single CMS instance, this project stands among the largest Sanity implementations built to date. The system now supports 20 languages, 55+ reusable content blocks, and fully API-driven content rendering across Magento storefronts. Editorial workflows are significantly faster through an intuitive Sanity Studio setup. 

Key outcomes

• 70 store views managed under one CMS (35 production + 35 staging)
• 20 languages supported
• 55+ reusable content blocks implemented
• Content updates that previously took hours now take minutes
• Previously fragmented Magento content creation logic unified under a single CMS
• Delivered in 5 months from design sign-off to go-live.

Need to come up with an efficient way to manage and scale your content? Get in touch with us today to plan your CMS transformation.

The post Case Study: One of the World’s Biggest Sanity CMS Builds appeared first on scandiweb.

Every click, product view, purchase, or support request reveals something about your customer. That is, if you know where to look. In eCommerce, customer data platforms (CDPs) are the power tools for understanding customer behavior and personalizing marketing, meanwhile improving overall customer experience.

Research shows that companies using CDPs achieve 2.9x greater YoY revenue growth compared to those that don’t. However, to achieve those results, you need to fully understand the topic. In our latest webinar, we broke down what a CDP is, what it does, how it works, and what it takes to implement one. Here’s what you need to know.

Watch the full webinar recording above, or read more for all the essentials on how CDPs can help eCommerce retailers grow their revenue.

What are CDPs and why should you care?

A customer data platform (CDP) is software that unifies customer data from various channels, such as eCommerce, mobile apps, POS, live chat, SEO, PPC, and more, into a single, centralized customer profile.

Unlike customer relationship management (CRM), data management platforms (DMP), or data warehouses, a CDP brings together customer data from all sources into one clear profile, online and offline, that stays updated over time. This makes it easier to personalize in real time and connect across channels.

It offers stronger identity resolution than CRMs (which focus on known contacts) and DMPs (which rely on anonymous data), and unlike data warehouses, CDPs are built for analysis and action.

Essentially, CDPs are built specifically for identity resolution and real-time personalization, which most CRMs and DMPs are not.

CDP core values

CDPs bring structure to messy customer data and turn it into something you can use. Here’s what they do best:

• Connect and combine data from all platforms (eCom, CRM, POS, ads, etc.)
• Merge customer info into one clear profile
• Build smart audience segments, including AI-driven ones
• Send personalized messages through email, ads, and more
• Track customer behavior, predict churn, and show segment performance.

Why CDPs are so valuable for customer retention and personalization

Businesses that use CDP show up to 90% reduction in time spent on segmentation, customer journeys, and reporting tasks. 

CDPs also unify data across channels so teams can personalize in real time and act on insights without manual effort. They increase customer loyalty and lifetime value, reduce churn, support data privacy compliance, as well as save time through effective data management, all of which contribute to higher revenue and marketing performance.

Also read:
What is CDP? Guide to Customer Data Platforms for eCommerce

CDPs and AI

AI goes beyond being a trendy term in the context of CDPs; it serves as a practical engine driving better decision-making and more precise targeting. Predictive features, such as anticipating user churn and tailoring messages to be more timely, are some of the ways AI helps CDPs make better use of data.

Here’s how the top platforms today use AI for customer insights, making them more precise and actionable.

Predictive audiences and analytics

Various AI models can help businesses forecast the following customer behavior:

• Who is likely to make a purchase soon
• Who is at risk of churning
• Which customers are likely to respond to a campaign
• How much a customer might spend over their lifetime
• When a customer is most likely to buy again.

Some CDPs with these features are Segment (Twilio Segment), Salesforce Data Cloud (Einstein AI), Bloomreach, and Treasure Data.

Real-time customer data unification and identity resolution

Real-time data unification means bringing scattered customer data into one clear profile automatically and instantly.

AI connects the dots between different devices, email addresses, purchase histories, and more to recognize the same person across platforms. This gives you a complete, up-to-date view of each customer without manual cleanup.

