Classify tickets, orders and documents with a model trained on your data. More accurate than , the market's reference model, it runs on your servers and never bills per use.
“Does my PIN come with my card?”

It gets your categories, your terms and your way of deciding wrong.

Millions of decisions a month become a bill that only grows.

Every question sends your customers' data to a third party.
No code, no GPU and no machine learning team.

Send your spreadsheets and files, or start from a public dataset. Messy is fine.

Pick a base model, prepared by our lab to serve all kinds of businesses.

Our agent organizes your data into standardized datasets, enriches it with synthetic data and public datasets, and anonymizes sensitive data. You review it and, with one click, training starts.

Get your production-ready AI model, trained on your data and tailored to your tasks, beating market models or your credit back.
points above in banking support, over 1,001 tests.
Public datasets run through the product the way a customer would: 20% of each held out for testing and measured on the same examples.
| Benchmark | typestrict | Jev | Gain over Jev | 95% CI |
|---|---|---|---|---|
| Banking supportBANKING77 · 1,001 tests | 0.915 | 0.798 | +11.8 | |
| InsuranceBitext Insurance · 802 tests | 0.991 | 0.896 | +9.5 | |
| Financial newsFinancial news · 804 tests | 0.824 | 0.743 | +8.1 | |
| TelecomBitext Telco · 806 tests | 0.991 | 0.911 | +8.0 | |
| MortgagesBitext Mortgage · 814 tests | 0.987 | 0.920 | +6.7 | |
| TravelBitext Travel · 792 tests | 0.984 | 0.952 | +3.2 | |
| Retail bankingBitext Retail Banking · 806 tests | 0.990 | 0.963 | +2.7 |
Across the 15 tasks we ran: 7 wins, 5 ties and 3 losses to . See them all on the blog
You pay once, run it wherever you want and keep owning everything.
Pay once. A million decisions cost the same as one.
About 165 MB, answers in milliseconds on a regular CPU.
Container, Cloud Run, AWS Lambda, Firebase, browser or phone.
The file is yours and keeps running even without typestrict.
Sensitive data is masked before training, and the original is deleted.
Every base model and public dataset we use allows commercial use.
No subscription and no GPU bill. Credits never expire.
In a pack, each model costs less.
Beats on your data or your credit back. Measured on 20% of your data held out for testing.
The starting point of your model: a small AI our lab prepared to decide (classify, prioritize, route). In training, it learns your company's way. The technical name is encoder.
It reads your files as they come, builds a standardized dataset, fills in with public and synthetic data and masks personal data. You check it before training.
A file with your model in the open ONNX format, which runs from Python, JavaScript, Java, C# and on phones, plus the performance report.
Like any service: your system sends a text and gets the decision back as JSON, with the probability of each option.
Wherever you already run your systems: a regular container, Cloud Run, AWS Lambda or Firebase Functions, your own server, the browser or a phone. No GPU.
Start with a spreadsheet. The agent handles the rest.