Inconsistent inspection
Manual checks vary with fatigue, volume, and product variation.
Computer vision
Visual intelligence. Built for your use case.
NIM model · Edge or cloud
What it solves
The problems teams most often bring to us first.
Events noticed late
Visual issues are found after they have already caused rework.
Manual counting
Counts and checks take time and are hard to audit.
Unused footage
Cameras record continuously, but the footage is rarely put to work.
Our own models
Our vision models. Your operating reality.
We combine NIM’s fine-tuned vision models with your visual inputs, business rules, and operational workflow. The result is designed around a specific task—not a generic demonstration.
1 model familyMany visual tasks
InspectionDetectionCountingVisual textMonitoring
Designed around the task
Define the objects, conditions, or visual information that matter to the workflow.
Tested in your conditions
Assess representative images, lighting, camera angles, and the exceptions your team encounters.
Connected to action
Make findings useful through alerts, review queues, records, or integration with existing systems.
See it in action
Counting, movement tracking and density mapping in a shared space.
Count activity
Detected people add to an on-screen count. Projects define counting zones and reporting periods around a real operational question.
Follow movement
Trails show paths through the scene, which helps teams study circulation and spot areas that need attention.
See density
Heatmaps make busy areas easy to see. Camera coverage and testing decide how far the results can be relied on.
Where it helps
Each application is scoped around your data, environment and requirements.
Quality & defect inspection
Help flag visible defects, inconsistencies, or missing components for operator review.
Object detection & counting
Locate and count defined objects to support inventory, production, or operational tracking.
Safety & process monitoring
Explore detection of agreed visible conditions, such as protective equipment or restricted-zone events, with appropriate human review.
Document & visual text extraction
Extract useful text or identifiers from suitable visual inputs and connect them to a validation workflow.
Retail & shelf visibility
Assess defined shelf or product conditions and route findings to the responsible team.
Logistics & package inspection
Identify visible package conditions, labels, or counts at agreed points in a handling workflow.
Asset & equipment observation
Identify defined visible states or changes and create records for operational follow-up.
Space & occupancy insights
Explore aggregate counts and use of space, with privacy requirements and data minimisation defined up front.
How it works
A typical flow, configured around your rules and approvals.
- 01
Capture image or video
- 02
NIM vision model analyses
- 03
Review relevant findings
- 04
Trigger a business action
What a project includes
From the first question to a working system your team owns.
- Use-case definition and visual environment assessment
- Data and labelling requirements for the agreed task
- NIM vision model configuration and evaluation
- Review, alert, and integration workflow
- Pilot testing in representative operating conditions
- Deployment, monitoring, and model review plan
Technology & integrations
IP & CCTV camerasEdge inference devicesNIM vision modelsOperator review screensAlerts by email, SMS, or WhatsAppMES & WMS integration
How we measure it
Acceptance criteria are agreed before a pilot grows into a rollout.
Detection quality
Measure task-specific performance, including missed events and incorrect alerts.
Operating conditions
Test representative lighting, angles, object variations, and difficult examples.
Review and action
Assess whether findings are useful, understandable, and correctly routed to the next step.
Questions, answered
Something else on your mind? Ask us directly.
Does NIM have its own computer vision models?
Yes. NIM has its own fine-tuned vision models. We apply them through a use-case-specific implementation that includes visual inputs, evaluation, review rules, and system integration.
Can the same platform support multiple vision use cases?
We scope each task separately because its data, acceptance criteria, and workflow may differ. Multiple use cases can then be planned as part of a coordinated implementation.
Can we use our existing cameras?
We first assess image quality, placement, lighting, access, and the target task. Existing equipment may be suitable; some environments need changes.
What do you need to start?
A description of the target condition, representative images or video, the operating environment, and the action you want to take after a finding.
How do you measure accuracy?
We agree on task-specific measures and test representative data, including difficult cases. False alerts and missed events are evaluated alongside the review effort.
Is the system intended to replace human judgement?
The appropriate level of automation depends on the task and risk. We build review and exception paths into the workflow, especially where a finding could lead to a consequential action.
How is visual data handled?
Data access, purpose, retention, and deployment requirements are discussed before implementation. Privacy and data minimisation are part of the solution design.
