Computer Vision Solutions Development
Real-time object detection, OCR pipelines, and edge-deployed quality inspection — built for manufacturing, logistics, and healthcare.
Built for teams like yours
Manufacturing
Visual quality control on production lines with MES integration.
Logistics & warehousing
Barcode-free tracking, damage detection, and dock monitoring.
Healthcare & pharma
Document OCR and regulated imaging workflows with GDPR controls.
Capabilities included in your build
Real-time detection & tracking
Low-latency pipelines for lines, warehouses, and live video feeds.
OCR & document processing
Invoices, IDs, labels, and forms with confidence scoring and human review queues.
Edge & cloud deployment
Run on NVIDIA edge devices, on-prem GPU, or EU-hosted cloud — same model artefact.
Fine-tuning on your data
Train and validate on your labelled datasets with reproducible MLOps.
Quality inspection pipelines
Defect detection, measurement, and pass/fail integration with MES systems.
GDPR-compliant image handling
Retention, anonymisation, and on-device processing where personal data appears.
How we ship
- 01
Discovery
We start with a structured workshop to map goals, users, constraints, and success metrics.
- 02
Design
Wireframes evolve into interactive prototypes you can test with real users before a line of production code is written.
- 03
Build
Weekly demoable increments, written tests, and code reviews — no surprises at launch.
- 04
Launch
Hardened deployments, observability, and a launch plan covering rollout, comms, and rollback.
- 05
Iterate
Post-launch we track usage, fix friction, and ship improvements on a cadence that fits your roadmap.
Engineered for outcomes, not invoices
- Cut manual inspection labour with measurable false-positive targets
- Deploy to factory edge without sending raw video to public cloud
- Model versioning and drift monitoring in production
- Integration hooks for MES, WMS, and existing alerting
- Optional 24/7 SRE and model ops post-launch
A modern, proven foundation
We pick boring, battle-tested tools so your platform stays maintainable five years from now.
Common questions
+Do you train custom models or use pre-trained ones?
We start from strong baselines, then fine-tune on your labelled data when domain accuracy requires it.
+Can it run on edge devices or only cloud?
Both — we optimise for ONNX/TensorRT on edge and keep cloud for training and heavy batch jobs.
+How much labelled training data do we need?
Pilots often start with 500–2,000 images per class; we advise on labelling strategy during discovery.
+What accuracy levels can we achieve?
Targets are set per use case in discovery — typically 95%+ on controlled inspection lines after fine-tuning.
+How do you handle model drift over time?
Monitoring dashboards, periodic re-eval, and retrain triggers when precision/recall fall below thresholds.
Ready to build Computer Vision Solutions?
Send us a brief — you'll hear back within one business day with next steps.