Some of the platforms that have these features are BlueConic and Salesforce Data Cloud.

Automated personalization

Automated personalization uses AI to adapt every interaction to each customer without manual setup. It adjusts everything from messages and website content to product recommendations based on what users engage with the most. 

Platforms like Salesforce Data Cloud, Bloomreach, and BlueConic use this to make personalization faster and more relevant.

Data cleansing and enrichment

Data cleansing and enrichment keep your customer data accurate and complete, so that it’s ready to use. In this case, AI takes care of routine cleanup by removing duplicates and fixing inconsistent and missing fields, so your team doesn’t have to. 

Tools like Treasure Data and BlueConic’s AI Workbench help maintain high data quality without manual effort.

Dynamic segmentation

Dynamic segmentation uses AI to automatically group customers based on how they behave and what they’re likely to do next. It updates segments in real time, so you’re always targeting the right people with the right message. 

Bloomreach, Segment, and Treasure Data make this possible with no constant manual updates.

Customer journey optimization

Customer journey optimization uses AI to track how people move across channels and fine-tune their experience as it happens. It spots drop-off points, adjusts timing, and delivers the right message at the right step, whether that’s on your site, in an email, or in-store. 

Bloomreach, BlueConic, and Salesforce Data Cloud are some of the tools that can handle this in real-time.

Case study: how a New York retailer increased revenue with CDP

A luxury New York fashion retailer struggled with fragmented customer data across online, offline, and loyalty channels, making personalization difficult. Without a unified view, insights like preferences and purchase history had to be manually calculated, limiting effective segmentation and targeted campaigns.

After implementing a CDP, these were the outcomes: 

• 2x revenue growth in 5 years
• 85% of revenue from re-engaged customers
• Over 40% of online sales driven by email and SMS
• Personalized customer service and in-store experience, knowing customer preferences
• Automated business reporting and insights, saving hundreds of hours of manual time
• In-depth customer knowledge, driving smarter product and marketing strategies across teams.

What it takes to implement a CDP platform

Implementing a CDP requires the right team and a solid tech stack. When you’re thinking about the team setup, these are the people to consider:

• Marketing, CRM, and eCommerce managers who define use cases and activation goals
• People who know the customer and the data
• Tech specialists: a CDP specialist, a data (analytics) engineer, and a BI specialist.

For a tech stack, you’ll need:

• A data warehouse to store and organize clean customer data
• A traditional or composable CDP 
• Data transformation tools to clean and transform data if not handled inside the CDP
• BI tools (e.g., Tableau, Power BI, or Looker) for visualization and reporting.

Our scandiweb team has the expertise to manage every aspect of CDP implementation, from defining use cases and activation goals to handling the entire tech stack. Our team of specialists will ensure your data is organized and ready for action, giving you the best results.

How to choose the right CDP platform

Before choosing the right platform, you have to keep in mind that there is no “one size fits all” solution. Here’s what you need to consider when choosing the right tool for your business.

Types of CDPs

The two main categories, traditional and composable, offer different levels of flexibility, control, and ease of use.

Traditional CDPs are packaged, all-in-one platforms that come with built-in tools for data unification, identity resolution, and activation. They’re quicker to set up and easier to manage, making them a good fit for teams that want a ready-to-use solution without heavy internal resources. However, they offer less flexibility when it comes to customization.

Whereas composable CDPs are modular and built on top of your existing data warehouse. Instead of an all-in-one tool, you combine best-in-class tools for each function. This gives you full control and avoids vendor lock-in, but it requires a strong internal data team to manage setup and ongoing operations.

