We think the reporting layer was the wrong place to solve this.
Every revenue team already owns the data that explains why the quarter went the way it did. It is scattered across ten tools, and answering "why" costs a week of spreadsheet archaeology. Intelegit exists to close that gap — not with another dashboard, but with a system that states the finding, proves it, and acts on it with approval.
Four commitments, and what they cost us.
Every number the product states carries the records it was derived from. If a claim can't be traced to source data, it doesn't ship — internally or to a customer.
Autonomy is a dial the customer sets, not a feature we turn on for them. The safe, reversible, boring path is the one that runs unattended.
No customer data trains a foundation model. Tenant isolation is architectural, not a configuration flag someone can get wrong.
Revenue teams plan quarters around this system. Predictable beats clever: idempotent execution, retries until reconciled, and failure modes we have already rehearsed.
Data first, then intelligence, then execution.
Each layer only works because the one under it is trustworthy. Built in that order deliberately.
- FoundationOne normalized revenue record
The data layer came first: CRM, email, calendar, calls, support and billing resolved to one customer, one deal, one truth — with a data-quality engine that surfaces what the CRM is getting wrong.
- IntelligenceForecasts you can defend
Pipeline, deal risk and forecast variance computed on each customer's own history rather than a rep's optimism, with the variance explained deal by deal.
- ExecutionApproval-gated action
Recommendations became approvable actions with a per-action-type autonomy dial, a full audit log, and reversibility as a design constraint.
- NowSeventeen modules on one core
Adopt them independently, on the stack you already run. Customer Experience is live; the rest ship on the same brain and the same evidence standard.
Small, senior, and on the rollout with you.
Built revenue tooling and data infrastructure for fintech and B2B SaaS before deciding the reporting layer was the wrong place to solve the problem.
Named engineers who own a rollout end to end — connector mapping, data-quality triage, pilot design and the first automations that go live.
Builds and grades the forecasting and risk models against customer history, and decides the confidence threshold below which the system says nothing.
Thirty minutes on your data is worth more than any deck we could send.