AI Assistant & E-commerce RAG

AI agents that know your catalog, your customers and your e-commerce business

Overwhelmed customer support, approximate product search, untapped e-commerce data. We build RAG systems that connect your AI agents to all your data — PIM catalog, customer history, support tickets, browsing behavior. Result: agents that respond with your data, cite your product pages, and continuously improve.

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The challenge

Why AI is becoming essential in e-commerce — and why traditional chatbots fail

Your competitors are investing heavily in AI. But deploying a chatbot without grounding it in your e-commerce data is wasting your investment:

AI chatbot that fabricates answers about your products — wrong prices, outdated stock, made-up information
Customer support costs rising steadily (+15 to 25% per year) with no reliable automation solution
Keyword-based site search: 30% of visitors leave after a search with no relevant results
E-commerce data in silos: PIM catalog, support tickets, customer reviews — never connected to AI
Product pages written manually — 2h per page, zero standardization, approximate SEO
Zero personalization: same recommendations for a first-time visitor and a loyal customer
Untapped behavioral data: search logs, browsing journeys, cart abandonment
Competitors already deploying RAG agents with semantic search and sourced AI support
Architecture

Technical overview

IA support client — RAG & agents

Pipeline RAG avec détection d'intention, génération augmentée et actions automatisées

Sources de connaissances
Pipeline RAG
Actions automatisées
Sortie
Gouvernance
Question utilisateur
Détection d'intentionClassification NLU
Retrieval (RAG)Recherche vectorielle
FAQ
Documentation
Runbooks
Contexte + sourcesPrompt augmenté
Génération réponseLLM (GPT, Claude)
Décision d'actionAgent
Création ticketJira, Zendesk
Mise à jour CRMSalesforce, HubSpot
Escalade helpdesk
Validation / Garde-fousFiltrage, conformité
RéponseAvec citations
Contrôle d'accès
Évaluation qualité
Logs & audit
Source
Traitement
Stockage
Sortie
Couche
Solution comparison

Which AI technology for your e-commerce?

The choice of model and infrastructure depends on your data, volumes and confidentiality constraints. We design the most suitable architecture for your e-commerce stack.

OpenAI / GPT

Strengths
  • Best generalist models (GPT-4o, o3) for e-commerce content generation
  • Performant text-embedding-3 embeddings for semantic product search
  • Native function calling for e-commerce API integration (stock, pricing, availability)
  • Multimodal: product image analysis, automatic description from visuals
Limitations
  • High costs at scale (input/output tokens x e-commerce volume)
  • Catalog data sent to OpenAI servers (except Azure OpenAI)
  • Strong vendor lock-in on proprietary APIs
  • Variable latency — problematic for real-time on-site search
Ideal for: Advanced e-commerce assistants, product page generation, multimodal support chatbots

Claude / Anthropic

Strengths
  • 200K token context window — ideal for ingesting an entire product catalog in context
  • Excellent at catalog analysis, product attribute reasoning and comparisons
  • Low hallucination rate — essential for prices, stock and terms of sale
  • Nuanced and natural responses for complex customer support (returns, disputes)
Limitations
  • More restricted ecosystem than OpenAI for e-commerce integrations
  • No public fine-tuning — prompt engineering and RAG only
  • Third-party embeddings needed for the product vector database
  • More recent e-commerce adoption (but growing fast)
Ideal for: RAG on large catalogs, complex customer support, document analysis (T&Cs, supplier contracts)

Open-Source (Mistral, LLaMA, Qwen)

Strengths
  • Full control: catalog and customer data stays in your infrastructure
  • Fine-tuning possible on your product data for specialized recommendations
  • No vendor lock-in, private VPC or on-premise deployment
  • Controlled costs at scale — crucial for high-volume search queries
Limitations
  • Requires ML/MLOps expertise and GPU infrastructure (A100+)
  • Lower performance on some complex tasks vs proprietary models
  • E-commerce fine-tuning: requires quality annotated data
  • Model maintenance and updates are your responsibility
Ideal for: Sensitive luxury data, high search volumes, customer data sovereignty required

Cloud AI (AWS Bedrock, GCP Vertex)

Strengths
  • Integrated with existing cloud e-commerce infrastructure (AWS/GCP)
  • Managed services: automatic scaling during peaks (sales, Black Friday)
  • Enterprise compliance (SOC2, PCI DSS, GDPR) — essential for transactional data
  • Multi-model access: test Claude, Mistral and Titan via a unified API
Limitations
  • Strong coupling with the chosen cloud ecosystem
  • Inference costs sometimes higher than direct APIs
  • Available models lagging behind direct releases
  • Complex configuration (IAM, VPC endpoints, KMS for PCI data)
Ideal for: E-commerce already on AWS/GCP, PCI DSS requirements, seasonal traffic peaks

No technology dogma. We recommend the solution best suited to your context, constraints and ambitions. Every choice is documented and justified.

Our methodology

End-to-end support, phase by phase

Each phase produces concrete deliverables. You maintain visibility and control at every step.

