Case study
A RAG diagnostic platform for 50,000+ mechanics — shipped to production
CTO · AHEAD Automotive · in production · 2M+ queries/month
The challenge
Mechanics need diagnostic answers while the vehicle is on the lift — and the right answer is rarely in one obvious place. Getting reliable answers to a user base of this size is not a documentation problem; it is a systems problem. That is what AHEAD Automotive set out to solve.
What I built
As CTO I built a retrieval-augmented generation (RAG) platform: instead of generating answers from a model’s memory alone, the system retrieves the relevant source material first and grounds every answer in it. It shipped to production and serves 50,000+ mechanics.
The result
The platform runs in production: 50,000+ mechanics use it, processing 2M+ queries per month. Production traffic at that scale is the evidence that matters — not a demo, a system that survives contact with real users.
For the Head of IT
- Approach: retrieval-augmented generation (RAG) — answers grounded in retrieved source material, not free generation.
- Production evidence: 50,000+ mechanics as users; 2M+ queries per month.
- Status: in production.
Want this kind of proof in your company? Start with the AI proof of concept package, or get in touch →