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Platform · InspectionAI

One connected pipeline for visual quality control.

Ingestion, annotation, segmentation, detection, deployment and monitoring — with a named human control at every stage.

Plan a pilotSee real output

01 — Modules

Four stages, each inspectable on its own.

Real model output: AnnotationReal model output: SegmentationReal model output: Detection and classificationReal model output: Deployment

01 / 04

Annotation

Raw inspection images become governed training datasets with structured reviewer QA.

  • Defect taxonomy agreed and written down first
  • Boxes, pixel masks and classes on the same image
  • Rejected labels kept, so mistakes teach the guideline

You stay in controlA reviewer approves every label before it enters a training set.

02 / 04

Segmentation

A mask says exactly which pixels — what turns a detection into a measurement.

  • Pixel masks, not just bounding boxes
  • Affected area reported in real units
  • Part isolated from background before judging

You stay in controlYou set the area and severity thresholds that turn a region into a verdict.

03 / 04

Detection and classification

Each finding gets a class from your taxonomy and a confidence.

  • A class from your list, never a generic 'anomaly'
  • Confidence printed beside every finding
  • Borderline calls routed to a reviewer

You stay in controlThe confidence floor is yours, and the review queue catches what sits near it.

04 / 04

Deployment

A model ships only after it is benchmarked against a held-out defect library.

  • Edge, in-line, on-premise or cloud
  • Every result stamped with its model version
  • Drift, throughput and trends tracked

You stay in controlYou decide when a new model replaces the one running, on evidence.

02 — How a pilot runs

From raw image to verified defect report. Stop after any stage.

  1. Capture

    Existing cameras, scanners or a folder of photographs.

    No new hardware to begin
  2. Annotate

    Reviewers build ground truth to an agreed defect taxonomy.

    Every label reviewed by a person
  3. Train

    Models learn your parts, defects and tolerances.

    Trained on your data
  4. Validate

    Benchmarked against held-out defects; a failing class is switched off.

    It earns its way onto the line
  5. Deploy

    The first weeks run alongside your inspectors.

    Shadow run before it counts
  6. Monitor

    Every reviewer correction feeds the next model.

    Better because people correct it

03 — The deal

What you provide, and what you own at the end.

You provide

Sample images shot the way they will be on the line; a person who can say what counts as a defect; a server or workstation on your network; stable lighting, which we help you check.

You own

The trained model on your hardware; every image and record in a database you control; the full, exportable audit trail; and the ability to keep running if you stop working with us.

04 — Honest timelines

What usually goes slowly. Not the software.

Finding enough bad parts

With a low defect rate, collecting rare examples is almost always the critical path.

Agreeing what counts

Inspectors disagreeing on a borderline mark has to be resolved in writing first.

Getting the lighting stable

The system corrects a great deal, but cannot invent detail the camera never captured.

What we do not claim

A pipeline is not a promise about your parts.

Every stage above runs today. What it produces on your line depends on your defects, your lighting and your data — which is why the next step is a pilot, not a number on a slide.

Talk to INSAIT

Start with the conversation, not the contract.

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