Operations

Don’t take our word for it. Try it.

Quality inspection is a decision made hundreds of times an hour on the line — pass or pull. The two models below make that decision in production. Test them yourself, right here, with our samples or your own images. No signup, no sales call first.

Go to the demos

01 — Live demo

Quality inspection

A YOLOv8 model trained to catch scratches, bends, and surface damage on machined parts. It classifies each part PASS or DEFECTIVE and shows you what it saw. Run a sample or upload your own part.

Screw Defect Detection — Custom YOLOv8 Model
This model is trained on a screw dataset to detect manufacturing defects — scratches, bends, and surface damage. It classifies each screw as PASS or DEFECTIVE and highlights the specific defect type.
Demo runs on CPU — inference may take 10–20 seconds. Production deployments run on GPU.

02 — Live demo

PPE compliance

An RF-DETR model that reads a workplace photo and identifies who is wearing a hard hat and reflective vest — and who is not. Run a sample or upload a photo from your own floor.

PPE Safety Compliance — Custom RF-DETR Model
This model detects hard hats and reflective vests in workplace photos. It identifies workers wearing proper PPE and flags those without — enabling automated safety compliance monitoring.
Demo runs on CPU — inference may take 10–20 seconds. Production deployments run on GPU.
92%+
Detection accuracy — verify it above
2
Production models running on this page
0
Signups required to try them

03 — Calibration

Where these decisions sit.

The models you just ran live in the first lane: the agent decides, every call is logged, and your team audits. Not every floor decision belongs there. This is how we would draw the lanes on a production line.

Flagging a defective unit off the line
The decision you just tested above. Every call logged with the frame that made it; your team audits.
Delegate
Reorder point per part, per line
Recomputed as consumption shifts, inside guardrails, every change auditable.
Delegate
Batch release after a defect spike
The agent assembles the evidence — defect rate, affected units, probable cause. The quality head signs the release.
Surface
Pulling maintenance forward on a drifting machine
Prepared case with sensor history and cost of waiting. The plant engineer decides.
Surface
Stopping the line
Too costly and too contextual to automate. The agent briefs; a person decides.
Hold
A worked example for a production line. Your floor gets its own ledger — that is what the diagnostic produces.

04 — Beyond inspection

The same discipline, elsewhere on the floor.

Demand forecasting. Models trained on your orders, seasonality, and lead times decide how much to make and when to make it.

Yield optimization. Process models read your production parameters and point to the input combinations that raise output quality and cut waste.

Predictive maintenance. Sensor models watch equipment drift and call the intervention before the breakdown, not after.

Every one of them starts the same way: with the operation, not the model — then the autonomy gets calibrated, decision by decision.

Further reading

BlogAI Quality Control in Manufacturing: A Complete GuideHow vision AI detects defects faster and more consistently than human inspectors on the production line.GlossaryWhat Is Predictive Maintenance AI?Predict equipment failures before they happen — reduce downtime and extend asset life.GlossaryWhat Is AI Supply Chain Optimization?How AI transforms demand forecasting, inventory planning, and logistics operations.BlogAI Inventory Management for Retail: Cut Stockouts by 65%ML-driven demand forecasting and automated replenishment that actually works in production.

Contact

Bring us one operation.

Tell us which decisions eat your team’s week. We’ll tell you, in writing, where AI earns its place there — and where it doesn’t. You’ll hear back within a day.

Prefer to talk? Book 30 minutes directly.