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Example engagement · Manufacturing

AI Quality Reports for a Contract Manufacturer

An assistant that turns inspection data and operator notes into complete non-conformance and corrective-action reports, ready for the quality engineer's sign-off.

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70 → 20 min
Time to write each report
0
Incomplete-record findings in the next audit
-15%
Repeat non-conformances
Profile — Contract manufacturer, ISO 9001, about 250 non-conformances a monthTimeline — 6 weeks to productionTeam — 2 engineers + 1 quality advisor
Representative engagement: a composite of the AI work we deliver in this industry, not a named client's project. Figures are the outcomes this kind of build typically delivers, and they are the targets we agree and measure in a pilot before scaling.
The problem

Quality engineers spent hours writing reports by hand. Root-cause sections were thin, and audits found incomplete records.

What we build

The approach.

  • 01Connected inspection results, operator notes and batch records.
  • 02Drafted non-conformance reports in the customer-required format.
  • 03Suggested likely root causes from similar past issues.
  • 04Required a quality engineer's sign-off before anything was filed.
Where the AI works

Claude assembles the facts, drafts the report and proposes root causes ranked by similarity to past cases, for the quality engineer to confirm.

Claude APIPythonQMS APISQL ServerPower BI
Proof, not promises

How we measure it.

Report time is logged in the QMS. Audit findings come from the next external audit. Repeat issues are counted over two quarters.

Questions

Before you ask.

No. Beyond Technologies requires a quality engineer to review and sign every report.

Yes. Each customer's required format is a template the assistant fills.

Next step

Want this measured on your own data? Start with a pilot.

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AI for Manufacturing →Generative AI →Data & Cloud →Next example: AI Maintenance Copilot for a Plant Floor →Named client case studies →

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