CDAAugmented document knowledge
Automotive industry · Quality division
How an automotive supplier certified its vendor compliance.
Caillau had to extract requirements from OEM specifications, match them to internal procedures and produce a defensible file — without nested “drawer” omissions.
Key metrics
- Client
- Caillau
- Sector
- Automotive industry
- Function
- Quality division
- Status
- Acceptance testing
- Deployment
- On-premise (containers)
The context
Automotive supplier working with many OEMs. First AI project in the group: systematically analyse customer specifications, standards and procedures.
The problem
- Estimated €150,000 a year of manual analysis, with no guarantee of full coverage.
- Long, multilingual documents with nested requirements. A “drawer” omission is a non-compliance risk.
- Judgements were undocumented: facing an auditor, the answer was a memory, not a file.
The solution
Seven-step pipeline: multi-format parsing, requirements extraction (including nested references), procedure indexing, semantic matching, scoring, justification comments, HITL validation before export.
NEXA components in scope
| Component | Role |
|---|---|
| Arbitration algorithm | RAG + keywords + heuristics |
| Evidence Panel | Score, matched sources, lineage per requirement |
| HITL | Quality validation before export |
| AI Knowledge Vault | Reusable compliance patterns |
| Audit Pack | Standardised export for auditors |
The impact
| Axis | Concrete impact |
|---|---|
| Compliance | Systematic requirements analysis; fewer omissions. |
| Productivity | Reduction of the annual manual-analysis cost (est. €150,000). |
| Quality | Client score estimated 80/100 from the first demonstration. |
CDAAugmented document knowledge
« Estimated score 80/100 from the demo. Confidence to converge in a few more iterations. »
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