Your data is scattered across systems. I turn it into intelligence your team can actually use.

Agents I built to carry the repeatable part of the work. I set what they look for and I check what they return — the judgment is not delegated.

Live agents run in production. Built agents are finished and deployable per engagement — they are not running until yours is. All 41 are in the agent catalog.

How I work.

These are the rules every deliverable is built on. They are also the reason the numbers survive contact with your own analyst.

1. 01

   Your systems don’t reconcile. I measure exactly how far off they are.

   Operations data and marketing data were never designed to talk to each other. Joining them cleanly — and measuring exactly how cleanly — is where customer intelligence starts. Most organizations have quietly accepted that the two halves of their business tell different stories.

2. 02

   Identity comes out on the way in — unless the work needs it back.

   Personal data is stripped at ingest by default, and everything downstream runs on hashed keys and aggregates. Where the deliverable is an addressable audience, identity is re-joined at the last step and only there, with column-level encryption on anything sensitive at rest. You hold identity where the work requires it and nowhere else.

3. 03

   A rate without a denominator isn’t a number.

   “Underperforming” means nothing until you know capacity. Where the denominator doesn’t exist, I’ll tell you the figure is relative and stop. Where it can be found, the same analysis turns a directional hunch into something you can act on.

4. 04

   Anything that drives a recommendation gets verified against source.

   A clean finding that dies under your analyst’s scrutiny costs more than it was worth. I recompute before I claim — and when something doesn’t hold up, you hear that. It is a stronger position than the finding would have been.

5. 05

   I won’t approximate to fill a gap.

   Where your data can’t support a measure, you get precision about the gap instead: what’s missing, why it matters, what would close it. Those gaps routinely become the sharpest questions an organization has about its own systems.

6. 06

   “Unknown” and “zero” are different facts.

   Conflating them produces confident wrong answers. Unknown stays unknown, every field carries its coverage, and uncertainty arrives as a stated range — not a hidden assumption.

7. 07

   Column names lie. Values don’t.

   Flags that govern nothing. Status fields that mean something other than their label. Indicators that exclude most of what they claim to describe. I classify by inspecting what is actually there, which is why the findings hold.

8. 08

   A control nobody has attacked is a control nobody has tested.

   Privacy floors, validation rules, suppression thresholds — every one gets adversarial review, because a passing test only proves the test passed. That process has caught things no deterministic check could see.

9. 09

   Correctness is built in, not remembered.

   Constraints that make bad data impossible to insert. Tooling that refuses to publish anything diverging from what was verified. Discipline drifts. Enforcement doesn’t.

10. 10

    Aggregate with a floor, and test the arithmetic around it.

    No group below the threshold is ever visible — and the harder work is confirming that published figures can’t be decomposed back below it. Suppression that survives only a mechanical check isn’t suppression.

11. 11

    What I deliver explains itself.

    No walkthrough required. Every view says what it shows, how to read it, and what it can’t tell you — because the person who forwards it isn’t the person who commissioned it.

12. 12

    I find my own mistakes first.

    That is what makes the rest of the numbers worth trusting.