AI Integration Services for the Systems You Already Run

Usually, yes, and it is most of what integration work actually is. Where an API exists we use it; where one does not we build a thin service in front rather than letting the AI reach into a database it does not understand. The audit tells you which of those you are dealing with before anyone commits to a timeline.

The feature degrades in a way you chose. That usually means a cached answer, a queued retry, or an honest message and a route to a human, never an error page. Fallback behaviour is designed in the architecture phase, because retrofitting it means touching every call site.

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.