Machine Learning Development, Measured in Production

Less than most teams assume for classification, more than they hope for forecasting. We start with a data-readiness review: it tells you whether the signal exists at all, and it often finds that a simpler model on cleaner data beats a deep one on everything you have.

You hear it from monitoring, not from a customer. Input and prediction distributions are watched continuously, alerts fire on drift, and a retraining pipeline is in place from day one, so the fix is a run rather than a project.

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.