You onboarded an AI agent.
It has prod access and no idea what your data means.
It can't ask which table is current, or which of four revenue columns finance is using - so it picks one, and nothing detects that mistake. Matterbeam gives agents what they need to know: Matterbeam automatically describes the sources you connect, continuously, with evidence and governance attached, and makes that available to your agents before they answer.
You ask an agent: “what was revenue last quarter?”
It searches your systems and finds four columns that might be relevant. Nobody told it which one finance uses in their reports, and there is no colleague to ask.
actually reports
The agent confidently reported the wrong number. No warning, no log entry, nothing for you to review. Matterbeam can show which column finance owns and actually uses, along with lineage, shape, and historical activity. Your agent had no way to see that before.
Data was shaped for people who could ask around. Agents can't.
Every ambiguity an agent resolves on its own is an silent "coin flip" inside your workflow. Joining the wrong tables isn't an error: it still returns rows, just the wrong ones.
Your data, as an agent finds it.
One workflow. Five dependent steps. Each one 90% reliable.
A "coin flip" that compounds. Demos are one step. Production is five.
We continuously collect data, profile it, and efficiently serve each agent its own governed view.
We connect once to each source and continuously collect into an immutable, replayable log, then stream governed views back out to wherever they're needed — each agent gets its own, and the same log just as easily feeds a warehouse or app. Because the data flows through us, we never send crawlers out to sample your systems and guess what they hold; we watch it in motion, so the description is a byproduct of collecting it, not a documentation project running alongside. It runs beside your existing stack, one source at a time — you don't rip anything out to start.
Everything you hold
CRM, ERP, databases, SaaS, files, the internal app nobody owns.
Immutable fact log
Collected once, stored as it arrived, replayable to any point in history.
Profile and inference
Shape, cardinality, drift, lineage, likely PII. Every claim carries its evidence.
Governed views, per consumer
Each agent gets its own; the same log just as easily feeds a warehouse or app. Masked at origin, filtered per record, every read logged. Computed once, reused.
We handed our profile of a CRM object to an AI model. No sample records, no explicit schema, no hints. It understood a lot more about what the data means.
It's a Salesforce object change stream, not a table export.
The company sells digital services to flooring vendors and manufacturers.
Admin email domains shifted in three phases — at least two corporate acquisitions, on these dates.
Test data is present in this production stream.
All true, including the acquisitions and the test records nobody had mentioned. A warehouse will tell you a column has 4,000 distinct values - it won't tell you the data is lying to you.
Five things a context layer has to do.
Every source, not the modeled slice
Coverage extends to raw and operational data uniformly, because that's often where agent's questions land.
It writes itself, and stays current
No army of data analysts hand-authoring definitions. A description that needs human maintenance always lags the data.
Every claim carries its evidence
Assertions arrive with the profile that supports them and soon, counter-checks that try to break them, so an agent can weigh instead of assume.
Governed before the agent is in the loop
PII masked at origin, record-level filtering, tenant isolation, complete read logs. Not just a filter bolted on at query time.
Computed once, served many
A lot of token spend goes to rediscovering your schema on every run. Served from the log instead of re-inferred each time, repeated reads stop paying that tax — meaningfully cheaper in our early testing, and we'll measure it against your workload rather than quote you ours.
A warehouse only describes what's been loaded into it — a single destination, shaped for one kind of question, with a meter attached.
We feed warehouses. But one destination, targeting a single use, can't help you understand all of the data across your organization.
We're looking for design partners to continue building this with us.
You pay a pilot fee, get early access and direct influence over what we build, and a running start on a problem you already have. We get real requirements, and the direction that comes with solving real problems.
Phase one runs on non-production or masked data, in a dedicated cloud account. We're not SOC 2 certified yet, and we'd rather say so up front.