Your line already knows what good looks like. Now your inspection system does too.
Semantic understanding, not pixel matching.
The system identifies screws, capacitors, indicator lights, connectors, buttons, labels — by understanding what they ARE, not by comparing pixel grids. Show it one good unit, confirm the result, and the line is live. No code, no threshold files, no vision engineer on call.
Visual Inspection AI Agent overlay on a control module PCB, with semantic highlights on components, labels, and text
Three advantages that survive the procurement conversation.
We understand components, not pixels
The system recognizes screws, capacitors, LEDs, connectors, buttons, enclosures, and labels as discrete component types — so it adapts to lighting shifts and positional variance that break pixel-grid comparisons.
Every number has a test method
We publish how we measured, not just what we measured. Each metric on this page discloses its methodology, baseline, and conditions so your engineering team can verify it independently.
Your PLC gets the answer in real time
Trigger via PLC or sensor, AI analysis runs inline, and the OK/NG decision feeds back through structured output (JSON/CSV) before the operator lifts their hand. MES, QMS, and upstream line control all receive the result.
Measured end-to-end on a named customer deployment: time from blank line to first stable OK/NG on a new product variant, compared to the same line's prior machine-vision setup.
Measurement method: Stopwatch measurement of full registration-to-production cycle. Baseline is the same customer's prior setup on the identical line. Full methodology report available under NDA.
Traditional inspection breaks in modern manufacturing.
Shorter product cycles, more variants, and tighter takt times have outgrown both human inspectors and rule-based machine vision.
Manual inspection is inconsistent.
Fatigue, operator variation, and subjective thresholds make human visual inspection unreliable and hard to scale.
Rule-based machine vision is brittle.
Every new product, new variant, or new lighting condition triggers weeks of re-coding and parameter tuning by a scarce specialist.
Lighting and material drift degrades stability.
Small environmental changes cause cascading false positives on rule-based systems, eroding the operator trust that took months to build.
Product changeovers kill throughput.
On mixed-model lines, the inspection system is often the bottleneck for launching new SKUs — not the line itself.
Three steps. No code. Minutes to deploy.
Photo, auto-analyze, confirm — a closed-loop engineering workflow with measured results at each gate. No pixel-level template, no threshold tuning, no script to write.
- 01
Shoot
Use an industrial camera or a standard smartphone to capture an OK sample of the product. No rigid fixtures, no calibration dance.
- 02
Auto-analyze
The AI Agent recognizes the product structure, text, and key components — screws, capacitors, indicator lights, connectors, buttons, labels — automatically. It builds a semantic baseline, not a pixel template.
- 03
Confirm
A single operator clicks through a visual interface once. The system generates a standard inspection model, ready for the line. Engineering closure — you see exactly what the system learned before it goes live.
3-step workflow diagram: camera, brain icon, checkmark — isometric illustration
No scripts, no thresholds, no dedicated vision engineer. Line engineers run the workflow end-to-end.
What the system actually understands.
Not "registration" and "detection" in the abstract — here is what the system specifically identifies, checks, and reports on every triggered cycle.
Component-level semantic recognition
The system learns what each component IS — not what its pixels look like. From a handful of qualified samples, it builds a semantic map of the product.
- Identifies screws, nuts, resistors, capacitors, LEDs, connectors, buttons, enclosures, and labels as distinct component types
- Single-click operator confirmation through a visual interface
- Automatic baseline model generation — zero scripting, zero threshold tuning
- Model changeover in minutes, not days
Real-time defect reasoning on the line
Triggered by PLC or sensor, the agent captures, reasons about components and text, and feeds the OK/NG decision back into the line in milliseconds.
- Text verification: missing, wrong, misplaced, or inconsistent content — OCR plus semantic cross-check
- Component verification: missing / wrong / mispositioned screws, capacitors, connectors, labels
- Assembly completeness and cosmetic conformance across the full product structure
- Structured output: OK/NG + annotated process image + JSON / CSV logs pushed to MES/QMS
Product registration UI showing an OK sample with auto-detected component regions
Real-time detection dashboard showing OK/NG results and annotated inspection images
Six differences that matter on the production floor.
