Computer Vision Development Services

It depends heavily on the task complexity and how different your use case is from publicly pre-trained models. For object detection on a narrow domain — say, 3 to 5 defect types on a manufactured part — 500 to 2,000 labeled images is often sufficient to fine-tune a strong baseline. For multi-class, multi-object scenarios with high visual variability, you may need 5,000 to 20,000+ labeled examples. We always start with a data audit to assess what you have and whether data augmentation or synthetic generation can close the gap before committing to large annotation budgets.

Yes. We deploy models on-premise to edge hardware or local servers — common requirements when images contain sensitive patient data, proprietary product designs, or must meet data sovereignty regulations. We've deployed on NVIDIA Jetson Orin, Hailo-8 accelerators, and standard Linux servers. The tradeoff is that model updates require a local deployment process, which we also engineer and document.

We build a retraining workflow as part of every computer vision engagement: data collection hooks that capture production images flagged for review, a pipeline to annotate and validate them, and a CI/CD process that runs regression tests before promoting a new model version to production. We also implement statistical drift monitors on model confidence scores and prediction distributions so you get early warning when inputs have shifted outside the training distribution.

A focused single-task system — one defect type, one document class, one object category — typically takes 8 to 14 weeks from data audit to production deployment. More complex multi-class or multi-camera systems run 16 to 24 weeks. Budget ranges from $50,000 for a scoped single-model system to $200,000+ for full-pipeline builds with edge deployment, integration, and operator tooling. We offer a 2-to-3-week paid data and feasibility sprint that gives you a concrete estimate before committing to full development.

Our production deployments include manufacturing defect detection (surface scratches, dimensional measurement, assembly verification), medical image pre-processing for diagnostic support, logistics and warehouse applications (barcode reading, parcel counting, vehicle identification), and document processing (form extraction, signature detection, ID verification). Healthcare and manufacturing are our deepest domains because the accuracy and reliability requirements there have pushed us to build more rigorous engineering practices.