AI Visual Quality Intelligence for Manufacturing

Pick your plant, see what the cameras already show

Lines, cells and stations. Cameras already cover the floor; what is missing is anything watching them continuously.

Runs on the estate you have. RTSP and ONVIF over the existing cameras, no rip and replace.
Zones you draw. Robotic cells, electrical rooms and restricted areas as virtual boundaries.
The clip, not the shift. Event-driven recording keeps the incident, not eight hours of nothing.

Continuous plant, few people. Long stretches where nothing happens and a short window where it matters.

Fire and smoke. Detected on camera rather than waiting on a fixed-point sensor.
PPE for the hazard. Respirators, harnesses and gloves as well as helmets and vests.
Inside the boundary. Self-hosted inference where footage cannot leave the plant.

Hygiene, handling and line speed. Compliance evidence that today is a paper record and a supervisor's memory.

Evidence per event. The incident lands as a record, not a screenshot in an email.
Zone discipline. Entry to areas that require a change of gear or a wash step.
Retention that fits audit. Lifecycle policy on the footage, not manual housekeeping.

Forklifts and people sharing floor. The proximity events nobody logs until one becomes an injury.

Forklift and pedestrian. Detection of both, and of the crossings where they meet.
Near misses surface. Recurring high-risk areas visible across weeks, not one shift.
Cross-camera. The same vehicle or person followed between cameras.

Where the record is the product. What was detected, what was changed, and who saw it, all evidentially held.

Chain of custody. Incident media held with an audit trail from the moment it is captured.
De-identified for sharing. Personal data removed on the copy that leaves; original untouched.
Air-gapped where required. Full deployment with no external calls.

What You Get

Where a describing model beats a fixed class list

Anomalies nobody defined

A multi-modal model describes what a scene shows rather than matching it against an enumerated class list, which reaches conditions no one thought to train for.

Prompt-Driven Analysis

Trained on your own material

Where a condition does recur and matters, a detection model can be trained against your own footage rather than waiting on a vendor release.

Custom Model Training

Every event kept

Flagged material is retained with the footage and an audit trail, so a quality investigation reviews evidence rather than recollection.

Event-Driven RecordingAudit Trail

Questionable, not just pass or fail

Confidence thresholds decide what a person looks at, so borderline cases route to a reviewer instead of being silently accepted or rejected.

Confidence ThresholdsHuman in the Loop

Ask across production history

Question weeks of recorded line footage in plain language and get the clip back with the answer.

Multimodal Search

Inside the plant boundary

Self-hosted models and air-gapped deployment, for production footage that cannot leave the site.

Self-Hosted Inference

How It Works

Alongside the inspection you already run

Watch the line

Cameras covering production and packaging are read continuously, with events raised on the conditions you configure.

Describe, do not just classify

A multi-modal model reads the frames and describes what it saw, which reaches the ambiguous and the unanticipated rather than only the enumerated.

Route what needs a person

Confidence thresholds send borderline material to a reviewer, and everything flagged is held with its footage for the investigation that follows.

FAQ

AI Visual Quality Intelligence for Manufacturing, asked and answered

Is this a replacement for machine vision inspection?

No, and it should not be evaluated as one. Deterministic inspection systems built for line speed and sub-millimetre precision do a job this does not: repeatable measurement against a known specification. This covers the other half -- visual conditions that were never defined as a class, described in language rather than matched against a list.

What can it actually catch?

Conditions a describing model can articulate from the footage: something out of place, a scene that does not look like it usually does, an unusual handling or packaging situation. Where a specific defect recurs and matters, a detection model can be trained on your own material to catch it repeatably.

Can it verify barcodes, labels or assembly steps?

Not as a dedicated inspection function. Barcode verification, label matching and component-presence checks against an expected assembly are the province of purpose-built inspection equipment, and you should keep using it for those.

How fast does it analyse?

Not at line speed. Analysis runs on cached frames on a cadence, which suits reviewing what has been happening rather than making a pass-or-fail decision on a moving part.

What happens to a flagged item?

The event and its footage are retained with an audit trail, and confidence thresholds decide whether it is acted on or routed to a person.

Can we train it on our own defects?

Yes. Custom detection model training runs against your own footage, so a condition specific to your process does not depend on a vendor adding it.

Does production video leave the site?

Not if you do not want it to. Self-hosted inference supports on-premises and air-gapped deployment.

Your Inspection System Catches What It Was Trained On