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AUTO-VIS-022 / AUTOMOTIVE AND INDUSTRIAL

Computer vision became evidence, not theater

An industrial quality program introducing computer vision into an existing inspection and disposition workflow.

Start a conversation.See the outcomes

Context

A prototype detected visible anomalies under controlled conditions, but production lighting, part variants, line speed, and human disposition created a different operating problem.

The problem beneath the brief

The model score had become the decision. There was no approved abstention state, no controlled image/evidence record, and no route for inspectors to challenge or correct a result.

7
defect classes governedaccepted failure taxonomy
3
abstention reasonshuman-review workflow
100%
production decisions version-linkedrelease trace query

Risk constraints

What could not be traded away.

  • false acceptance
  • line takt time
  • variant and lighting drift
  • inspector authority
  • evidence retention

Findings

What inspection changed.

  • confidence thresholds hid class-specific failure
  • lighting drift looked like part drift
  • retraining data could enter without disposition provenance

Architecture

The operating system we installed.

  1. 01image capture and provenancegolden-set governance
  2. 02class-aware evaluationhuman override
  3. 03abstention and human dispositionlighting baseline
  4. 04drift monitoringmodel/version trace
  5. 05versioned model releaserollback

Delivery sequence

Four gates. No ceremonial phase changes.

  1. 01

    Frame

    Define the decision, outcome, work products, authority, dependencies, exclusions, and acceptance evidence.

    A named sponsor and principal approve the bounded charter.
  2. 02

    Inspect

    Observe the operating reality, trace systems and records, test assumptions, and rank failure modes.

    Critical unknowns have owners, evidence plans, and stop conditions.
  3. 03

    Build

    Implement the smallest coherent change with versioned decisions, controls, and verification attached.

    The integrated state meets the agreed evidence threshold.
  4. 04

    Transfer

    Rehearse recovery, resolve exceptions, accept the work, remove temporary access, and transfer operating ownership.

    The receiving owner signs the handoff with open limits visible.

Complications

Where the plan had to become more honest.

  • The best aggregate model was worse on the rare defect class that mattered most.
  • A line-speed improvement reduced image quality below the accepted baseline.

Outcomes

What changed—and what the record proves.

  • The system abstained rather than forcing a low-evidence classification.
  • Inspectors retained disposition authority and every correction fed a governed review queue.
  • Release evidence tied model, camera, lighting, line, and part configuration together.

Lessons

What we would carry into the next system.

  • Aggregate accuracy can hide the risk class that matters.
  • A vision system includes lighting, capture, workflow, and review.
  • Human correction is an operating control, not model embarrassment.

Handoff

The engagement ended with an operating owner.

  1. 01golden-set owner
  2. 02drift review cadence
  3. 03inspector escalation
  4. 04version rollback
  5. 05correction and retraining governance

Start with the decision

Bring the priority. We will help bound the work.

If the decisions or constraints look familiar, start with the operating reality—not a preselected solution.

Start a conversation.