Before the AI could explain anything,
the numbers had to match to the decimal.

Years of consumer research — locked in files and spreadsheets.

A consumer-research firm runs large brand-health and usage studies for consumer brands: thousands of respondents per wave. Every new client question meant analysts going back into the data and the statistics package by hand.

The firm wanted a platform its clients could simply ask, with one condition that mattered more than any feature: the platform's numbers had to match the firm's existing statistical output exactly.

Prove parity before promising anything.

Rather than lead with a demo, we started with the hardest test. Correspondence analysis — the technique behind brand-imagery maps — is notoriously sensitive to the scaling conventions each tool uses. Our engine matched a published benchmark to six decimal places.

We then ran the firm's own client files — a binary logistic regression and a linear regression — and reproduced their existing statistical output. Where one goodness-of-fit measure differed, we traced it to a documented difference in convention between tools, not an error, and explained exactly why.

• The calculator computes every figure with ordinary, verifiable mathematics.
• The detective finds where in the funnel consumers are lost.
• The explainer writes the finding in plain language — and only ever describes numbers the calculator produced.

The questions brand teams ask every wave — answered by one engine.

The proof of concept takes the questions brand teams ask every wave: category penetration, preference-to-purchase conversion, the brand-health funnel, imagery versus the category leader, the consumer profile, and usage.

Each one is answered on demand, in plain English, with a chart and the source of every figure — and with the same numbers the firm would have produced by hand.