Factors to consider when choosing the right CDP for you

When you’re about to choose a CDP, there are many variables to consider, which is why we here at scandiweb work closely with the client and run an in-depth discovery to evaluate everything properly. These are the key factors to consider:

• Business goals you want to achieve (data unification, personalized marketing, retargeting in ads, or better customer insights)
• Pricing models based on tracked users, event volume, or data records
• Privacy compliance requirements like GDPR or CCPA
• Data source compatibility, as some CDPs offer native integrations, while others require external connectors or workarounds
• Data transformation needs, as some CDPs expect cleaned, structured data before ingestion
• Activation targets like email platforms or ad tools
• Identity resolution flexibility 
• Documentation quality and access to onboarding resources.

Some of the top CDP platforms

1. Salesforce Data Cloud offers strong integration with Salesforce tools and flexible data modeling, but has complex pricing, limited documentation, and non-persistent unified IDs.
2. Bloomreach is user-friendly, cost-effective for small and medium-sized businesses, with solid support and documentation, though it has a learning curve and less intuitive data validation.
3. Segment excels in integrations and data governance, with native messaging and AI features, but lacks built-in reporting and can become costly with scale.
4. Insider CDP includes built-in automation and a strong UI, but it offers fewer integrations and has pricing and stability issues.
5. Adobe Experience Cloud is powerful but best suited for companies already using the full Adobe stack.
6. Pimcore is open-source and flexible, ideal for custom setups, but has fewer ready-made connectors.
7. RudderStack is warehouse-native, giving full data ownership and flexibility, but requires technical expertise for setup and maintenance.

Summary: why a CDP is a must for any eCommerce store

Retailers often struggle with disconnected systems and scattered customer data. This limits the ability to satisfy customers and slows down personalization efforts and business decisions. Bringing online and offline customer data together solves a key challenge for businesses. CDPs provide the central system needed for this.

Take, for example, the New York luxury fashion retailer. With the help of our CDP experts and all the right tools, we were able to increase their revenue more than twice. In-depth customer knowledge is one of the driving factors for creating smarter product and marketing strategies.

With the rising role of AI in marketing, CDPs are becoming even more effective for delivering true data-driven insights and actions. Therefore, retailers that don’t use CDPs in their eCommerce are losing to their competitors. 

Want a clearer view of your customers? Get in touch with us and see how unified customer data leads to more effective marketing and stronger customer loyalty, producing better business results.

The post Webinar Recap: How to Increase Your eCommerce Revenue 2-3x with CDPs appeared first on scandiweb.

Search behavior is rapidly shifting—people now expect AI-driven queries rather than manual filters and browsing. Brands must be discoverable by AI, accessible directly in assistants like ChatGPT. Offering a custom GPT like SpoGPT is a crucial step toward being present and effective in this emerging “AI search” landscape.

How it works

This week, we’ve built SpoGPT, a ChatGPT Custom GPT for Sportland, a leading sports apparel and equipment retailer. Leveraging OpenAI’s Custom GPTs feature with actions, SpoGPT combines:

• General GPT knowledge (product trends, use cases, web search)
• Live access to Sportland’s Magento catalog via custom OpenAPI‑based actions

These “actions” let SpoGPT fetch real-time details—inventory, pricing, sizes—on demand, blending structured API data with conversational expertise

Processing and development

• Defined robust OpenAPI schemas covering key Magento endpoints
• Configured secure authentication using API-key auth
• Uploaded action schemas and configured them via the Custom GPT builder
• Crafted prompt flows that trigger API calls only when needed, keeping conversations coherent

Example walkthrough

Impact

SpoGPT positions Sportland as AI-ready:

• Shows up in AI-led search via ChatGPT
• Offers real-time accuracy on price, stock, size
• Delivers expert guidance on fit, terrain, materials
• Simplifies UX with natural language—no discipline switching needed

This demonstrates how brands can embed themselves into the future of AI-powered shopping.

Built with

• OpenAI Custom GPTs – leveraging the Actions feature for API integration
• Magento API – source of real-time product data

Complexity

Medium

Looking to leverage modern AI tools within your company? Get in touch and explore next steps.

The post AI Agent Bites #8: SpoGPT, the Sportland AI Agent appeared first on scandiweb.

