Forecasts that admit what they do not know.

It means turning your historical data into a model that scores something before it happens — which customer will churn this month, which order is likely fraudulent, how much demand to expect next week. Instead of a dashboard that tells you what already occurred, you get a number or a flag you can act on in advance.

You need enough historical examples of the outcome you're predicting — past churn events, labeled fraud cases, past sales history — typically several months to a year, depending on how often the event occurs. In discovery we audit what you have and tell you honestly if there isn't enough yet to build something reliable.

A focused single-target model — one churn score, one fraud flag — typically takes 4–7 weeks from scoping through validated deployment. Recommendation engines and multi-signal personalization systems usually run 8–12 weeks given the extra feature and pipeline work involved.

It depends entirely on the problem and the data available, so we set a target accuracy metric during scoping — precision and recall for fraud, mean absolute error for forecasting — and don't ship until the model clears it on holdout data. We'd rather tell you a model isn't ready than ship one that looks good on paper and fails in production.

Forecasting predicts a number over time — demand, revenue, churn rate. Fraud and anomaly detection flags individual events as unusual against learned normal behavior. Personalization and recommendation engines rank or match content to a specific user. Each uses different model types and validation approaches, which is why we scope the target before picking a technique.

Classification models for churn, fraud, and lead scoring; regression models for forecasting revenue, demand, or lifetime value; and clustering models for customer segmentation and anomaly baselines. We pick the model family based on the prediction target, not a default toolkit.

We scope exactly which fields the model can access, anonymize or hash identifiers where the prediction doesn't require raw PII, and can train and host inside your own cloud environment when compliance or data-residency requirements call for it.

We hold out a slice of real data the model never trains on and test performance against it, simulating how the model will behave on new, unseen cases. We also run explainability checks so you can see which signals are driving predictions, not just the accuracy score.

Model accuracy degrades as real-world patterns shift — this is called drift. Every model we deploy ships with drift monitoring that tracks prediction quality against live outcomes, so we know when performance is slipping and retrain before it becomes a problem you notice first.

Depending on how the prediction is used, we deploy as a real-time API endpoint your app calls, a batch job that scores a table nightly, or a feed into your warehouse or BI tool. We build against your existing data infrastructure — Postgres, Snowflake, BigQuery, and common event pipelines are all common integration points.

If a generic tool already solves your exact problem well, we'll tell you to buy it — we don't build what doesn't need building. Custom models earn their cost when your data, product, or fraud patterns are specific enough that an off-the-shelf model underperforms or can't see the signals that matter to you.

Cost depends on data readiness, model complexity, and how many prediction targets are in scope. We give a fixed project quote after reviewing your data during discovery, plus hosting and any ongoing retraining costs if you keep us on retainer.

Yes — drift monitoring, periodic retraining, and accuracy review are typically handled through a monthly retainer so the model keeps performing as your business and data evolve. We can also hand off a documented pipeline if you'd rather retrain in-house.

We're a startup-focused team that ties every model to a business metric you already track, validates on holdout data before anything ships, and stays on for the drift monitoring most vendors treat as an afterthought. You get a model your team trusts enough to actually act on, not a report that sits in a slide deck.

Book a scoping call — we'll review what data you have, define the prediction target and success metric, and give you a fixed-scope proposal and timeline within a few business days.