Models and dashboards that turn data into decisions — not slide decks.
Data science earns its keep when it changes a decision. We start with the question, then work back to the model or dashboard that actually answers it — and put it in front of the people who act on it.
It’s the right fit for teams sitting on data they don’t use, leaders who want forecasting instead of hindsight, or product teams building data-driven features.
Most models move from prototype to production inside a quarter.
Every engagement ships against outcomes we agree up front.
Forecasting, classification, and scoring — validated on your data.
Metabase, Looker, or Power BI dashboards your team will actually use.
The unglamorous work that makes everything downstream trustworthy.
A/B and quasi-experimental designs run properly.
Findings written for decision-makers, not statisticians.
Deployed and monitored — not sitting in a notebook.
A real sequence — each step earns the next.
Turn the business question into a measurable one.
EDA, feature work, and baseline models.
Trained, validated, and reviewed model or dashboard.
In production with monitoring and retraining plan.
Concrete artifacts you take away from the engagement.
We start with the decision, not the model.
Realistic accuracy — not cherry-picked numbers.
Models ship — they don’t die in notebooks.
Every project runs with a senior data scientist.
Depends on the question — sometimes we work with less data than teams expect. We scope up front.
Both. We’ll be explicit about which stage we’re shipping into.
Behind APIs, as scheduled jobs, or embedded — whatever fits your product.
We set a baseline first, then target a lift. Anyone promising a fixed number without your data is guessing.
Most POCs land in 4–8 weeks; production deployments in 8–12.
Tell us what you’re building — we’ll reply within one business day.