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# From Diagnostic Code to Repair Guidance
- URL: https://www.allinondata.com/from-diagnostic-code-to-repair-guidance/
- Published: 2026-06-01T21:34:55.000Z
- Updated: 2026-06-11T21:00:24.000Z
- Description: Generative AI for Consumer Automotive Diagnostics
- Author: Peter Memon
- Tags: Case Studies

## **The Engagement**

All In On Data partnered with a US-based automotive diagnostics manufacturer to assess whether generative AI could power the next generation of their consumer-facing diagnostic product line. The client's OBD scanners produce industry-standard diagnostic trouble codes, but converting a raw code into clear, trustworthy, step-by-step repair guidance — calibrated to the user's mechanical experience level — remained a significant unsolved product problem.

The engagement paired a structured AI Discovery phase with an executed Proof of Concept and ran over approximately twelve weeks. Discovery inventoried candidate AI use cases across the client's product roadmap, plotted each on a benefit-versus-attainability matrix, and identified the technical proof points that would need to be confirmed before any production commitment. Those proof points defined the POC scope.

## **The Approach**

The Proof of Concept focused on the most consequential question: can a language-model system reliably synthesize vehicle-specific diagnostic and repair instructions from heterogeneous public content? We designed and built a multi-stage agentic pipeline that retrieves relevant material from sources including video transcripts and archived technical documentation, extracts jargon and key terms per source, resolves synonymy across sources, and synthesizes a single cited instruction set adjusted to the user's stated experience level. The system was deployed to a working demo environment with an embedded feedback mechanism, so client stakeholders could mark up generated content and inform iterative refinement.

## **Findings and Path Forward**

Structured testing confirmed the core proof points: useful novice, intermediate, and advanced diagnostic and repair instructions could be generated reliably enough to support productization. The work also surfaced the constraints — generation latency, output variability across runs, and occasional loss of small-but-important details — that would have to be addressed in a production build.

Our final recommendations laid out a three-phase development plan: an agentic data architecture on AWS suited to the breadth of source formats expected; the fine-tuning of a smaller open-weight model to achieve foundation-model-level performance at materially lower inference cost and latency; and a structured feedback program combining AI self-critique, community input, and internal human-in-the-loop evaluation. We additionally recommended building a dedicated benchmark of roughly 150–200 client-validated request/instruction pairs to anchor performance measurement as the platform matures.

> The engagement delivered both a working artifact and a defensible roadmap — a concrete demonstration that the capability is achievable, paired with the architectural and data-strategy decisions needed to take it from prototype to production.