Inspection at volume
Manual checks become harder to keep consistent across long shifts, product variants, and changing conditions.
Manufacturing & industrial operations
More visibility on the floor. Less manual effort around it. Connect cameras, documents, and operational systems so teams can spot exceptions earlier, record them consistently, and act with the right context.
Visual defect inspection
Flag visible defects, missing components, or inconsistencies for an operator to confirm.
See all applicationsWhat slows teams down
Common pressures in manufacturing & industrial operations.
Paper-heavy records
Quality, maintenance, and production information is often captured on paper or in spreadsheets, then re-keyed.
Late exception reporting
Issues are noticed after the fact, when rework and delay already cost more.
Fragmented visibility
Information about lines, shifts, and stock sits in separate systems and is hard to compare.
Where AI helps
Each application is scoped to your data, systems and approvals.
Visual defect inspection
Flag visible defects, missing components, or inconsistencies for an operator to confirm.
Computer visionCounting & verification
Count defined parts, packs, or items at agreed checkpoints and reconcile against expected quantities.
Computer visionSafety & zone monitoring
Detect agreed visible conditions, such as protective equipment or restricted-zone entry, with human review.
Computer visionMaintenance & quality records
Digitise checklists and logs, extract key fields, and route issues to the responsible team.
AI automation & agentsProduction reporting
Bring line, shift, and quality data into consistent pipelines and dashboards.
Data & AI engineeringShop-floor assistance
Let teams query SOPs and work instructions in their preferred language.
Generative & conversational AIAn example workflow
How one of these applications runs day to day.
- 01Camera captures the line
- 02NIM vision model flags a candidate defect
- 03Operator confirms or dismisses
- 04Outcome recorded in the quality system
What to plan for
We work through these questions with you during discovery.
Camera environment
Lighting, angle, line speed, and product variation all shape what is reliable. We assess these before committing to a model approach.
Edge or cloud
Latency, bandwidth, and data-handling needs decide whether analysis runs on-site, in your cloud, or both.
Systems of record
Findings are most useful when they reach your MES, ERP, or quality tools. We map the integration early.
Operator ownership
Operators keep decision authority. The system supports their judgement rather than replacing it.
Capabilities we bring
Computer vision
Our own fine-tuned vision models, applied to multiple real-world use cases.
AI automation & agents
Intelligent workflows that connect your people, processes, and systems.
Data & AI engineering
The data pipelines, integrations, and infrastructure that make AI work.
Generative & conversational AI
Useful assistants and copilots, grounded in the information that matters.
Questions, answered
Something else on your mind? Ask us directly.
Can this work with the cameras we already have?
Sometimes. We review image quality, placement, lighting, and the target task first. Some environments need repositioned or additional cameras.
Do we need a lot of labelled data to start?
It depends on the task. We start with representative samples, including difficult cases, and agree a labelling plan that fits the pilot scope.
Can it run without sending video to the cloud?
Yes, edge deployment is an option for suitable tasks. We weigh latency, hardware, maintenance, and data-handling requirements together.
