AI agent built for prediction,
not guesswork.
Tell it what you want to predict. It learns from the rows you have already judged, scores every remaining one, and puts a confidence figure on each. In chat, or as a tool your agents call.
| company | owner age | years held | staff | state | sell intent | confidence |
|---|---|---|---|---|---|---|
| Northwind Mechanical | 63 | 27 | 41 | OH | high | 0.92 |
| Kestrel Heating | 67 | 31 | 24 | PA | high | 0.81 |
| Orchard Climate | 59 | 22 | 63 | NC | high | 0.64 |
| Vantage Air | 45 | 12 | 130 | FL | low | 0.90 |
| Aster Plumbing Co | 38 | 6 | 88 | TX | low | 0.87 |
How it works
Ask once. Every row scored.
You bring the judgement. It handles everything between the question and the answer.
Show it what good looks like
A handful of rows you have already judged. Fifty is often enough. That is your criterion, not a setting in a dropdown.
Ask in plain English
"Score these for fit." In chat, or as a tool call from your own agent. No column mapping, no config file.
Get every row back, scored
With a confidence figure on each one, so you know which rows to trust and which to check. Download it or pipe it onward.
The model
No training run, no tuning
Under the hood is a tabular foundation model, pre-trained on millions of synthetic tables. It already knows how columns tend to relate, so your examples go in as context rather than as a training set.
A prediction takes seconds instead of a sprint, and you can change your criteria and run it again without starting over.
Your criteria, not a fixed schema
Lead fit, risk flags, churn, priority, a quality grade. If you can label 50 rows, you can predict the rest.
Works on plain CSVs
Numbers, text categories, missing values. Columns are matched between the two files for you.
Runs on your machine
The app runs locally on a laptop CPU. Your data does not have to leave your network.
Where it fits
Anything you can label
Lead and account scoring
Label the accounts that closed. Score the rest of the list against the same bar.
Churn and renewal risk
Label who left and who stayed. Flag the accounts heading the same way.
Review triage
Label the cases that needed a look. Rank the queue by how likely each one is to matter.
Data quality flags
Label the rows you know are wrong. Find the ones that look like them.
Category fill-in
Tag part of a catalogue by hand. Let the model finish the rest.
Estimates and grades
Continuous targets work too: price, duration, a numeric quality score.