PRODUCTION PROOF
Live vision system
Deployed across UK hospitality venues.
COMPUTER VISION SYSTEMS
Turn video into useful operational information.
We build production systems that detect what is happening in images and video, turn it into meaningful events and give people the tools to review, understand and act on the result.
Operational visibility · Visual inspection · Movement analysis · Video intelligence
SYSTEM DELIVERY
Edge + cloud delivery
Camera processing through reporting.
CLIENT FEEDBACK
5.0 on Upwork
Verified client reviews.
DELIVERY MODEL
Founder-led delivery
Direct access from scope through production.
WHERE COMPUTER VISION FITS
When an important decision depends on something only a camera can observe.
Computer vision is valuable when employees repeatedly watch, inspect or interpret visual activity—or when the required operating information is never recorded by existing software.
Operational visibility
Understand what is happening inside a physical operation.
Convert movement, activity and configured zones into events such as queues, service stages, dwell or exceptions.
CAMERA ACTIVITY → OPERATIONAL EVENT → REPORTING
Visual inspection and verification
Check states, conditions and evidence consistently.
Identify required objects, configured states, defects, anomalies or proof that a physical action occurred.
IMAGE OR VIDEO → VISUAL CHECK → RESULT OR REVIEW
Movement and form
Turn body movement into structured, reviewable information.
Estimate pose, sequence and range of movement for coaching, rehabilitation or guided product experiences.
VIDEO → POSE + MOVEMENT → STRUCTURED FEEDBACK
Video intelligence
Transform long footage into an explainable timeline.
Track relevant activity and create events, highlights or evidence that users can inspect without watching the entire recording.
FOOTAGE → EVENT TIMELINE → REVIEW
SELECTED COMPUTER VISION EXAMPLE
From restaurant cameras to multi-site operational reporting.
Eyezilla is included as production evidence for the complete system described on this page: capture, model behaviour, event logic, operating interfaces and recovery working together in real venues.
SUPPORTING CASE STUDY · PRODUCTION COMPUTER VISION
Eyezilla
Important parts of restaurant operations happen physically and are not recorded by the POS or other business systems. Eyezilla converts camera streams into structured operational events and multi-site reporting.
We designed and built the complete system: camera connectivity, edge processing, detection and tracking, event logic, data pipelines, dashboards, device monitoring and production recovery.
- Operating in real venues
- The system processes footage from active hospitality environments rather than controlled demonstrations.
- Complete system ownership
- Camera, inference, event processing, data and user-facing reporting were developed together.
- Designed for partial failure
- Device, network and processing problems are monitored and recoverable.
- Continued evaluation
- Visual behaviour can be reviewed as venues, conditions and models change.
MORE THAN A MODEL
The model identifies something. The system makes it useful.
A production vision product must reliably capture the scene, interpret it, decide what constitutes a meaningful event and expose the result inside an operable product.
- 01
Capture
Connect cameras, streams, uploaded footage or mobile capture with appropriate resolution, coverage and health monitoring.
- 02
Interpret
Apply detection, segmentation, tracking or pose models evaluated against representative scenes.
- 03
Decide
Turn model outputs into persistent events using business rules, confidence, time and scene context.
- 04
Operate
Provide review, correction, analytics, alerts and integration with downstream business systems.
Device health · Model versions · Evaluation data · Access control · Evidence retention · Monitoring
PERFORMANCE IN THE REAL WORLD
Accuracy belongs to the environment—not only the model.
A model that performs well on test footage can still fail when camera angle, lighting, crowding or operator behaviour changes.
RepresentativeEvent detected · 94%
Low lightEvent detected · 77%
OccludedReview required
CrowdedAmbiguous tracks
Representative evaluation
Test against footage from the environments, camera positions and conditions the system will actually encounter.
Explicit failure categories
Measure missed events, false events and ambiguous cases separately rather than hiding them inside one accuracy number.
Operator correction
Allow permitted users to review important results and provide structured feedback.
Controlled changes
Compare new models and event logic against the established evaluation set before deployment.
Appropriate privacy
Minimise identity, footage access and retention wherever they are not required for the operating purpose.
FROM FOOTAGE TO PRODUCTION
Prove the visual decision before scaling the infrastructure.
- 01
Assess
Review representative footage, the required decision, camera setup, operating environment and acceptable failure behaviour.
FEASIBILITY FINDINGS · RECOMMENDED TEST
- 02
Prove
Test the difficult detection, tracking, pose or event logic against an agreed evaluation set.
WORKING PROOF · BASELINE RESULTS · LIMITATIONS
- 03
Pilot
Deploy into one real environment to measure scene variation, hardware, connectivity and operator workload.
REAL-ENVIRONMENT PILOT · PRODUCTION RECOMMENDATION
- 04
Deploy and improve
Build the complete operating system, expand it carefully and continue measuring behaviour as conditions change.
PRODUCTION SYSTEM · MONITORING · IMPROVEMENT ROUTE
Representative footage · Required decision · Current camera setup · Operational and privacy constraints
ADDITIONAL WORK
Computer vision shaped around a useful product decision.
ALLIMB
Proving camera-guided rehabilitation on everyday devices.
HealthcareMVP developmentComputer vision
Working mobile PWA with live pose analysis, repetition counting and corrective feedback.
View case study
TENNIS VIDEO INTELLIGENCE
Finding active play in fixed-camera tennis recordings.
SportComputer visionVideo processing
Court-aware player tracking and explainable active-play timelines.
View case study→
Discuss the footageHAVE SOMETHING SIMILAR IN MIND?
Bring us representative footage and the decision it needs to support.
FAQ
Computer vision questions
Often, yes. Feasibility depends on stream access, resolution, frame rate, camera position, network conditions, retention rules and the decision the system must support. We assess those constraints before recommending new hardware or infrastructure.
We begin with representative footage, the required decision and explicit failure behaviour. We determine whether an existing model provides a useful baseline, what data or labelling is required and whether the complete capture-to-event workflow can satisfy the operating constraints.
We use task-specific evaluation against representative scenes and keep missed events, false events and ambiguous cases separate. Performance is reviewed across relevant lighting, camera angles, occlusion, crowding and operating states rather than reduced to one aggregate accuracy number.
Yes. Edge processing may be appropriate where latency, bandwidth, intermittent connectivity, privacy or camera access makes cloud-only processing unsuitable. A system can also combine local inference with controlled cloud event storage and reporting.
Controls are designed around the operating purpose. They can include minimising or masking identity, processing on the edge, storing events rather than continuous footage, limiting retention and restricting footage and review access to authorised roles.
PROJECT INTAKE / COMPUTER VISION
Show us the footage and the decision it needs to support.
Tell us what the camera can see, what a person or system needs to know, what infrastructure already exists and which errors would matter. We will help determine whether the idea is feasible and what the first representative test should prove.