Predictive modeling & applied AI

What we build

• Forecasting and demand models: what sells, when, and how much, so inventory and staffing stop being guesses.
• Classification and risk models: churn, default, fraud, quality. Which customers leave, which claims need a second look.
• Natural language models: turning free-text descriptions, notes, and documents into structured, usable categories.
• Retrieval systems (RAG) over your own documents, so your team asks questions in plain English and gets grounded answers with sources.
• Autonomous agents for multi-step workflows: qualification, triage, and research tasks that used to burn analyst hours.
• Segmentation and clustering: who your customers actually are, from the data rather than the org chart's assumptions.

How we build it

1. Frame the decision. What action changes when the model speaks? If no action changes, we tell you a model isn't the answer.
2. Audit the data. Coverage, leakage, label quality. Most model failures are data failures caught too late; we catch them first.
3. Baseline before brilliance. A simple rule or regression sets the bar. Complexity has to earn its keep against it.
4. Train and compare candidates. Multiple approaches, one honest scoreboard, tested on data the models never saw.
5. Validate like a skeptic. Holdouts, backtests, and stress cases. We try to break our own model before your operations do.
6. Document and hand over. A model card, a validation report, and a plain-language readout your leadership can actually read.