Machine Learning Consulting That Starts With Your Data

That is a successful assessment, and it happens. You have spent two to four weeks instead of two quarters, and you usually leave with something more useful: which data to start capturing, or a rules-based approach that gets most of the value now. We would rather tell you early than bill you for a model that cannot work.

A representative sample is usually enough, and it can be anonymised or synthetic-shifted where the data is sensitive. What matters is that it is real in shape, volume, missingness and label quality, because those are exactly the properties a cleaned-up extract hides.

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.