Auto Prediction
An agent labels your rows.
You just check its work.
Labeling is where prediction projects die. Not the model, the afternoon nobody has for judging four hundred rows by hand. Auto Prediction puts an agent on that job, makes it show its reasoning, and lets you overrule it.
| company | label | why it said so |
|---|---|---|
| Northwind Ltd | worth a call | Hiring three ops roles, headcount up 40% in a year |
| Aster Health | not yet | Eight people, no ops function to sell into |
| Kestrel Robotics | worth a call | Two warehouses opened since March, stack matches |
| Vantage Print | not yet | Flat headcount, no hiring, legacy stack |
| company | headcount | open roles | stage | prediction | confidence |
|---|---|---|---|---|---|
| Halcyon Group | 310 | 7 | Series B | worth a call | 0.94 |
| Bellweather Co | 44 | 0 | Seed | not yet | 0.89 |
| Orchard Labs | 1,180 | 21 | Series D | worth a call | 0.86 |
| Merrow Freight | 96 | 2 | Bootstrapped | worth a call | 0.58 |
The part that was never automated
Labeling, done by an agent that shows its reasoning
Every prediction tool on the market starts by asking you for labeled examples. Nobody asks where those come from. They come from a person reading rows one at a time, which is why most prediction projects stall before the model is ever run.
An agent can read a row the way you would. Not just the numbers in the columns, but the filings, the job posts, the site. So we put it on the labeling job, and made it write down its reason for every single row. That reason is what makes the work checkable in minutes rather than hours.
Why the reasons matter
A label you cannot check is worthless
An agent that just hands back four hundred labels has moved the problem, not solved it. You would have to trust it blind or redo the work. The reason column is what turns it into something you can actually accept.
It reads more than the columns
Filings, job posts, the company site, whatever it collected in step one. It labels on the same evidence a person would use, not on four numbers in a spreadsheet.
One line of reasoning per row
Plain English, short enough to scan. Wrong reasoning is obvious at a glance even when the label happens to be right, which is the failure everyone else hides.
Your correction is final
Overrule it and that row is settled for good. It relabels the rest of the sample against what you just told it, so one fix moves everything like it.
The gap
The model was never the hard part
Ask anyone who has shipped a prediction where the months went. Almost none of it was the model. It went on finding data that was not in the table, arguing about what the label actually means, and hand-tagging rows until somebody lost patience and shipped a rule instead.
Auto Prediction takes all three jobs. You say what you want to know. It works out what it needs, goes and gets it, labels a sample, checks those labels with you, and scores everything else.
- Find the dataA list of names is not a dataset. It works out which signals would separate your outcomes and goes and collects them.
- Agree the labelYour criterion lives in your head. It writes a first draft, you correct it, and the corrected version is what gets used.
- Score the restA tabular foundation model reads your examples as context and scores every other row. Seconds, not a training run.
How it works
Say it once. Watch it work.
It finds the data
You have names and not much else. It decides which signals matter for your question and collects them from public pages, filings, job posts, review sites, and your own systems. Every field it adds carries its source.
It labels the examples
It reads a sample and labels each row against your criterion, with a one-line reason. You skim, correct what it got wrong, and your corrections outrank its guesses from then on.
It predicts the rest
Every remaining row scored, with a confidence figure on each. Download it, push it back to your CRM, or let your agent read it straight from the tool.
Control
Nothing happens off-screen
An automated pipeline you cannot inspect is a rumour with a progress bar. Every stage of a run is written down and open to you.
Change your mind about the criterion and run it again. It costs minutes, so you can afford to be picky about what a good row actually looks like.
Every field shows its source
Click any collected value and see the page, filing, or system it came from, and when it was read.
Every label shows its reason
One line per row, in plain English. If the reason is wrong, the label is wrong, and you can see it in a glance.
Your corrections win
A row you have judged is never overwritten by a row it guessed. Your edits define the criterion.
Where you ask
Ask from wherever you work
In chat
Describe the prediction in a sentence. It comes back with the run, the labels, and the scored rows.
From your agent
Over MCP, as a tool your agent calls mid-task. It gets a number and a confidence back, not a paragraph of prose.
In the browser
Watch a run as it happens, correct the labels by hand, and download the result.
Tell us what you want to know
Bring a question and a list of rows. We will show you what a run looks like on your own data.