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Use cases

Certification first. Then everything it makes possible.

The first case is the one we are built around: two systems disagreeing about one number before sign-off. The rest are what the same governed sources and ceilings do once they are connected, for the teams next door to finance.

B2B SaaS, FP&A

One number you can defend

Declare the CRM view and the finance view of one line as two governed metrics. Insighter watches them on your cadence and only interrupts when they drift past what you call noise.

Q3 commit: the CRM says 4.0M, the model says 3.0M. Which one stands?

They are 25 percent apart, which is past your 2 percent tolerance. Your rule says sales runs hot beyond 10 percent, so it proposes the finance figure. Last quarter you went the other way: two slipped deals came back in writing, and the CRM was right. Both queries and their definition versions are attached.

CRM commit
Finance model
Tolerance
Illustrative conversation. On your data every answer carries sources you can open, and every query is read-only.
  1. AnsweredBoth numbers side by side, each traced to a definition version and how fresh the table under it is
  2. HeldThe rule proposed the finance figure. Nothing was preselected, and a person certified with a reason
  3. WatchingThe certified figure is frozen with its query. If the data moves underneath, it says so and says what changed

Minutes, and it replaced the meeting that used to settle this.

And then it keeps running

“Compare the CRM commit against the finance model for the current quarter. Anything past 2 percent raises an exception to the FP&A lead with both queries, the rule that applies, and what was decided last time.”

Every night, and before every closeCeiling: read only, and a person certifies
Connects toSalesforce or HubSpotPostgreSQLGoogle Sheets
Doing the workGoverned metricsCertificationRestatement
Try it in the demoRun it on your dataRead the full case

Where it sits

Keep the stack. This goes after it.

Monte Carlo tells you the pipeline broke. dbt tests tell you the data failed its checks. Power BI shows the number. Insighter sits downstream of all three: it remembers what was reported from that stack, says when it stops being true, and does the follow-up. Nothing gets ripped out.

What holds it together

The answer is the easy part. The follow-up is not.

Every scenario above ends the same way: someone decides something. Insighter records that decision behind the summary you asked for, then uses the data to answer whether it was implemented and whether it worked.

Agreed, happened, and worked are three questions
A decision can be approved, never enacted, and therefore have no effect. Reporting that as one status is how a memory system starts lying, so Insighter keeps three.
Nobody fills in a form
Records are created from the analysis you were already doing. A capture step people have to remember is a capture step that does not happen.
Unimplemented decisions go quiet
Monitoring the assumptions behind a policy nobody applied would generate confident warnings about a fiction, and teach everyone to ignore alerts.

Consolidate the night shift at North hub

Recorded 3 March from the answer you asked for

Agreed

approved

Happened

not detected

Worked

inconclusive

night_shift_countflat
utilisationflat

The leading number never moved, so this was probably never enacted. Its alerts stay quiet: warning you about a policy nobody applied is how a system teaches you to ignore it.

A real decision record, six weeks after it was made.

Your data

Never used to train a model. Never sold. AES-256 at rest under a key derived per organization, so no other tenant's key opens yours, and no standing internal access to any of it.

How it is enforced

Yours is probably on the list

And if it is not, bring the question anyway. Answering it against your own data is usually enough to know.

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