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Case Study

The Service Bay That Reads The Manual

850 technicians at a premium European dealer network. 40,000 pages of service manuals, TSBs, and wiring diagrams. A repair used to start with 30+ minutes of searching. Now it starts with a question.

65%
Faster diagnostics
40K+
Manual pages searchable
92%
Answers technicians use
850+
Technicians, daily

The Challenge

A premium dealer network with 850 technicians. Every repair starts the same way: identify the model year, the trim, the regional variant — then find the right procedure across 40,000 pages of manuals, TSBs, and wiring diagrams. Senior technicians knew where to look. Newer ones could spend 30+ minutes searching while a customer waited in the lounge.

Six tabs open, still no answer

Service manuals, TSBs, wiring diagrams, training materials. The information existed. Finding it was the bottleneck.

Search took longer than the repair

30+ minutes searching before a wrench was turned. The customer waited in the lounge while a technician flipped through manuals to confirm one torque spec.

Thirty years of know-how, locked in one head

Senior technicians knew where to look — and where the manual was wrong. New hires didn't. The gap showed up in repair times and first-time fix rates.

Three model years, two trims, one wrong wire

The procedure for a 2019 CX-5 2.5L isn't the procedure for a 2021 CX-30. The wrong page got applied. Comebacks happened.

The Solution

We built the search that reads the manual for them. A technician asks the question they'd ask a senior colleague — in plain language. The answer comes back as the exact procedure for the exact model, with the right diagram and the right torque spec. Behind it: 40,000 pages of documentation, indexed and trained on automotive language.

Manual reader

Ingests 40,000+ pages of PDFs, diagrams, and technical drawings. Extracts torques, part numbers, and procedures into a searchable format.

Plain-language search

Vector database trained on automotive vocabulary. A technician asks the question the way they'd ask a colleague. The system understands the question, not just the keywords.

Step-by-step answers

GPT-4 generates the response. Each answer comes with the relevant diagram, torque specs, and any TSBs for that exact model year.

Diagrams attached

Wiring diagrams, component locations, and exploded views are linked to the answer automatically. No second search.

Implementation Timeline

Phase 1

Read the manuals

3 weeks

Catalogued every service manual, TSB, and training document. 40,000+ pages, normalized into a single index.

Phase 2

Teach it the language

5 weeks

Custom embeddings trained on automotive terminology. Tuned the response model on real technician questions until it stopped sounding like a chatbot.

Phase 3

50 technicians, 5 dealerships

4 weeks

Limited release. Watched which queries failed, retrained on those edge cases, fixed them before scaling.

Phase 4

Live across the network

6 weeks

Deployed across the full dealer network. Tablet for the bay, desktop for the parts counter. 850 technicians on day one.

The Results

  • Diagnostic time dropped from 45 minutes to 16.
  • First-time fix rate up 23%.
  • New technician onboarding cut in half — they ask the system the questions they used to ask a senior.
  • Customer satisfaction up 31%. Lounge wait times collapsed.
  • Calls to the technical hotline dropped 45%. Most questions don't reach a human anymore.

"Before this system, I'd spend 20 minutes flipping through manuals for a single repair procedure. Now I ask a question and get the exact steps, diagrams, and torque specs in seconds. It's like having 30 years of experience on demand."

Rolf Kettner Master Technician, Fahrwerk Motorentechnik

Technologies Used

Python LangChain OpenAI GPT-4 Pinecone FastAPI React

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