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 reports so then it picks, and nothing flags the wrong pick. Matterbeam hands it what it needs to know. Matterbeam automatically describes the sources you connect, continuously, with evidence and governance attached, and hands that to your agents before they answer.
You ask an agent: “what was revenue last quarter?”
It searches your systems and finds four columns that could answer. Nobody has told it which one finance reports, and there is no colleague to ask.
actually reports
The agent reported the wrong number as fact. No warning, no log entry, nothing for you to review. Matterbeam can show which column finance owns and actually feeds with lineage, shape, and historical usage. Your agent had no way to see that.
Data was shaped for people who could ask around. Agents can't.
Every ambiguity an agent resolves on its own is an unlogged coin flip inside your workflow. A wrong join isn't an error. It still returns rows.
Your data estate, as an agent finds it.
One workflow. Five dependent steps. Each one 90% reliable.
A coin flip with better manners. Demos are one step. Production is five.
We collect your data once, describe what it is, and 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 a model our profile of one CRM object. No records, no schema, no hints.
It's a Salesforce object change stream, not a table export.
The company sells digital services to flooring vendors and manufacturers.
Admin email domains shift in three phases — at least two corporate acquisitions, on these dates.
Test data is sitting in a production stream.
All correct, 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 where the agent's questions land.
It writes itself, and stays current
No army 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 trust.
Governed before the agent is in the loop
PII masked at origin, record-level filtering, tenant isolation, complete read logs. Not a filter bolted on at query time.
Computed once, served many
A lot of agent 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 correction that comes with them.
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.