Someone spends hours every week copying numbers into a status report — and the figures still don't match. We define the source-of-truth fields and the cadence, and AI assembles the draft with traceability for every number. A person edits and approves.
Common pain points
The weekly report is assembled by hand from scattered tools, so it costs hours and the cost repeats every cycle.
No source-of-truth is agreed per field, so two people report the same metric differently and nobody knows which is right.
The numbers are copied without a trail, so when one looks wrong there is no way to check where it came from.
When the person who builds the report is away, the report does not happen, because the method lives only with them.
There is no line for what the report should and should not include, so it grows until nobody reads past the first page.
Off-the-shelf automation pulls numbers without judgment, so it produces a report nobody trusts and the team goes back to doing it by hand.
An operational reporting system defines who the report is for, which source owns each field, on what cadence, and who owns it. AI assembles the draft from your connected tools with a trail for every number and flags anything missing or stale. A person edits and approves, and what AI may not infer — a number it cannot trace — is written down. AI assembles; a person signs off.
How we work
We define the report spec with you: the audience, the fields, the source that owns each field, the cadence, and the owner.
We connect AI to the source tools to assemble the draft with a trail per number, and to flag inputs that are missing or stale.
We set the approval gate and the rule that AI does not invent numbers, and hand over a runbook the team operates and edits.
What we build
Area 1
We write the spec the report is built to: who it is for, the fields it carries, how often it goes out, and the named person who owns it. The spec is agreed before any automation, so the report answers a real question for a real audience instead of growing into a wall of numbers.
Deliverable
A report spec: the audience, the fields, the cadence, and the named owner.
Area 2
We connect AI to the source tools and have it assemble the draft — each number carrying a trail back to where it came from. The trail is the point: when a figure looks wrong, anyone can check the source in one step instead of trusting it or rebuilding the report.
Deliverable
An AI-assembly flow where every number in the draft links back to its source.
Area 3
AI assembles the draft; a named person edits and approves before it goes out. The rule is explicit: AI may not infer or estimate a number it cannot trace to a source — if the data is missing, it says so rather than filling the gap. Approval is a person's job, not a default.
Deliverable
An approval gate with a named sign-off and the written rule that AI never infers a number it cannot trace.
Area 4
We map, field by field, which tool is the source of truth — so the same metric is never pulled two different ways. Where two tools disagree, we decide with you which one the report trusts, and write it down. The map is what makes the numbers consistent week to week.
Deliverable
A source-of-truth map: per field, the single tool that owns it and is trusted for the report.
Area 5
When a source has not updated, or a field is missing, the report should say so — not quietly carry a stale number. We define how the assembly flags missing or stale inputs so the person approving knows exactly what is solid and what to check before it goes out.
Deliverable
A flagging rule: how the draft marks missing or stale inputs so the approver sees them before sign-off.
Area 6
We hand the system over as a runbook: the spec, the source-of-truth map, the assembly flow, the approval gate, and the flagging rule. The goal is for the report to run without us — the team can adjust the spec when the audience changes and bring a new owner up to speed without rebuilding it.
Deliverable
A runbook with the spec, the source-of-truth map, the assembly and approval flow, and the flagging rule the team owns.
The weekly report assembles itself from the source, so the hours that went into rebuilding it by hand go back to the team.
Every number carries a trail to its source, so a figure that looks wrong can be checked in one step instead of trusted blindly.
A person edits and approves before the report goes out, and AI never invents a number it cannot trace.
Each field has one source of truth, so two reports of the same week stop disagreeing.
Missing or stale data is flagged, not buried, so nobody signs off a report on numbers that quietly went out of date.
The spec, the map, and the flow live in a runbook the team operates and edits, so the report survives the person who used to build it being away.
No. We do not build a BI stack or a data warehouse. This is the spec, the source-of-truth map, the assembly flow, and the approval gate — on the tools you already use. If you genuinely need a warehouse or a BI platform, we will say so and point you to the right partner instead of selling you a reporting system that cannot fix a data problem.
Demand-gen reporting is about the marketing and sales funnel — leads, channels, pipeline contribution. This is horizontal operational and status reporting — the weekly team status, delivery, and operational metrics, from a different set of sources for a different audience. They sit next to each other on purpose; we will tell you which one you actually need.
No. AI assembles each number with a trail back to its source, and the rule is explicit that it may not infer or estimate a figure it cannot trace. If a source is missing or stale, the draft flags it rather than filling the gap. A person approves the report; AI never signs it off.
A named person. AI does the assembly — pulling, formatting, flagging — and a person edits and approves before anything goes out. The sign-off is a human judgment about whether the report is right and ready, not a step AI is allowed to skip.
By default, no. We connect AI to the sources you already have and assemble the report from them. If the diagnostic shows the real problem is that the underlying data is a mess, we will tell you — that is a CRM data hygiene or data-foundation job first, not a reporting system.
It does not include a BI or data-warehouse build, marketing and sales funnel reporting (that is demand-gen reporting), a guarantee about your operational numbers, or an AI that approves the report. It also does not include fixing the underlying data quality. If what you need is one of those, we will tell you and point you to the right partner.
Decisions get made in Slack and email, never written down, then reopened weeks later. We define what counts as a decision and where the log lives — and AI dr…
Read moreGrowth operationsA demand-gen report that just lists metrics drives no decisions. We design the report your team acts on — and AI assembles the draft from your sources with t…
Read moreGrowth operationsA CRM becomes useless when nobody knows which fields matter, who owns them, or when they're reviewed. We set the rules and the cleaning cadence — and AI flag…
Read moreBook a 30-minute call to look at how your status report gets built today. We will talk through who it is for, which sources own which numbers, and what it would take for the report to assemble itself with a person signing it off. If the real problem is the underlying data rather than the report, we will tell you and point you to the work that fits.
Book a 30-min call