Two disciplines, one team
Forecasta pairs a research-side ML practitioner with a senior infrastructure engineer on every project. Not a handoff between teams — the same two people, from problem statement to production.
Why two disciplines, not one
The AI services market has a structural gap: shops that can model well usually can't ship production infrastructure, and shops that can ship infrastructure usually outsource or fake the modelling. We built Forecasta specifically to close that gap — one person owns the model, one owns the infrastructure it runs on, and both are senior enough to work without a hand-off between them.
How we work
We take two engagements at a time. Fixed price, fixed scope, milestone payments, and a named deliverable inside the first week of every sprint. We don't bill hourly, and we don't staff a project with anyone junior — every hour on your engagement is senior time.
Where we are
Based in Canada, serving teams across North America and the UK. Engagements run remote, with a weekly check-in and direct access to whatever we're building as it's built.
Work we decline
Open-ended retainers with no scope
We'll take an ongoing retainer once a system is in production and needs monitoring and iteration — not as a substitute for scoping the first engagement properly.
Modelling without a production path
If there's no realistic path to deploying and monitoring a model, we'll say so before taking the engagement, not after delivering a notebook.
Hourly billing
Fixed price, fixed scope, every time. Hourly billing invites a rate comparison against offshore agencies we're not trying to compete with on cost.