Vision and generation at production volume.

The strongest fits are workflows with a clear visual signal and a repeatable decision: matching a product photo to catalog items, flagging a defect on an assembly line, pulling structured data off a scanned document, or generating on-brand imagery at volume. If the task can be described as "look at this image and decide X," it's usually a good candidate.

Ideally a sample of real images or footage from your actual product or environment, even a few hundred examples. If you don't have data yet, we can help set up capture during discovery, or bootstrap early versions with synthetic or licensed data while your own dataset grows.

We start with off-the-shelf APIs whenever they clear your accuracy bar — it's faster and cheaper. We move to fine-tuning or a custom model only when generic models plateau on your specific imagery, domain, or edge cases, which is common for niche products, industrial settings, or brand-specific generation.

It depends entirely on the task and how much labeled data is available — we set a target accuracy during scoping based on similar production systems and your risk tolerance, and don't recommend going live until the eval suite hits it.

We hold out a test set the model never trains on, weighted toward the edge cases and failure modes that matter most for your use case — poor lighting, unusual angles, rare defect types — and score against it before anything ships. Generative outputs are reviewed against brand and quality guidelines with the same rigor.

You own the output produced for your project. We use generation providers with commercial-use terms and flag any provider-specific IP or indemnification limits during scoping, so there are no surprises about what you can and can't do with generated assets.

We scope exactly what images and metadata leave your systems, use provider-level data-retention opt-outs where available, and can deploy self-hosted or region-locked models when compliance or customer imagery sensitivity requires it. Access to training data is scoped per project, never blanket.

We wire the model into your existing stack via API, batch job, or an edge-deployed model where latency requires it — connecting to your image storage, product database, or manufacturing systems directly, rather than requiring you to build a separate workflow around it.

A scoped feature built on off-the-shelf models typically ships in 3–6 weeks. Projects requiring data collection, annotation, and custom fine-tuning usually run 8–14 weeks depending on data availability. We give an exact timeline after discovery.

Cost depends on scope — whether we're integrating an existing API, fine-tuning a model, or building a full annotation and training pipeline. We give a fixed project quote after the discovery call, plus inference or generation costs that scale with usage.

Yes — accuracy monitoring, drift detection, and scheduled retraining as new data comes in are typically handled through a monthly retainer, since real-world image distributions shift over time. Ad hoc support is available if you'd rather run it in-house after handoff.

Yes. We regularly inherit models built in-house or by another vendor — auditing the training data, evaluation approach, and failure logs first, then fixing accuracy or reliability issues without a full rebuild where possible.

We're a startup-focused team that fine-tunes on your real imagery instead of shipping a generic API wrapper, and we treat model accuracy as an ongoing product with evals and monitoring built in — our engineering leads stay hands-on through launch and beyond.

Yes, with review steps built in — we typically pair generation with automated quality and brand-compliance checks, plus a human approval gate for anything customer-facing, so generated imagery meets the same bar as photographed or designed assets before it goes live.

Book a scoping call — we'll walk through the visual task you want to solve, the image or video data you have available, and give you a fixed-scope proposal and timeline within a few business days.