We build AI that survives production.
Most AI projects die as demos. Forecasta builds the model and the infrastructure it runs on — forecasting, retrieval systems, agents, and the pipelines underneath them. One team, from problem statement to something your business depends on.
Why most AI projects don't ship
Every buyer in this market has been burned the same way, once.
Pilots that never leave the notebook
A demo that impresses in a meeting and never reaches a production endpoint, because no one built the path to get it there.
Models nobody monitors
Accuracy quietly decays after launch. Without monitoring and a retraining loop, the model that shipped is not the model running six months later.
Data that isn't ready
The model was never the hard part. The pipelines, the ingestion, the schema — that's where most projects actually stall.
Seven ways in, one team throughout
Fixed price, fixed scope. We take two engagements at a time, so every one gets senior attention end to end.
AI agents & workflow automation
Agents and automations that take real actions in your systems — not a chatbot demo. Scoped to one workflow, shipped with monitoring and a rollback path.
Retrieval systems & internal AI assistants
Retrieval-augmented assistants over your own documents and data — built for answer quality and traceability, not just a vector database and a prompt.
Machine learning & forecasting models
Models that predict what your business needs to plan around — demand, load, risk — with uncertainty stated plainly, not hidden behind a point estimate.
AI deployment & production infrastructure
The part most AI vendors skip: monitoring, retraining, versioning, and a deployment path that survives the model that made it into the first demo.
Data platforms & pipelines
Ingestion, transformation, and storage that makes the models above possible — pragmatic data platforms sized to the problem, not a lakehouse you don't need yet.
AI voice & conversational agents
Voice and chat agents that handle real call and conversation volume — qualification, scheduling, tier-one support — with a clean handoff to a human the moment the conversation needs one.
AI opportunity audit
A two-week, fixed-price audit of your data, use cases, and infrastructure — a scored readout and a concrete starting point, not a slide deck of ambitions.
Three ways to start
AI opportunity audit
A two-week, fixed-scope read on your data, use cases, and infrastructure. Credited in full against a sprint booked within 30 days.
A fixed-scope engagement
One of the six sprint services above. Fixed price, fixed scope, milestone payments, a named deliverable in week one.
Ongoing ownership
Monitoring, retraining, and iteration once a system is in production. Three-month minimum term.
We take two engagements at a time.
Ten to twenty hours a week, split across two senior people. That constraint is why the work is senior-only, and why we scope honestly instead of overselling capacity we don't have.
Where does your AI initiative actually stand?
A short, free assessment across data maturity, use case clarity, infrastructure, and team capability — scored, not guessed.
Ten questions, a few minutes, a plain-language diagnosis and a recommended starting point mapped to one of the services above. The paid audit does this properly, with your actual data, in two weeks.
Take the assessment →