01 E-commerce Data Audit & AI Use Cases
02 Data Pipeline & RAG Knowledge Base
03 AI Agent POC & Semantic Search
04 Industrialization & SI Integrations
05 Data Science, Optimization & Growth
01 2 to 3 weeks

E-commerce Data Audit & AI Use Cases

Map all your e-commerce data exploitable by AI: product catalog, customer data, transactions, browsing behavior, support tickets, reviews. Identify the highest-ROI use cases and audit data quality.
Deliverables
  • Complete mapping of e-commerce data sources (PIM, CMS, ERP, CRM, OMS, ticketing)
  • Catalog data quality audit: attribute completeness, descriptions, images, metadata
  • Behavioral data analysis: search logs, browsing journeys, cart abandonment
  • Support corpus evaluation: tickets, FAQ, T&Cs, return policies, product guides
  • Prioritized AI use case catalog (AI support, semantic search, recommendations, content, categorization)
  • Scoring by business impact (resolution rate, conversion, productivity) and technical feasibility
  • E-commerce AI solutions benchmark and technology stack recommendation
  • Macro e-commerce AI roadmap with quick wins and structural initiatives
  • Budget estimate and projected ROI per use case
02 3 to 4 weeks

Data Pipeline & RAG Knowledge Base

Build the data pipeline that feeds the e-commerce RAG system. Extract, clean, enrich and vectorize your product, support and customer data. This is the data science foundation without which no AI agent can respond with relevance.
Deliverables
  • ETL pipeline connected to e-commerce sources (PIM, CMS, ERP, OMS) via API/webhooks
  • Product catalog extraction and structuring (attributes, descriptions, variants, pricing, stock)
  • Data cleaning and enrichment: deduplication, normalization, missing attribute completion
  • Chunking strategy optimized by content type (product pages, FAQ, T&Cs, guides)
  • Vector embedding generation on the full corpus (products + support + documentation)
  • E-commerce vector database setup (Pinecone, Weaviate, or pgvector)
  • Real-time sync pipeline: new product -> automatic indexation
  • Behavioral data indexation: popular search queries, product associations
  • Data quality metrics: catalog coverage, freshness, price/stock consistency
03 4 to 6 weeks

AI Agent POC & Semantic Search

Prototype the e-commerce AI agent with a complete RAG pipeline: semantic product search, customer support assistant, and contextual recommendations. Test with real customers and real data to validate value before industrialization.
Deliverables
  • RAG conversational agent connected to product catalog and support base
  • Semantic product search: intent understanding, synonyms, natural language
  • Hybrid retrieval pipeline: vector (semantic) + BM25 (lexical) + filters (price, size, availability)
  • Result reranking by contextual relevance (cross-encoder on e-commerce data)
  • Response personalization by customer profile (purchase history, preferences, segment)
  • Source citation mechanism: each recommendation links back to the product page
  • Real-time integration with stock, pricing and availability via e-commerce API
  • Evaluation framework: search relevance, support resolution rate, customer satisfaction
  • Testing with real customers and business teams on representative e-commerce scenarios
  • Go/no-go with measured metrics and validated ROI
04 2 to 4 months

Industrialization & SI Integrations

Deploy to production with full integration into your e-commerce ecosystem. Scaling for traffic peaks (sales, Black Friday), CMS/PIM/ERP/CRM integrations, specialized agents per channel (site, email, chat, social media).

-40%coûts cloud
Deliverables
  • Scalable production architecture (autoscaling, semantic cache, high availability)
  • E-commerce site integration: chat widget, AI search bar, augmented results page
  • Integration with existing systems (CMS, PIM, ERP, CRM, OMS, ticketing)
  • Specialized AI agents per channel: website, email, live chat, WhatsApp, social media
  • Multi-agent orchestration: L1 support routing (AI) -> L2 (human) with intelligent escalation
  • Automatic product page generation, translations and SEO descriptions via AI
  • Automatic categorization and tagging of new products
  • Production AI monitoring: response quality, latency, cost per query, conversion
  • Load testing and performance validation for seasonal peaks
  • Training for support, merchandising and content teams on AI tools
  • Complete technical and operational documentation
05 Ongoing

Data Science, Optimization & Growth

Leverage data science to continuously optimize the performance of your e-commerce AI agents. Search query analysis, retrieval optimization, fine-tuning on your data, continuous AI catalog enrichment, and deployment of new use cases.

S1S2S3S4S5
Deliverables
  • Data science analysis of search queries: zero-result terms, popular queries, trends
  • Support conversation clustering to identify knowledge gaps
  • Continuous retrieval optimization: chunking, embeddings, reranking adjustment per product vertical
  • Fine-tuning or model distillation on your product data and customer conversations
  • A/B testing of response and recommendation strategies
  • Continuous RAG base enrichment (new products, new FAQs, customer feedback)
  • E-commerce AI dashboard: resolution rate, assisted conversion, satisfaction, cost per interaction
  • Inference cost optimization (semantic cache, model routing, batching)
  • Progressive deployment of new use cases (personalized recommendations, AI email, assisted merchandising)
  • Monthly reporting (business KPIs + AI quality) and quarterly strategy review
Business value

What you concretely gain

Expected results

Support costs reduced by 40 to 60%

Semantic search that understands intent

Contextual personalization at scale

Support costs reduced by 40 to 60%

A RAG agent trained on your data resolves 60 to 80% of L1 requests without human intervention — with sourced answers from your catalog, FAQ and T&Cs. Your human agents focus on complex cases.

