Intelligence

Retrieval over your documents, knowledge base or database, with an LLM answering from what it finds.

Tool-using agents scoped to one job, with the boundaries and the audit trail decided up front.

AI added to software a business already runs — extraction, classification, summaries, reports — behind a real endpoint, with evaluation and cost accounting, and no rebuild of what works.

Models trained on your own data where a rule would not hold — classification, scoring, forecasting — with the evaluation that says whether it beat the rule.

Model ensembles per instrument, retrained on a schedule against a versioned feature store.

Orderbook and gamma monitors that archive every observation, not just the interesting ones.

Matchup models over a multi-season archive, with odds collection running beside them.

Dashboards and scheduled reporting fed by the pipeline itself, so the number on screen has a lineage.

Ingestion, stream processing and schema design for data that has to survive being queried a year later.

An honest read on whether the model is worth building, before anyone commits a quarter to it.