RetailConversational AI

innFactory builds STACKI, a sovereign Llama 3 RAG chatbot for Schwarz Group employee training on STACKIT

Schwarz Group (STACKIT)· GermanyLlama 3 · STACKIT Model Serving · STACKIT Kubernetes Engine +1

The Schwarz Group needed an internal AI assistant for employee training with the requirement that no data leave Germany. innFactory built STACKI, a RAG chatbot using Llama 3 with STACKIT Model Serving, PGVector in PostgreSQL and STACKIT Kubernetes Engine, hosted exclusively in German data centers.

Overview

The Schwarz Group needed an internal AI assistant for employee training with the requirement that no data leave Germany. innFactory built STACKI, a RAG chatbot using Llama 3 with STACKIT Model Serving, PGVector in PostgreSQL and STACKIT Kubernetes Engine, hosted exclusively in German data centers.

This entry has 12 published fields tied to exact passages in an immutable source capture.

Inspect the highlighted source

The challenge

The Schwarz Group uses a sovereign Google Workspace and needed an internal AI assistant for employee training, with the requirement that no data may leave Germany; classic AI solutions such as ChatGPT or Claude were not an option because they run on US servers.

The solution

innFactory developed STACKI, a RAG-based chatbot that runs entirely on STACKIT infrastructure in German data centres. It uses Llama 3 as the open-source LLM with STACKIT Model Serving for GPU-accelerated inference, PGVector in PostgreSQL for vector embeddings of internal training materials, and STACKIT Kubernetes Engine for container orchestration.

Conversational AIGenerative AI

Reported business value

According to the source, STACKI runs 100% sovereign in German data centres, searches and summarises internal documents, supports employee training on the sovereign Google Workspace, transfers no data to external providers, and is GDPR compliant and AI Act ready. The source gives no quantified results.

Sources

Open any source and check the claim yourself — that is the point of the register.

This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)

Related entries

Other retail entries in the register.

All entries
RetailMachine LearningPublic Cloud

John Keells Holdings scales retail advanced analytics with Azure Databricks

John Keells Holdings' OCTAVE Center of Excellence used Azure Databricks, Delta Lake and MLflow to unify siloed data across 70 companies and seven industry verticals, running over 40 advanced analytics use cases including a store-clustering supply chain optimization pilot for its Keells supermarket chain that improved dry-category revenue and cut the time for annual category review.

96/100HighPrimary source
John Keells HoldingsAzure Databricks · Delta Lake · MLflow +1
RetailConversational AIPublic Cloud

Omoda's generative AI stylist lifts conversion rate 2.5x for online fashion shoppers

Dutch fashion retailer Omoda built AI stylist, a virtual shopping assistant powered by Google Cloud's Gemini Pro 1.5 model with Vertex AI Multimodal Embeddings and Vector Search. AI stylist holds natural-language conversations with customers, understands their needs, and builds complete outfits combining clothing, shoes, and accessories rather than just matching similar products, with an Intelligent Brand Matching feature that factors in brand affinities. Customers who use AI stylist convert at a rate 2.5 times higher than average, and the tool also improves inventory management through better trend anticipation.

96/100HighPrimary source
Omoda· NetherlandsGemini 1.5 Pro · Vertex AI Multimodal Embeddings · Vertex AI Vector Search
RetailComputer VisionPublic Cloud

Furniture.com Transforms Online Search with Databricks

Furniture.com unifies over 60 retail partners and 1.5 million SKUs on the Databricks Data + AI Platform, using Delta Lake, MLflow and Unity Catalog to run its ML lifecycle. Its Find It AI product-discovery tool uses generative AI to create a synthetic image representing shopper intent, then matches it against the product catalog for image-based search. A Collections model uses LLMs to automatically group related products, finding more than 16,000 collections across 50+ partners with no human intervention. Users who interact with Find It AI show a click-through rate 8x higher than baseline and a return rate 3.2x higher than baseline.

96/100HighPrimary source
Furniture.comDatabricks Data + AI Platform · Delta Lake · MLflow +3
RetailConversational AIUnknown

How Walmart Achieved Enterprise Transformation Through Digital Experience Innovation

Working with implementation partner Nativa on LivePerson's technology, Walmart customized and integrated conversational technology into its WhatsApp channel, including a FAQ-Transactional Bot to streamline searches, inquiries and purchases. The solution delivered a 60% increase in productivity and a 10 percentage point improvement in CSAT/NPS, along with operational savings through automated customer service processes.

92/100HighPrimary source
WalmartLivePerson · WhatsApp

Was this helpful?

Your feedback helps us improve our use case database