Generative AI Development, Grounded in Your Own Data

It depends on the task, and it is usually more than one. We benchmark candidates against your own evaluation set on cost, latency and quality, and often route different requests to different models. Because the routing sits behind an interface, the answer can change later without a rewrite.

Yes. Open-weight models can run in your VPC or on-premises, which is the usual answer when data cannot leave the building. We size the hardware, handle serving and quantisation, and keep the same evaluation suite pointed at it so quality is comparable.

We start with a short discovery call to clarify goals, constraints, and success metrics. Then we identify high-impact AI use cases, review your data and systems, and propose a phased roadmap with costs and risks attached to each phase.

Discovery runs 2 to 4 weeks, an MVP typically lands in 8 to 16 weeks of weekly-demo sprints, and operate is an ongoing arrangement with monitoring and support after launch.

Fixed-scope work is priced per milestone, dedicated teams are billed monthly per seat, and staff augmentation is weekly or monthly. You pick the model that fits and can switch between phases. There is a fuller breakdown of what moves the number on our AI development cost page.

You do. The full repository, documentation, and deployment transfer to you at every milestone, with no lock-in to us.

NDAs from day one, scoped access, and data residency decided at the architecture stage. We work within HIPAA, SOC 2, and similar requirements where they apply.

We monitor accuracy, cost, and uptime, run regression checks as models drift, and keep a defined escalation path with support so the system stays healthy after release.