Predict what moves the metric, before it moves.
Churn, LTV, lead scoring, and demand forecasts built on your data, wired into the tools your team already uses.
WHAT IT IS
The plain description.
We build predictive models on your own history: who will churn, which leads will convert, what a customer is worth, and what demand looks like next quarter.
The point is not a notebook full of charts. Predictions land where decisions happen: your CRM, your dashboards, your campaign targeting.
We are honest about uncertainty: every model ships with an evaluation, a baseline to beat, and a plan for when it drifts.
WHEN YOU NEED IT
Signals that this fits.
Churn surprises you after it happens
Score risk early and act while you still can.
Sales chases every lead equally
Budget is planned on gut, not forecast
You know LTV matters but cannot compute it reliably
You have data but no model in production
HOW WE DO IT
A short, honest sequence.
- 01
Define the decision
We start with the business decision, not the algorithm. What action changes if the prediction is right?
- 02
Data & features
We audit your data, build stable and explainable features, and flag gaps before modeling.
- 03
Model & validate (baseline first)
We train a few candidates, compare each to a simple baseline, and pick the honest winner.
- 04
Ship into the workflow
Predictions flow into your CRM, dashboard, or ad platform where someone can act on them.
- 05
Monitor & retrain
We track performance and data drift, then retrain on a cadence that matches your business cycle.
WHAT YOU GET
Deliverables at the end.
- A production model or scoring endpoint
- Predictions pushed into your CRM/dashboards
- An evaluation report vs a baseline
- A monitoring + retraining plan
- Documentation and handover
STACK
Tools we reach for.
Modeling
- Python
- scikit-learn
- XGBoost
- PyTorch
Data
- dbt
- BigQuery
- Postgres
Serving
- FastAPI
- batch jobs
Monitoring
- Evidently
- custom drift checks
QUESTIONS
Answers, before you ask.
- We usually start with a few hundred labeled events or customers. More is better, but a small, clean dataset often beats a large, messy one. We validate feasibility before any build.
Next step
Book an intro call.
Fifteen minutes. We ask the sharp questions, tell you if we are a fit, and either scope a sprint or point you elsewhere.