Computer Vision Development Services for US & EU Industrial and Product Teams

It comes down to the task and the data. YOLO v11 and YOLOv8 are the default for object detection and instance segmentation when you have boxes or masks; v11 is faster and more accurate, v8 has the larger ecosystem of pretrained checkpoints. SAM 2 is what we reach for when you need segmentation masks without click-level labelling, especially for video. CLIP and DINOv2 are the picks for zero-shot classification, image retrieval, and visual search. Custom training (Detectron2, MMDetection, custom heads on ViT/Swin backbones) earns its keep when the domain is far from natural images: X-rays, satellite, semiconductor wafers, microscopy. The first deliverable is always a written model-selection memo, not a chosen model.

Latency, privacy, and unit economics decide. Edge (NVIDIA Jetson, OAK-D, Coral, mobile NPUs) wins when you need sub-100 ms response, when bandwidth is constrained, or when sending video to the cloud is a privacy or compliance non-starter. Cloud (NVIDIA Triton on GPU instances, AWS Rekognition for commodity tasks, GCP Vision, Azure Vision) wins when you need centralized model updates, when accuracy beats latency, or when devices cannot host a 200 MB model. Many production systems do both: a small detector on-device for triage, a larger model in the cloud for verification. We benchmark both paths on your real frames before recommending.

Three-step playbook. First, pre-label with foundation models: SAM 2 for masks, GroundingDINO for boxes, CLIP for classification, frontier VLMs (GPT-4o, Claude 3.7) for hard cases. This cuts annotation time by 60 to 80 percent. Second, human-in-the-loop review in Label Studio, CVAT, or Roboflow with an inter-annotator agreement target above 0.85 (Cohen kappa) before any frame enters training. Third, active learning: the model picks the next batch to label based on uncertainty, not random sampling. We can run the annotation team ourselves or set up the pipeline and hand it to yours.

Three signals tracked daily. First, output distribution: per-class confidence histograms, detection-count drift, mask-area drift, plotted against a seven-day baseline in Grafana. Second, input drift: embedding shift in CLIP or DINOv2 feature space using MMD or KS tests against the training set. Third, ground-truth feedback: a tunable percent of inference frames routed to human review (or to a downstream business signal that proxies for ground truth), and weekly precision/recall reports per slice. Alerts fire on threshold breach and trigger the retraining workflow in MLflow, with a documented rollback path.

Yes, with the compliance work scoped in from week one. Under GDPR Article 9, biometric data is special category data: legal basis must be explicit consent, vital interest, or substantial public interest. We co-author the DPIA with your privacy team before any frame is processed. Technical safeguards include on-device inference where feasible, hashed face templates instead of raw embeddings, age-out retention, and IAM-segregated storage. For US deployments we follow BIPA (Illinois), CUBI (Texas), and Washington H.B. 1493. We are GDPR-aligned, ISO 27001 ready, SOC 2 Type II in progress, HIPAA-capable, and CCPA-acknowledged.

Engagements are fixed-scope and lean, all-in and quoted in USD. A CV PoC runs from $2,900 (4–6 weeks); a single detection or classification model from $8,100; an end-to-end vision pipeline deployed to one target from $10,400; a production ML system with MLOps and drift monitoring from $13,800. The exact number depends on how far your domain sits from natural images, how much labelled data exists on day one, your deployment target and latency budget, and biometric or regulated scope. You see the line-item budget at the end of discovery and sign off before any code is written. There is no recruitment markup and no tool surcharges, and GPU compute, annotation labour and edge hardware run on your own accounts, so you keep the cost lever.

There is no single accuracy number worth quoting — a model at 95% mAP that fails on the 5% of frames that matter to the business is a bad model. We set the target during discovery as precision and recall per class and per slice, tied to the actual decision the model supports: a false negative on a safety-critical defect costs far more than a false positive, so the threshold is asymmetric and set with you. The golden eval set is frozen before training, and every model iteration is reported against it by slice, not as a headline average. “Good enough” is the point where the model plus its human-in-the-loop review beats your current process on the metric that drives the P&L — and we say so in writing if the data cannot get there.

Both. For video we add temporal handling on top of frame-level detection: SAM 2 for mask propagation across frames, multi-object tracking (ByteTrack, BoT-SORT) for persistent IDs, and frame-sampling strategies so you are not paying to infer on 30 near-identical frames a second. Real-time streams are an edge-vs-cloud and latency-budget question we benchmark up front — a p95 under 80 ms constraint on an RTSP feed pushes toward a quantized detector on a Jetson at the camera, while a batch-analytics pipeline over recorded footage runs cheaper in the cloud. We size the frame rate, resolution and buffering to the actual event you need to catch.

You do, from day one. IP transfer is written into the contract at the start, not negotiated at the end. You keep the trained weights, the annotation pipeline, the golden eval set, the training data and the deployment code — there is no vendor lock-in on the model artefact and no per-inference licence to us. GPU compute, annotation labour on high-volume datasets and edge hardware run on your own cloud and hardware accounts, so the assets and the cost lever both stay with you. If you later take the model in-house or to another team, everything needed to retrain and redeploy it is already yours and documented.

Yes — that is a common starting point. We begin with an audit rather than a rebuild: we re-run your model against a properly constructed per-slice eval set to find where it fails (a specific class, lighting condition, camera angle or edge case), check whether the problem is the data, the labels, the architecture or drift since launch, and quantify the gap. Often the fix is annotation quality and active learning on the failure slices, not a new model. When a rebuild genuinely is the cheaper path we say so and show the numbers. The output of the audit is a written diagnosis and a costed remediation plan, and it maps onto the same fixed-scope tiers as a new build.