Checkout expectations have changed. Shoppers now demand speed and control with ease, whether online or in-store. Self-checkout solutions deliver exactly that.

Did you know that about 96% of consumers have used a self-checkout kiosk at least once? And 77% of them do so because it’s faster. 

Allowing customers to scan, pay, and go on their own terms helps reduce checkout wait times and cut operational costs, thereby improving satisfaction, especially among younger, digital-first shoppers.

In this article, learn more about the importance of self-checkouts in retail and see two successful case studies from our recent projects.

Self-checkout in retail: why it matters

Today, when speed and convenience influence customer loyalty, self-checkout kiosks are becoming a core part of the retail experience.

Retailers using self-checkout benefit from:

• Shorter checkout times and fewer frustrated customers
• Fewer staff interventions
• Higher accuracy in transactions
• A great checkout alternative for customers with varying demographics and technical knowledge.

Research shows effective self-checkout setups can cut wait times by up to 40% while increasing customer loyalty.

Best practices for efficient self-checkout design

It’s important to design a checkout experience that is convenient and accessible for everyone. To make sure of a smooth flow, these are the best practices to keep in mind:

• Make an easy-to-use interface to reduce confusion and speed up the process
• Use large fonts and high contrast to help users of all ages and abilities navigate the checkout confidently
• Minimize steps and optimize the scanning process for speed
• Make payments quicker by showing multiple options, and handle errors effectively with clear instructions and accessible help
• Give shoppers the choice to use loyalty programs or check out as guests, depending on what they prefer.

When these principles are put into practice, self-checkout can be both effective and enjoyable. 

Also read:
How to Implement Self-Checkout Right: Best Practices

Now, let’s look at two successful examples that illustrate how self-checkout systems can improve customer experience and store performance.

Self-checkout case studies

Making car buying easier with self-checkout

Over the past several years, scandiweb has worked closely with Rockar, an automotive retail company in the United Kingdom, to change the way people buy cars. 

We built the first digital platform for buying premium vehicles fully online. The platform offers features that were rare in the industry at the time, all available in one, easy-to-navigate interface: 

• Configure vehicles
• Apply for financing
• Test drive booking
• Trade in the old car, and complete the purchase entirely online. 

To bring the digital journey full circle, we extended the online experience into Rockar’s physical showrooms by developing a custom in-store point-of-sale (POS) solution. Touchscreen kiosks in the showrooms replicate the website’s full functions. Customers can browse car models, explore financing options, book test drives, and place their order right there, without sales pressure. 

The system uses Magento and a microservices architecture, ensuring data stays in sync in real-time. This lets customers begin online and continue in-store, or vice versa. It links digital and physical experiences directly.

Connecting the online and physical store processes changed how Rockar used its showrooms. Staff became more like helpers, guiding customers rather than traditional sales agents. This puts the customer in charge, making the car-buying process clear and independent from start to finish.

Scaling lottery sales with custom POS

Another example is Spinola, a major lottery operator in Southern Asia. They needed a system capable of managing thousands of retail locations and processing high volumes of real-time transactions quickly and reliably.

We designed and delivered a system built specifically for their operational requirements. It provided the necessary speed and reliability, supporting their expansion plans without interrupting their existing operations:

• Custom POS app solution addressing unique business requirements
• Multiple pre-made lottery types and individually configurable widgets
• Frontend data integration from multiple sources
• Real-time data exchange supporting lotteries and payouts
• Parallel delivery streams enabling fast feature rollout without disrupting the core system.

Take a look at the video below to see how the Spinola self-checkout system works.

The solution supports Spinola’s current markets and is ready for expansion into new regions. Spinola now has a reliable and adaptable POS system that successfully runs Spinola’s daily lottery operations across thousands of retail points. It was deployed on more than 15,000 devices in the first country.

To understand how we built and implemented the custom POS system for Spinola, read this in-depth case study.

