Computer Vision & Edge AI

Computer vision brings AI into the physical world — automating the visual tasks humans currently do by looking at screens, products, documents, or environments. Modern vision systems can detect defects in manufactured parts, extract structured data from forms and receipts, monitor security feeds in real time, and analyze video for operational insights. The right system depends on three engineering choices: where the model runs (cloud vs. edge), how it is trained (general vs. custom), and how it integrates with the rest of your operations.

Edge AI vs cloud AI. Edge deployment runs models directly on local hardware — cameras, industrial sensors, mobile devices — instead of round-tripping data to the cloud. Edge wins when you need sub-second latency (manufacturing quality control, real-time security), when bandwidth is limited or unreliable (remote sites, vehicles), or when data privacy requires that video never leaves the premises. Cloud is right when you need higher model capacity, are processing recorded footage rather than live feeds, or need to centralize analytics across many sites.

Custom model development. Off-the-shelf models work for general object detection, but most business use cases require custom training on your specific data — your products, your forms, your facility, your defect types. We handle the full lifecycle: data labeling and curation, model selection and training, hardware-aware optimization for the deployment target (CPU, GPU, edge accelerator), and ongoing retraining as conditions change.

Document and OCR intelligence. Beyond physical-world vision, we build document AI systems that extract structured data from invoices, contracts, claims forms, and handwritten records. This goes well beyond basic OCR — modern document AI understands layout, table structure, and field semantics, turning unstructured paperwork into queryable data.

What gets automated. Visual inspection, inventory counts, security event detection, document data entry, video event extraction, and quality control — work that is expensive, slow, or error-prone when done by humans alone.