Semantic search that understands intent

No more approximate keyword searches. RAG understands purchase intent and returns the right products even with vague queries, synonyms or natural language. Search conversion rate +15 to 25%.

Contextual personalization at scale

Recommendations, support responses, product suggestions — the agent adapts to the customer profile (purchase history, segment, preferences) in real time. Every interaction is personalized through behavioral data.

Content team productivity x3

Automatic product page generation, translations, SEO descriptions, categorization and tagging — AI-assisted and controlled by your teams. Average saving of 2h per product page.

E-commerce data finally leveraged

Search logs, browsing behavior, customer reviews, support tickets, transactional data — the data pipeline transforms your dormant data into actionable intelligence for merchandising and product.

Lasting competitive advantage

A RAG system trained on your e-commerce data creates a competitive moat impossible to replicate. The more the agent interacts with your customers, the more data science improves it — it's a cumulative effect.

Client references

They trusted us with this type of engagement

Kering — Boucheron

AI product knowledge base for retail and e-commerce teams. RAG pipeline on the high jewelry catalog, semantic search on the product universe, AI agents contextualized by market (WW, APAC).

Groupe Bayard

AI applied to editorial and e-commerce content. Data pipeline on the press catalog, automatic AI categorization, metadata enrichment, personalized reading recommendation system by reader profile.

Frequently asked questions

Your questions, our answers

01 What are the most profitable AI use cases in e-commerce?
The three highest-ROI use cases are: 1) RAG customer support agent (40-60% reduction in L1 support costs, sourced answers from your catalog and FAQ), 2) Semantic product search (15 to 25% increase in search conversion rate through intent understanding), and 3) Automatic product page generation (3x productivity gain for content teams). We always start with the use case offering the best impact/effort ratio in your context.
02 How does RAG concretely leverage my e-commerce data?
The data pipeline connects to all your sources: PIM (product attributes, descriptions, images), ERP (stock, pricing), CRM (customer history, segments), OMS (orders, returns), ticketing (FAQ, past resolutions), CMS (editorial content), and navigation logs (search queries, journeys). Each data type is cleaned, enriched, chunked (optimized chunking), vectorized (embeddings), and indexed in a vector database. The RAG agent queries this data in real time for each customer interaction.
03 How does the AI agent handle real-time pricing, stock and availability?
The agent uses function calling to query your e-commerce APIs in real time (stock, pricing, availability, delivery times). Static data (descriptions, attributes, FAQ) comes from the vector RAG. Dynamic data (stock, pricing) is always fetched live via API. Result: the agent never gives an outdated price or an out-of-stock product.
04 Our data is sensitive (luxury, customer data). How do you protect confidentiality?
Several architectures based on your requirements: open-source models deployed in your private VPC (no data leaves), Azure OpenAI or AWS Bedrock with DPA and non-retention guarantees, self-hosted vector database with encryption, or hybrid architecture. For luxury, we often deploy on private infrastructure with models fine-tuned on your data — complete sovereignty. The strategy is defined from the scoping phase based on your GDPR and PCI DSS constraints.
05 How much does an e-commerce RAG AI project cost?
Data audit and use case scoping costs between 15K and 30K EUR (3-4 weeks). Data pipeline + RAG POC with conversational agent ranges from 25K to 50K EUR (6-10 weeks). Full industrialization with SI integrations and multi-channel agents represents 80K to 200K EUR depending on complexity. Run costs (inference, vector database, infrastructure) vary from 500 to 8,000 EUR/month based on query volume. We always provide a detailed TCO estimate with expected ROI.
06 How does the agent continuously improve after launch?
The data science loop is at the core of the system. We analyze conversations (zero-result queries, dissatisfaction, escalations), identify knowledge gaps through clustering, enrich the RAG base with new data (products, FAQs, customer feedback), optimize retrieval (chunking, embeddings, reranking), and fine-tune models if needed. Continuous A/B testing of response strategies. The agent becomes more performant every month thanks to your usage data.
07 How does AI integrate with our existing e-commerce stack?
AI integrates via REST/GraphQL API with your platform (Shopify, SFCC, Magento, Sylius, commercetools), your PIM, your CRM and your OMS. We deploy an independent AI orchestration service that communicates with your existing ecosystem via dedicated connectors. The data pipeline synchronizes in real time or batch depending on sources. Integration is progressive and non-intrusive — your site continues to operate throughout the deployment.

Ready to transform your e-commerce data into high-performing AI agents?

Free 30-minute AI diagnostic. We map your e-commerce data, identify your priority use cases and estimate the ROI of a RAG system — no commitment.

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