Computer Vision Development Services

Cost depends on the task, your image data, and deployment — edge adds work. A focused single-task CV system typically runs $20,000–$50,000; a multi-task or edge-deployed system with custom model training runs $50,000–$120,000+. We usually start with a fixed-price PoC (typically $12,000–$20,000) that proves accuracy on your real images first, and every engagement opens with a free 30-minute discovery call.

A production CV system typically takes 10–14 weeks: roughly 2 weeks discovery and image-data assessment, 5–6 weeks data annotation and model build, 2 weeks accuracy and hardening, then pilot and production. A scoped PoC runs in about 6 weeks. The biggest variables are image-data availability and edge deployment, both of which we scope upfront so the timeline is honest.

Yes — you rarely need as many as you fear. We use transfer learning (starting from models pretrained on millions of images), data augmentation, focused active-learning labelling, and synthetic data for the rare cases that are hardest to capture. We design the cheapest path to your accuracy target rather than waiting on a huge hand-labelled dataset, and the images the system sees in production become the dataset that improves it.

Accuracy depends on the task and image quality, so we measure it on a labelled set of your images — precision, recall, mAP — and set a target with you before building, tuning to the error trade-off your use case needs, because a missed defect and a false alarm rarely cost the same. It doesn't stop at launch: cameras move, lighting shifts, products change, so we monitor accuracy in production and re-train or tune before it degrades. The labelled evaluation set we build is what makes every change measurable.

Both — and we decide based on your needs. For real-time results, poor connectivity, or keeping footage on-site, we deploy optimised models on edge devices and cameras. For heavier models or lower volumes, cloud is simpler. Many systems are hybrid — a fast edge model plus cloud for hard cases. We optimise (quantise, compress) so accuracy survives the move to a device rather than collapsing on it.

Yes — it's one of our highest-value use cases. We build inspection that catches defects, damage, and anomalies at production speed, including anomaly detection for defects you've never labelled. We tune it to the cost of a miss versus a false reject, and engineer it for your line speed, lighting, and hardware — not a lab bench.

Both. We build OCR and document AI that extracts text and fields from photos, scans, forms, IDs, labels, and even handwriting — validated against a schema so the output is structured, usable data, even from messy captures (skew, glare, low resolution) that off-the-shelf OCR struggles with. And we build video analytics for counting, tracking objects or people, and detecting events and movement in real time, turning live or recorded feeds into metrics and alerts.

Usually, yes. We can run vision on the feeds from cameras and CCTV you already have, turning existing hardware into a sensing layer for inspection, safety, or counting — no need to rip out and replace, provided the image quality and placement support the task, which we assess upfront rather than after install.

Carefully and by design. We can run entirely on the edge so footage never leaves your site, blur or avoid storing identifiable faces where the task doesn't need them, encrypt data, and restrict and log access — aligning to GDPR, HIPAA, and ISO 27001 requirements depending on your context. For sensitive imagery — people, medical, secure sites — privacy, proportionality, and governance are part of the design, not an afterthought, so the system stands up to a compliance review.

Often, yes. If you have a model that's inaccurate, slow, or fails in certain conditions, we assess it and frequently improve it — more representative data, augmentation, a better-suited architecture, or edge optimisation — rather than starting over. Sometimes a fresh build is cheaper; we'll tell you honestly which.

Yes to both. Every engagement includes 90 days of post-launch support — accuracy monitoring, tuning, and adjustments — with retainers beyond that for ongoing monitoring, re-training as conditions change, new visual tasks, and maintenance, because an unmaintained vision model drifts as the world in front of the camera changes. And ownership is complete: the trained models, code, pipelines, annotated datasets, and documentation are yours unconditionally, including custom models trained on your images. We sign NDAs before any discovery conversation and never reuse your data for anyone else.