Final thoughts

Self-checkout systems change how people shop, giving customers more control. Businesses also gain by simplifying work and cutting costs. At scandiweb, we’ve seen this impact directly; custom self-service setups shift industry practices.

As customer habits change, custom, flexible self-service systems become essential. Across multiple business sectors, our self-checkout solutions help companies meet customer needs through easy, enjoyable shopping experiences.

Considering self-checkout for your retail store? Our UX experts will help you get it right. Reach out to us by filling out the contact form for a free consultation.

The post How Self-Checkouts Make In-Store Experiences Faster and Smarter appeared first on scandiweb.

November 13, 2025 | Toronto, Canada

Meet Magento is heading north. For the first time ever, the global Magento and Adobe Commerce conference is coming to Canada. On November 13, eCommerce professionals will gather in Toronto for a full day of practical sessions, expert talks, and conversations that matter to the whole Magento community.

Why attend Meet Magento Canada 2025

After 11 successful years in New York, Meet Magento is expanding with the exact same community-driven format and industry relevance. This event brings together everyone building, selling, scaling, or supporting Magento stores. And just like Meet Magento New York 2025, scandiweb is proud to join Adobe and the Magento Association in making this milestone event happen.

500+ attendees
40+ speakers from Adobe, global brands, and ecommerce leaders
25+ sponsors supporting the Canadian debut

Merchants, retailers, developers, marketers, technology partners, and Adobe specialists—let’s meet at Meet Magento Canada (#MM25CA)!

On the agenda

Every session is selected by an independent committee, and the agenda is built around the hottest topics. There is no fee to submit speaker applications, no pay to speak spots on the business and technical tracks, and no payments for speakers. Instead, we will present talks that have earned their place based on relevance and contribution.

You’ll get access to three core tracks:

• Technical sessions from Magento engineers working on live implementations
• Business talks from merchants sharing the decisions that helped them grow
• Solutions, tools, workflows, and proven tactics to help you solve challenges and scale.

Meet #MM25CA organizers, scandiweb

scandiweb is also organizing this year’s Meet Magento New York, one of the longest-running and most attended Magento events globally. Now we’re bringing that same energy and focus to Toronto. With 21+ years of eCommerce experience and the world’s largest Adobe Commerce-certified team, we’ve partnered with PUMA, Jaguar Land Rover, The New York Times, JYSK, and 600+ leading brands. 

We’re super excited to help launch this new chapter for the Magento community. Be part of the first-ever Meet Magento Canada!

See you in Toronto

November 13, 2025 | Toronto

Stay tuned for the Early Bird ticket launch!

Let’s meet, talk, learn, share, and see what’s next.

Got a question about the event or want to connect with scandiweb? Feel free to send us a message, and we’ll get back to you within 48 hours.

The post Meet Magento in Canada for the First Time: See You in Toronto! appeared first on scandiweb.

The leaders of tomorrow are already building with AI-first today. The Baltics have now caught up!

This May, at the iconic House of the Blackheads in Riga—a landmark long associated with trade and commerce—scandiweb hosted the first-ever Baltics AI Meetup dedicated to eCommerce. The energy was high, and the conversations pointed clearly in one direction: AI is becoming the backbone of how forward-thinking companies grow. 

The #1 Baltics AI Meetup sold out in advance and brought together a mix of business leaders, marketers, technologists, and AI practitioners who share a common interest in understanding how to move beyond ChatGPT tinkering and start putting AI to work in ways that actually change how businesses operate.

Attendees listened to insights and real examples and participated in open discussions about the potential and the challenges of using AI in business. We looked closely at how companies are already applying AI in eCommerce to streamline workflows and reinvent customer interactions, and what steps are needed to move from theory to practical, lasting impact.

Key takeaways

• AI is becoming the core of how modern companies operate; it’s part of the entire operations, from strategy to execution.
• Answer engine optimization is the next frontier of SEO, helping brands show up inside AI-generated answers.
• Many businesses, including startups and global brands, are already using AI to automate tasks, personalize experiences, and launch campaigns faster. 

