The problem
Most businesses plan next quarter using last quarter's spreadsheet. Inventory is guessed, churn is discovered after the customer leaves, and pricing is set by instinct.
Your historical data already contains the signal. We build the models that surface it — and put predictions inside the tools your team already uses.
What we deliver
Predict sales, inventory needs, and staffing weeks ahead with confidence intervals.
Know which customers are about to leave and which leads are worth pursuing.
Decision-support models for portfolio analysis, risk, and scenario planning.
Personalized product and content recommendations that lift order value.
Catch fraud, outages, and data errors the moment they happen.
Pipelines that keep models accurate as your business changes.
Our process
We assess what your data can and can't support — honestly.
A first model beats the spreadsheet or we tell you why.
Feature engineering and tuning against agreed success metrics.
Predictions delivered into your CRM, ERP, or dashboards on schedule.
forecast-accuracy improvement in our sample warehouse + ML build. See forecasting in action inside a data project — read the full sample case study.
Read case studyQuestions we hear often
Less than you'd think. Two to three years of transactional history is usually enough to start; we'll tell you upfront if your data can't support the goal.
We define accuracy targets together before building, benchmark against your current method, and report honestly — including when a simple statistical model is the right answer.
Yes. This practice is predictive modeling on structured data (numbers, transactions, time series). Generative AI handles language and unstructured content. Many projects combine both.
No — we build analytical tooling and decision-support models. Investment decisions remain with you and your licensed advisors.
A 30-minute call is enough to tell you whether this is the right move, what it would take, and what it would cost.
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