Zero programming
Shoot, confirm, ship. Line engineers run the whole workflow without a machine-vision specialist in the loop.
Semantic understanding of real components
The system reasons about screws, capacitors, connectors, and labels as component types — so it survives the lighting shifts, material drift, and positional variance that real production floors produce.
Strong generalization across variants
Survives lighting changes, material drift, and minor positional shifts without recalibration. New product families inherit existing component knowledge.
Covers the traditional blind spot
Unstructured text, complex assembly verification, and multi-component cross-checks that classical OCR and template matching cannot handle.
Fast replication and rollout
Templates are sedimented per workstation and product type, so additional lines and variants inherit existing work.
Every inspection builds your quality library
Detection experience feeds back into a growing quality feature library. Templates sediment, component knowledge accumulates, and accuracy compounds over time — a data flywheel that makes the system more valuable the longer it runs.
Numbers we stand behind — with the test method for each.
We publish how we measured, not just what we measured. If a number can't survive scrutiny, it doesn't belong on this page.
| Metric | Value | Test method | Baseline |
|---|---|---|---|
| Engineering debug time | −95% | End-to-end time from blank line to first OK/NG on a new product variant | Customer's prior traditional machine-vision setup on the same line |
| Single-station throughput | +50% | Takt-time measurements post-deployment compared to pre-deployment baseline | Manual visual inspection at the same station |
| Programming lines of code | 0 | Engineer timesheet during model registration (photo + confirm) | — |
| Product changeover time | Minutes | Stopwatch measurement from new sample in hand to stable OK/NG | Traditional rule-based vision: hours to days |
Figures are from a single named customer deployment under NDA. Specific numbers vary by line, product mix, and lighting conditions. A full methodology report is available on request after NDA.
Real deployments, anonymized results.
Electronics assembly line with multiple control module variants
Control-module assembly for electronic equipment
Multi-model control module line with heavy text-content and component-placement variance. Deployed across multiple stations; measured results above.
Populated PCB with color-coded component overlay
Multi-model PCB inspection at an EMS factory
Complex PCB boards with variant-dependent BOM. The agent auto-recognizes all component placements and flags missing or misplaced parts.
Cylindrical product with label and barcode inspection overlay
Cylindrical-label integrity checking at an OEM
Validates label text, barcode, and serial number integrity on cylindrical housings — a known blind spot for classical OCR.
The OK/NG decision hits your MES before the operator lifts their hand.
Public cloud
Quickest to startFastest path to a pilot. Leverages cloud compute with no on-site hardware footprint. Ideal for POC and evaluation.
- Fast deployment for evaluation and POC
- Minimal upfront infrastructure
- Centralized model updates
Private / on-premise
Recommended for productionFull data sovereignty — images never leave the factory network. Low-latency inference runs on local hardware with no external dependency.
- Images never leave the customer network
- Low-latency inference for real-time PLC-triggered decisions
- Meets strict compliance and confidentiality requirements
Trigger via PLC or sensor → AI analysis inline → real-time OK/NG feedback → structured output to MES/QMS. All images and detection data can be processed on-site in a closed loop inside the customer network.
Your images, your perimeter.
Local closed loop
All images and inspection data can be kept on local servers — nothing leaves the factory boundary.
Physical isolation
Per-project and per-line data isolation, with audit logs and role-based access control.
Compliance-ready
Designed to align with internal information security standards. ISO 27001 and SOC 2 Type II alignment are in progress — we never claim "certified" until there is a certificate to show.
Clear ownership
Data ownership and usage rights are contractually defined. NDAs and confidentiality agreements are signed up front.
Questions procurement and engineering actually ask.
How do I verify your accuracy claims?
What happens to my image data?
Can this connect to our Siemens / Rockwell PLC?
How many products can be registered per site?
What is your company scale?
Ship a new inspection model in minutes, not weeks.
Book a 30-minute demo with the engineering team. We will walk through your line, your defect taxonomy, and a realistic POC plan — and show you exactly how the numbers on this page were measured.