Custom models that forecast demand, score leads, predict churn, read documents and power recommendations — built on your data and deployed where your team actually works.
If you're collecting data but not acting on it, this is for you.
Years of records, spreadsheets and CRM history that nobody's turned into a decision.
Stocking, staffing, lead prioritization or risk calls made on gut feel, over and over.
A prior ML project that never made it to production, or broke and nobody fixed it.
Predict demand, revenue, inventory and staffing so you plan on data, not gut feel.
Flag the customers about to leave — with reasons — in time to save them.
Rank leads and accounts by likelihood to convert so effort goes where it pays.
Detect defects, count objects, read images and automate visual inspection.
Extract fields from invoices, forms and contracts — no manual entry.
Copilots and search over your own knowledge base, grounded in your data.
We ship models to production and wire them into the tools your team already uses.
Every model maps to an action — what to stock, who to call, what to flag.
We monitor for drift, retrain on schedule, and alert when accuracy slips.
Reason codes and feature importance so your team trusts and acts on the output.
Snowflake, BigQuery, Postgres, spreadsheets — we build the pipelines around it.
Access controls, PII handling and audit trails built in from day one.
We assess your data, the decision it should drive, and whether ML is the right tool — honestly.
We build pipelines, clean the data, and set a baseline to beat.
We train, test against holdout data, and prove lift before anything ships.
We put it in production, wire it into your workflow, and run the MLOps to keep it sharp.
Often yes — more than teams expect. On the strategy call we assess volume, quality and history, and tell you honestly whether a model makes sense or whether simpler analytics would serve you better.
Yes. We work with your Snowflake, BigQuery, Postgres, spreadsheets or wherever your data lives — and build the pipelines to keep it model-ready.
We monitor for drift, retrain on a schedule, and alert when performance drops. A model isn't a one-off — we run the MLOps around it.
Yes. We favor explainable models and ship feature-importance and reason codes so your team trusts and can act on the output.
Yes — document understanding, summarization, RAG over your knowledge base and custom copilots, alongside classic predictive ML.
They helped us streamline data, track key metrics and launch campaigns, giving us the clarity we needed to grow with confidence.
Book a data strategy call — we'll look at what you have and tell you honestly what a model can and can't do for it.