Insightek vs rule-based AOI
Rule-based AOI can run 100% and it is fast — but threshold rules over-kill on complex backgrounds, and they cannot separate normal saw marks or texture from real defects. Insightek adds dedicated models, micron metrology, and a traceable data loop. This comparison reflects general differences of typical configurations, not any specific vendor; actual capability is subject to on-site validation.
Rule-based AOI
Threshold-and-rule inspection pipelines tuned per product and lighting — fast, but sensitive to complex textures and unable to separate real defects from normal saw marks or texture.
Insightek
AI inspection and metrology on one platform: dedicated models for small defects and complex textures, real-vs-false verdicts with sample-set acceptance, and micron metrology in the same output.
Where rule-based AOI still fits — and coexists
AOI is not the enemy. On clean, stable, high-contrast features it is fast and effective, and Insightek is designed to coexist with it — adding metrology, real-vs-false verdicts, and a data loop to offset AOI overkill rather than ripping the line out.
- Clean, high-contrast features on stable backgrounds where threshold rules already pass
- A frozen product and lighting condition that rarely changes
- Lines where AOI already runs and you want to add — not replace — metrology and real-vs-false verdicts
- Simple presence / absence checks that do not need micron metrology
Where Insightek changes the cost curve
These are the conditions where threshold rules run into overkill, escapes, or a "we cannot quantify that" answer.
- Complex or textured backgrounds where threshold rules over-kill
- Normal saw marks or texture that AOI flags as defects (false calls)
- Small or low-contrast defects that need dedicated models, not thresholds
- Micron metrology required on the same part, not just pass/fail
- A traceable data loop — coordinates, size, class, trend — into MES / SPC
Capability comparison
Five dimensions drawn from typical configurations. This reflects general differences, not any specific vendor; actual capability is subject to on-site validation on your samples.
Coverage & detection
Coverage
100% possible, but speed-limited
In-line 100%; speed scales with compute
Tiny / low-contrast defects
Threshold rules; high overkill on complex backgrounds
Dedicated models tuned for small defects and complex textures
Judgment & metrology
Real-vs-false discrimination
Cannot separate normal saw marks / texture
AI verdicts with sample-set acceptance, consistent
Metrology capability
Partial, limited accuracy
Inspection plus micron metrology in one output
Data & traceability
Data loop
Results output only
Coordinates, size, class, trend + MES / SPC link
Adding to — or moving off — rule-based AOI
Adoption is phased, and each step has a deliverable. You can start small — software and offline validation first, an integrated unit next, standard equipment last — and coexist with AOI along the way.
- 01
1 · Sample & process review
We review your samples and process together. Deliverable: a feasibility conclusion.
- 02
2 · Imaging & algorithm validation
We validate imaging and the algorithm on your parts. Deliverable: a demo report.
- 03
3 · Inspection items & thresholds
We define inspection items and thresholds with you. Deliverable: a verdict scheme and acceptance criteria.
- 04
4 · Line-side integration
We integrate at the line. Deliverable: an integration validation report.
- 05
5 · Acceptance & replication
We run acceptance and prepare to replicate. Deliverable: an acceptance report and a maintenance plan.
Frequently asked
Do we have to remove our existing AOI?
How is this different from AOI's threshold rules?
Can you measure, not just pass/fail?
How do you prove accuracy before we commit?
Bring your hardest AOI false-call.
A technical review of where thresholds over-kill or escape on your parts. The first deliverable is a feasibility conclusion, not a quote.