Computer vision agencies: hire for production vision systems

Choose a vision specialist when success depends on image or video quality under real capture conditions — lighting, motion, domain shift, class imbalance — and you need labeling, evals, and inference ops as much as model choice. A general LLM shop can prototype an API wrapper; they rarely own factory-floor failure modes or document-layout drift. If your bottleneck is chat or RAG on text, stay in LLM integration; if the input is pixels and the metric is precision/recall or escape rate, shortlist computer vision depth.

A useful PoC runs on your (or closely matched) data against a fixed eval set, reports precision/recall or task error with confidence intervals you understand, checks latency on the target hardware path, and ends in a written kill-or-continue decision. It should surface labeling gaps and hard classes — not only a happy-path demo reel. Production hardening (monitoring, drift, retraining, security, handoff) belongs in a separate phase with its own acceptance criteria.

Hire an agency when you need speed and pattern experience (data pipelines, eval harnesses, edge deployment) without staffing a full CV team yet. Build in-house when vision is core IP, data cannot leave your environment, and you can own labeling guidelines, on-call, and model refresh long-term. Hybrid is common: agency ships the first production slice and harness; your team owns sensors, integrations, and steady-state ops. Either path requires repos, weights, and cloud/edge accounts under your org.

Enough representative images or video from the real environment — including hard negatives and rare defects — to define classes and score a PoC. Agencies can design labeling guidelines and help scale annotation; they cannot invent conditions you never capture. If you only have stock photos or a handful of pristine samples, fund a collection/labeling sprint first. Share privacy constraints and retention rules up front so proposals stay realistic.

You keep raw and labeled data, eval sets, trained weights derived from your data, application code, and inference accounts. The agency owns delivery against the SOW: pipeline design, training/eval approach, and documented handoff. Require that your data is not used to train models for other clients unless you allow it, and that offboarding includes exports and runbooks — not a black-box service only they can operate.