Computer vision that reads, detects, and inspects at scale

A proof of concept on your own images usually takes three to six weeks: enough to train a first model, measure it on a held-out set, and show whether the accuracy target is reachable. A production system — integrated with your cameras or scanners, deployed, and monitored — typically lands in three to five months, depending on how much data needs collecting and labelling. We agree milestones at the start and report against them every week.

Access to representative images, video, or documents — the more they reflect real operating conditions, the better — plus someone on your side who can tell us what a correct answer looks like. If you have no data yet, we can plan a capture programme and, where appropriate, generate synthetic data to get moving. We also need to know where the model will run and which systems it must feed.

We build primarily in Python with PyTorch, OpenCV, and the Ultralytics and Hugging Face model families, and export to ONNX, TensorRT, or Core ML for deployment. Annotation runs in CVAT or Label Studio. Cloud deployments use AWS, Google Cloud, or Azure; edge deployments target NVIDIA Jetson, Intel, and ARM hardware. Where a managed service such as Google Cloud Vision or Azure AI Vision meets the need, we will recommend it instead of a custom build.

Every model ships with a scorecard: precision, recall, F1, and mean average precision on a held-out test set, broken down by class and by condition such as lighting, site, and camera. In production we track the same metrics on sampled, human-reviewed predictions, along with latency and throughput, and share a live dashboard plus a written monthly summary that connects model performance to the business outcome we agreed.

You do. Training data, labels, model weights, inference code, and deployment configuration are delivered into repositories and cloud accounts you own. We work under a written agreement that assigns intellectual property to you on payment, and we document everything so another team could maintain the system without us.

As an extension of it. We can lead end to end, pair with your engineers so they own the system afterwards, or advise an existing data team on architecture and evaluation. You get a named lead, a shared channel, weekly demos, and code reviews open to your developers. Handover includes documentation, training sessions, and a runbook for the people who will operate it.

It depends on the task, how much data needs collecting and labelling, and where the model has to run, so we scope every project individually rather than publishing a rate card. Tell us what you want the system to see and what it should do about it, and request a quote — we reply within one business day with a proposed approach, timeline, and fixed-scope estimate.