Sub-second defect detection on the line — with a governance trail.

AI for Visual Quality Inspection

T7 builds computer-vision inspection systems that catch defects at line speed, learn from every escape, and give quality leaders a trail auditors trust.

The problem

Manual visual inspection misses subtle defects, fatigues over a shift and can't keep up with new product variants.

Off-the-shelf vision APIs don't run at line speed, don't handle your defect classes and can't be audited.

How T7 solves it

Custom-trained vision models per defect class, deployed to edge devices for sub-second inference next to the line.

A closed-loop labelling console lets QA leads confirm or correct edge decisions — retraining happens weekly on real production data.

Every decision is logged with image, model version, confidence and reviewer — a governance trail that survives audit.

The workflow

Step 1

Capture & label

Line cameras stream to an edge box; QA labels seed the first model per defect class.

Step 2

Detect & act

Sub-second inference triggers a reject signal to the PLC or a review flag on the HMI.

Step 3

Improve & audit

Weekly retraining on confirmed escapes; every decision retained with full lineage for auditors.

Outcomes we ship for

-64%
Escape rate on target defect class
<300ms
Inference latency at the edge
3x
Inspection coverage vs. manual

The stack

PyTorch / YOLO / segmentation modelsNVIDIA Jetson / Intel edgeMLflow / DVCOPC-UA / MQTT to PLCGrafana line dashboards

Frequently asked questions

How much labelled data do you need?

For a well-defined defect class, we can seed a first model with 500–1,500 labelled images and reach production accuracy within 6–8 weeks of line feedback.

Can this run offline?

Yes — inference runs on-edge with no cloud dependency at the line. Training and dashboards sync when connectivity is available.

Ready to Build Your AI Product?

Talk to a senior AI consultant from T7 about your industry, workflow, or product idea. Free, no commitment — reply within one business day.

  • · AI feasibility & architecture review
  • · Product / MVP roadmap
  • · Integration & automation strategy