---
title: "AI technical sales advisor for a B2B online store | Red Moon Consulting"
description: "An agent that finds products, checks live price and stock, calculates materials and drafts quotes, citing the data sheet behind every figure."
url: https://www.redmoonconsulting.es/en/case-studies/ai-sales-advisor/
lang: en
---

[Back to work](/en/case-studies/)

# AI technical sales advisor for a B2B online store

An agent that finds products, checks live price and stock, calculates materials and drafts quotes, citing the data sheet behind every figure.

Industry

Flooring and wall panel distribution

Engagement

Direct client

My role

Agent architecture and development

Stack

Python, FastAPI, LangGraph, Claude, PostgreSQL, pgvector, Next.js

## The problem

The client wanted their online store to answer the questions homeowners, installers and architects ask before buying: which product works in a bathroom, how many boxes cover 34 m², which certifications a panel has. And to do it without making up product codes or prices.

## What I decided

* **A fixed LangGraph graph instead of an autonomous agent.** The sales process is known in advance, so a graph is more predictable and cheaper.
* **Code does the maths.** The model interprets the question and writes the answer, but boxes, square metres and wastage come from deterministic functions.
* **A validator checks every answer before it goes out.** If it mentions a product code, a figure or a claim that isn’t in the catalogue or the documentation, the answer is discarded and a template reply is used.
* **Two models:** a fast one to understand the question and a stronger one to write the answer.

## What I built

* Hybrid RAG over data sheets and certificates, with verbatim quotes and page numbers.
* A read-only adapter on the store’s database for live price and stock.
* Draft carts and quotes, plus a handoff to the sales team when needed.
* Per-turn traces of latency, tokens and cost, with personal data redacted, and a monthly spending cap.

## Result

On a suite of 42 real cases over the catalogue:

| Metric               | Result         |
| -------------------- | -------------- |
| Cases passed         | 39 of 42 (93%) |
| Product accuracy     | 96%            |
| Verbatim citations   | 100%           |
| Exact calculations   | 100%           |
| Median / p95 latency | 4.2 s / 8.4 s  |
| Cost per answer      | $0.008         |

## Tell me about your project

A 30-minute call, no strings attached. If I'm not the right fit, I'll tell you.

[Book a call](/en/contact/)

Or email me at [hola@redmoonconsulting.es](mailto:hola@redmoonconsulting.es)