About Baltics AI

Baltics AI (B/AI) is a meetup series dedicated to helping businesses in the Baltics explore AI in practical ways. Instead of big promises or buzzwords, each session focuses on tools, frameworks, and use cases already working in local companies today. 

Who is B/AI for?

• Business leaders & owners looking to learn how others in the region are applying AI
• Innovation leads & product managers exploring how to pilot AI in workflows and scale successful use cases across their teams
• AI-curious professionals eager to cut through the noise, hear real stories, and start experimenting with AI in low-risk, meaningful ways.

During these events, you can expect keynote speakers, real-world showcases, panels, live networking with founders, execs, and AI explorers, and actionable inspiration.

Beyond ChatGPT: real AI transformation

Kicking off the first event, Glebs Vrevsky, co-founder of scandiweb and our AI evangelist, set the tone with a bold but necessary distinction that using AI as a tool is not the same as adopting AI as a way of working.

Most teams today use ChatGPT, Gemini, or other tools to speed up isolated tasks. It’s a productivity boost, like switching from fax to email, but it doesn’t change the underlying process. That’s only the starting point.

Actual AI adoption means putting AI at the heart of how decisions are made, how teams collaborate, and how systems are built. It’s less about speeding up the old way and more about questioning whether the old way is even worth keeping.

This shift is as much cultural as it is operational. It requires new habits, new roles, and a different mindset altogether—one where AI is a core part of how work gets done.

What’s holding companies back from adopting AI?

Next, Antons Sapriko, founder of scandiweb, addressed the question, “If the value of AI is so clear, why aren’t more companies fully embracing it?”

Rather than hype or assumptions, Antons shared grounded insights based on real conversations with clients and partners. The blockers are usually strategic and organizational, not technical.

Some of the main challenges

• Fear of the unknown—many teams hesitate to adopt AI due to unclear outcomes or perceived risks
• Lack of clarity—without a clear use case or business goal, AI efforts stay stuck in the experimental phase
• Unstructured or siloed data makes it difficult to train or integrate AI tools effectively
• Overreliance on tools—teams often use AI as a productivity hack, rather than reshaping workflows around it.

For AI adoption to move beyond isolated use cases, companies need to shift how they think about change. This will most likely mean rethinking the tool stack entirely.

Getting discovered by AI via AEO

As AI tools like ChatGPT, Claude, Perplexity, and Google’s AI Overviews become key sources of answers online, traditional SEO is no longer enough. That’s where answer engine optimization (AEO) comes in.

Artyom Jurkevich, co-founder of BetterAnswer.ai, introduced the concept of AEO as a strategic approach to making content AI-readable, AI-quotable, and AI-visible. Traditional SEO and aiming for the highest rankings are being replaced by becoming the answer.

AEO focuses on helping your brand show up inside AI-generated responses by aligning your content with how these systems gather and deliver information. Artyom laid out a practical 4-step framework for AEO:

1. Know your position

Use tools like AnswerRank to find out if and how your brand appears in AI-generated answers. Identify which queries, topics, and content formats are favored.

2. Make your site AI-ready

Structure your content clearly, with headers, FAQs, internal links, concise snippets (40–60 words), and schema markup. Create pages like “Top questions about [your product]” or detailed comparisons.

3. Enhance your presence beyond your site

AI tools rely heavily on third-party signals. Building up brand mentions, positive reviews, and being referenced in trusted sources all contribute to better AI visibility.

4. Maintain your position

AEO requires ongoing refinement, monitoring where you appear, and adapting when Google or OpenAI updates their models.

AI-generated results reflect how your brand lives on the broader web, and AEO is your way of creating that perception.

AI use cases from our clients

Glebs presented a hands-on showcase of how scandiweb clients are already using AI. The use cases can be structured in three levels of adoption, each representing a deeper integration of AI into everyday work.

