An enterprise intelligence platform that never sends your data out

Every enterprise has the same shape of problem. The answer to a leadership question exists, but it is spread across a CRM, a finance system, three databases, a warehouse and a shared drive, and reaching it means asking an analyst, who spends four days assembling it. By the time it arrives the question has moved on.

The obvious fix is to point a language model at the data. The obvious fix is also the one a CISO refuses, because it means enterprise records leaving the estate to be processed by somebody else's model. Matrix was scoped around that refusal: the same capability, with the data staying put.

What we did

A managed team accountable for a defined outcome, running the full loop from first understanding through to production and then onward into continuous improvement.

1. Scoping, one week. The hard boundary was established first: what may cross the perimeter, what may not, and what that rules out. Architecture follows that line, so it gets drawn before anything else.
2. Factory setup, week one. Environments, CI, evaluation harness and the delivery platform stood up before the first feature.
3. Build, weeks two to eighteen. Six certified engineers on three surfaces: Brain for querying across connected sources, Copilot for planning and document generation, and Agents for task execution. Agents in our own loop generated the bulk of the connector layer, which is mechanical and voluminous; the engineers held the query planner, the access-control model and the evaluation harness.
4. Hardening and rollout, weeks nineteen to twenty-two. Role-based access controls, compliance architecture, audit trails, and the iOS and Android clients.

What made the difference

Connectors are the tax on this kind of product. Salesforce, HubSpot, Stripe, NetSuite, Postgres, MySQL, MongoDB, Snowflake, BigQuery, Redshift, S3, Drive, SharePoint, and an internal API surface per customer. Written by hand that is most of a year. Generated from the specifications and then corrected by engineers, it was a fraction of the schedule, and it is the single clearest example of what changes when agents do the mechanical work.

What could not be delegated was the evaluation harness. A system that answers confidently and wrongly is worse than no system, so the measurement of answer quality was built before the features it measured, and it stayed a human responsibility throughout.

Where it landed

Matrix runs as a closed loop: natural-language querying, real-time analytics, planning and document generation, and agent task execution, with the customer's data inside the customer's infrastructure throughout.