Image and video analysis for inspection, detection, document capture and counting, deployed in the cloud or at the edge.
Stack we work in
Figures as of Oct 2026
Trusted by
We build computer vision systems that detect objects, inspect products for defects, read documents and analyze video from your existing cameras.
Models are trained on your own images, tested against your quality targets and deployed on cloud servers or edge devices close to the camera. Where people appear in the footage, privacy safeguards such as anonymization and data minimization are designed in from the start.
Manual checks and unused camera footage cost time and let issues through.
People checking products by eye get tired and inconsistent, and only a sample of items is checked.
Staff retype data from invoices, forms and labels, which is slow and introduces errors.
Sites record hours of video but only review it after an incident, so live issues go unnoticed.
A demo works on clean sample images but breaks with real lighting, angles and product variation.
Video of people raises legal and trust questions that stop projects before they start.
Outcomes
An assessment of your images, cameras and targets, with the expected approach and any hardware or lighting changes needed.
A labeled image set with clear labeling rules, so your team can add more data consistently.
The model with results on real-condition test images, including misses and false alarms.
The model running on edge devices or in the cloud, optimized for the hardware and connected to your systems.
Live views of detections, counts or extracted fields, with alerts sent to the people who act on them.
Repositories, deployment scripts and a runbook for operating and retraining the system.
Detect scratches, dents, missing parts and wrong labels on the line and flag or reject faulty items.
Read invoices, delivery notes, IDs and forms and send structured data to your finance or operations systems.
Count footfall, measure queues and dwell time and track occupancy without identifying individuals.
Detect missing PPE, people in restricted zones and vehicle near misses, and alert supervisors.
Read labels and barcodes, count items, check pallets and track parcels through the site.
Analyze drone and camera images to spot crop issues, damage or equipment faults.
Built with
Built with
We agree the precision and recall you need for the decision at hand, then test against them on your own images before go-live.
CCTV and IP cameras over RTSP, existing VMS platforms and line equipment are integrated as they are. New hardware is only proposed when it is needed.
Latency, bandwidth and privacy decide whether a model runs on NVIDIA Jetson at the camera or in the cloud, and we show the numbers behind the choice.
Face blurring, anonymization and retention rules are built in where people are on camera, with data handling designed around GDPR.
Results flow into MES, ERP and alerting systems, with monitoring, retraining and support after deployment.
Featured case study
Every business already owns the sensor network it needs. CCTV records everything and tells you nothing — until someone reviews the footage, usually days after the loss.
Read the case study
We review your images or video, camera positions, lighting and quality targets, and agree what the system must detect and how accurate it must be.
You receive Feasibility report and success criteria
We collect representative images, set labeling rules and label a training set with your experts, covering normal cases and the edge cases that matter.
You receive Labeled dataset and labeling guide
We train and compare models such as YOLO or Detectron2, or use a cloud vision API where it is good enough, and test against held-out images from real conditions.
You receive Trained model and accuracy report
We run the model on live cameras or document flows alongside your current process, review misses and false alarms, and tune thresholds.
You receive Pilot results and tuning log
We optimize the model for the target hardware, deploy it to edge devices or the cloud and connect results to your dashboards, alerts and business systems.
You receive Production system and integration
We monitor accuracy and device health, collect hard cases for labeling and retrain the model as products, sites or conditions change.
You receive Monitoring and retraining plan
Tech stack
AI and machine learning
Backend and APIs
Languages
Cloud and DevOps
Other tools
Case study
See how we built a video analytics system that turns CCTV footage into useful events and alerts.
Cost depends on how many objects or defect types the system must recognize, how much labeled data exists, image and lighting conditions, the number of cameras or sites, edge hardware needs and integrations. We start with a feasibility study on your own images, then give a fixed estimate for the pilot and rollout. Hardware and cloud costs are estimated separately. As a guide, a focused AI pilot usually costs $10,000 to $50,000, and a full custom AI build $40,000 to $150,000 or more (USD).
A feasibility study on sample images often takes a few weeks. A pilot on site and a production rollout typically take a few months, depending on data collection, labeling and the number of locations.
It depends on the task and how varied the conditions are. Simple detection can start with a few hundred labeled images per class, while subtle defects or many classes need more. We often begin with a pretrained model and your examples, then add images from the pilot to cover the cases it gets wrong.
Often yes. Many projects use existing CCTV or IP cameras. For fine inspection work, camera resolution, angle and lighting matter, and we will recommend changes only where they are needed to reach your accuracy target.
Edge deployment on devices such as NVIDIA Jetson suits low latency, many cameras, limited connectivity or footage that should not leave the site. Cloud processing suits documents, batch analysis and lower volumes. Many systems combine both, and we compare cost and performance for your case.
We collect only what the use case needs. People counting and occupancy can work without identifying anyone, faces and number plates can be blurred, and raw footage can stay on the device with only counts or events sent on. We help you document the lawful basis and retention rules. Data handling is designed around GDPR and the local data protection rules that apply to you.
We agree target accuracy, acceptable false alarms and misses with you at the start and report results on images from your real conditions. We do not promise a figure before testing on your data, and the pilot shows performance before any wider rollout.
You own the code and IP we build for you, including trained models, labeled datasets and deployment code. Your images and video stay in storage you control.
Yes. Detections, counts and extracted fields can go to your MES, ERP, document management system, dashboards or alerting tools through APIs, message queues or database writes. We work with your IT and operations teams on the integration.
We monitor model accuracy and device health, review hard cases, retrain models when products or conditions change and update edge devices remotely. Support runs on a monthly plan, or we hand over to your team with documentation and training.
Book a call, and we talk through the goal, the systems involved and any constraints under NDA. You then get a short written proposal with scope, timeline and estimate. Send a sample of images or video from the real environment, and we run a feasibility check and quote a pilot on one camera or line.
Running models on the device cuts latency and bandwidth costs and keeps images on site.
Edge devices and gateways
Models deployed on NVIDIA Jetson, industrial PCs and smart cameras next to the production line or store.
On-device mobile vision
Detection and recognition inside iOS and Android apps with Core ML and LiteRT, working offline.
Model optimization
Quantization, pruning and conversion with ONNX Runtime, TensorRT and OpenVINO so models fit small hardware.
Privacy by design
Only events and counts leave the site, not raw video, which helps with data protection rules.
Fleet updates and monitoring
Remote model updates, health checks and accuracy monitoring across many devices.
Book a free consultation. Share sample images or video and we will assess feasibility and suggest a pilot.