Level 1: out-of-the-box tools

These are plug-and-play AI solutions that improve productivity without requiring major system changes, for example:

• A custom GPT-based advisory board, simulating decision-making styles of leaders like Elon Musk and Warren Buffett
• Generating marketing assets, like product visuals and campaign ideas, using reference images and prompt-based tools
• Automating recurring content tasks, such as structuring lessons or content calendars for marketing leads.

Level 2: context-aware tools

These tools go a step further by using company-specific context to deliver more relevant output:

• Feeding transcripts from meetings and sales calls into AI to turn them into actionable insights
• Building concierge-style AI assistants that recommend products based on customer profiles and preferences
• Internal AI planners for project scoping, helping teams go from idea to structured action plan in minutes.

Level 3: connecting & automating with agents

Here, AI stops being a helper and starts acting as infrastructure. Think multi-agent systems that:

• Monitor SEO trends and auto-generate blog posts
• Act as internal procurement assistants, reading and analyzing offers
• Automate internal workflows across tools like Notion, n8n, and Claude, turning fragmented processes into smooth, AI-powered operations.

The common thread is that none of these projects started as AI initiatives. They started with a business need, and AI happened to be the best way to solve it.

Q&A: How Samsung reinvented live shopping with AI avatars

One of the standout moments of the event was the fireside chat between Boriss Sadrins (Samsung) and Maris Skujins (scandiweb), where they discussed how Samsung tackled a growing challenge in their strategy: live shopping at scale.

Traditional live shopping campaigns required large production teams, hired talent, multiple weeks of prep, and significant costs. They were engaging but not scalable across markets or frequent product launches.

scandiweb introduced a solution using AI avatars—digital versions of Samsung experts who could speak multiple languages, deliver product presentations, and scale instantly across campaigns. The avatars were paired with a custom-built live shopping engine capable of generating scripts and videos quickly without any physical production.

As a result:

• Campaigns that once took weeks could now launch in days or even hours
• Significant cost savings without compromising quality
• Flexibility to adapt to different products and audiences with no added production work
• The approach is not limited to B2C—Samsung has already applied the same system to B2B campaigns.

AI in Estonia: practical use cases across industries

To round out the day, Nikolay Sekachev, founder of NeuroSchool, gave the audience a look into how Estonian companies integrate AI into their daily operations.

Instead of big, high-risk overhauls, many Estonian teams are embracing small, targeted implementations that deliver fast results. Some standout examples include:

• AI-powered research agents using tools like Gemini to run deep competitive analysis, scan legal documents, and generate content in multiple formats: infographics, web pages, even quizzes
• Automating repetitive tasks like invoice reading, form filling, and report generation cuts hours of manual work down to minutes
• Data enrichment for eCommerce, linking product records with real-time supplier data to create better listings and reduce mismatches
• Vision-based diagnostics, including tools that analyze facial or skin images to provide feedback in medical, wellness, or education contexts
• Voice-powered assistants built to help with schoolwork or even car buying

What makes these examples compelling is their variety and how attainable they feel. No giant AI departments or multi-year transformation projects; just sharp use of the available tools, guided by real business needs.

Closing thoughts

AI has already changed how businesses solve problems, create content, interact with customers, and make decisions. Global brands like Samsung and agile teams in the Baltics and beyond are moving forward—sometimes with big leaps, often with small, smart steps. The shift is underway, whether through plug-and-play tools, contextual assistants, or fully automated agents.

There’s no one-size-fits-all path, but all businesses are learning that real value comes not from using AI to speed up old workflows but from rethinking them entirely.

At scandiweb, we’re continuing to explore what this means for eCommerce and are excited to help others do the same.

If you’re ready to build something AI-first or just want to discuss the possibilities, we’re here. Send us a message, and let’s come up with something great together!

The post The AI Era in the Baltics Is Officially Underway: Baltics AI eCommerce Meetup #1 Highlights appeared first on scandiweb.