RAG · AI Agent · E-commerce · Production

Web Sales Assistant — AI RAG Agent

Production AI Sales Assistant for a Polish wallpaper and décor e-commerce store. A LangGraph ReAct agent with Hybrid RAG architecture — SQL for hard facts, pgvector for semantic knowledge. Zero hallucinated prices, 1,200+ SKU catalog, ~90% API cost reduction. Built and deployed in production — not a demo.

~90% API cost reduction vs. hot-cache approach through architectural design
1,200+ SKU Zero hallucinated prices — hard facts in SQL, not vector search
5 tools LangGraph ReAct agent: search, filter, detail, recommend, FAQ
98% Data optimisation through ETL pipeline and knowledge-base contract tests
Architecture

How it works

  • Hybrid RAG: SQL for deterministic facts (price, SKU, dimensions), pgvector for semantic knowledge (style, mood, recommendations).
  • LangGraph ReAct agent — tool-calling loop with fallback handling and observability via LangSmith.
  • FastAPI backend + web chat widget (/chat endpoint). Telegram used for internal debugging only.
  • ETL pipeline with deduplication and knowledge-base contract tests to prevent data drift.
  • Production-deployed on Railway — auto-scaling, zero-downtime.
Business impact

What it replaced

  • Handles product search, filtering, detail queries, recommendations, and FAQ without human intervention.
  • Buyer-scenario integration tests ensure the agent behaves correctly under edge cases.
  • ~90% reduction in API costs compared to naive LLM-for-every-query approach.
  • Sales team gets a working AI assistant that answers complex product questions instantly.
  • Scales to any catalogue size — not limited by context window.
Engineering decisions

Why this approach

  • Split SQL vs. vector intentionally — price hallucinations are a business risk, not a technical inconvenience.
  • ReAct agent over single-shot prompting — complex user queries often require multi-step tool use.
  • LangSmith observability from day one — not bolted on later.
  • Baseline ETL tests before embedding — garbage in, garbage out is the most common RAG failure.
Relevance for your business

Can this work for you?

  • Any e-commerce or product catalogue business in Germany with 100+ SKUs can benefit from this architecture.
  • Works for: furniture stores, electronics retailers, B2B parts suppliers, wholesale catalogs.
  • Integrates into existing websites as a chat widget — no full rebuild required.
  • Cost-effective: designed to minimise API spend from the start.
Tech stack

Tools used

Python LangGraph FastAPI Supabase pgvector Gemini 2.5 Flash Sentence-Transformers LangSmith Docker Railway pytest
Need a similar system for your business?

I build AI sales assistants and RAG agents for e-commerce and B2B companies in Germany and EU. Let's talk about your use case.

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