Most businesses are sitting on years of data they’ve never looked at.
It’s in the till system, the shop platform, the accounts package. Nobody has the time, and every proper tool assumes an analyst you don’t employ.
So decisions get made on impression
Trade’s going well. We’re busier than last year. Sometimes that’s right. Often it’s wrong in a way that quietly costs money for years.
What if you could just ask?
The analyst you were never going to hire.
Ask questions of your own data in plain English. Every number computed against your real figures. Every finding labelled fact, projection or judgment. Nothing acts until you approve it.
In plain English, about your own business
“What were my top countries last year?” “How many customers came back?” “Which accounts have gone quiet?”
It builds the analysis, runs it against your real data, charts it, and explains what it means. Pin what matters to your dashboard; the rest is one question away. Or build an analysis yourself, point and click: what you’re measuring, broken down how, over what period.
This is a live dashboard from a real business, which is exactly why the figures are pixelated. Their numbers are theirs. Yours would be yours.
How an answer is made
The engine computes. The AI explains
The AI never does the arithmetic. That sounds like a technical detail. It's the whole product.
Which of my customers are worth a different kind of attention?
Computed against your orders, customers and revenue. Not estimated.
A small number of trade accounts produce about a quarter of your revenue.
Month to month, revenue isn't reliably predictable. Here is the trend instead.
These accounts deserve a different kind of attention.
Draft a check-in note to each trade account.
Awaiting your approvalAsk most AI tools about your sales and you get a fluent, confident, plausible number, produced by a language model, which is to say invented. Here, every figure that reaches you came out of a real computation against your real data. The AI decides what to work out and explains what it means. Strip out the computation and it produces no figures at all, rather than making some up.
Every number is real. The AI explains
It forecasts when the data earns it
Before projecting anything forward, it tests itself against your own history to see how well it actually predicts. Then it reports at the highest confidence the data supports.
A precise forecast
When your history supports one, and it can show you how accurate it has been.
A directional signal
When it can say which way, but not how far. Up, down, or holding, with the reason.
The facts and the trend instead
When month to month isn’t reliably predictable, it says so, and shows what it can stand behind.
When the forecasting was built, it was tested against several years of a real business's trading history and concluded that revenue was not reliably predictable month to month. Real businesses are lumpy: one large account going quiet for a month looks like a collapse. A lesser product draws the confident line anyway. This one reported the trend, the seasonality and its own measured accuracy, and said a confident forecast wasn't warranted.
It keeps looking, and proposes what to do
It works on its own
Spots who's slipping away
Proposes, never acts
Watches for things
Reports on a schedule
Bring whatever you've got
Analysis runs against your own data, brought in per deployment. It works with what your business already has, whatever it runs on.
A quarter of the revenue, from a handful of accounts
One business using it discovered that a very small number of trade accounts were producing roughly a quarter of their revenue. A different kind of customer, deserving a different kind of attention, sitting in plain sight for years.
Your data has been trying to tell you something