Meet your
AI Data Squad.
A whole data team, end to end. Any database, any platform, deployed in your environment.
Watch it answer one question.
This demo shows the analysis half of the squad. The architecture, pipeline and engineering work described below runs the same way. We will walk you through it live rather than claim a demo that is not built yet.
Four things a data team struggles to do.
Two teams, two definitions of revenue. It writes the metric down once and applies it everywhere.
The follow-up takes 90 seconds, not another week in the queue.
It checks lineage and freshness before answering, not after someone questions the result.
It knows what depends on the model it is changing. The analyst who built it left and took that with them.
The work of a whole data team.
A dozen specialisms, one standard, lineage attached.
Where a judgement is yours rather than the agent’s, such as which definition of revenue is correct or which exceptions to include, see what it cannot do.
What it cannot do.
Real limits, and who is needed.
Whether revenue is recognised on booking or on delivery, whether an intercompany transfer counts. These are business definitions with real consequences. It will surface the conflict and force the decision, but it will not make it for you.
If the source system never recorded a field, no amount of modelling recovers it. It will tell you plainly what is missing and what that means for the answer, rather than interpolating and hoping.
The records that fail validation, the duplicates, the accounts nobody owns. It finds them and lists them. Deciding what happens to them needs someone who remembers why they look like that.
Warehouse, source systems and BI tooling all need credentials issued by someone with the rights to grant them.
It produces the definitions, the lineage and the training. Getting teams to stop keeping their own spreadsheet is human work, and it is usually what decides whether a data programme succeeds.
Guardrails and safety features.
Human in the loop on review, every definition decision, and publishing.
The agent is deployed inside your own platform and works there. Nothing is stored or processed outside your control. There is no Clipeum system holding your records, and no copy of your warehouse leaves it.
If a figure does not tie back to the system of record, it says so in the answer instead of shipping it to a dashboard and letting someone else find out.
Changing what a metric means changes every report built on it. Those changes are proposed, recorded, and applied only once someone with the authority agrees.
When a quality check fails, the run stops and you see the failure. A pipeline that silently drops bad rows is worse than one that breaks loudly.
Models and pipelines are versioned, and any change can be reverted.
An always-on sales team. It works your pipeline around the clock, answers inbound the minute it lands, and books the meeting while nobody is awake.
Bring us the question nobody can answer.
The metric two teams disagree on, the report that never ties out, the pipeline nobody will touch. We will work it live against your real data and show you what the first two weeks would look like.