Most "AI decisions" in production aren't open-ended generation. They're classification in disguise:
- which team should handle this ticket?
- is this e-mail phishing?
- what's the total on this invoice?
- which sentence in the contract supports that answer?
Teams usually send each of these to a large LLM: 1–2 seconds and a bill per call, a JSON answer to parse, and a "confidence" that means nothing.
We built THX-01 for exactly these decisions. Today we're making its hosted API free.
Try it now (no key, no sign-up)
curl https://api.hal-x.ai/v1/systemone \ -H "Content-Type: application/json" \ -d '{ "state": "Hi, I was charged twice for one order and nobody answers. Refund me!", "questions": { "team": {"type": "choice", "question": "Which team handles this?", "criteria": {"billing": "billing", "tech": "technical", "sales": "sales"}}, "refund": {"type": "noul", "question": "Does the customer want money back?"}, "urgency":{"type": "score", "question": "How urgent?", "criteria": ["low", "medium", "high"]} } }' Every answer comes back with a probability for each option, computed in a single forward pass of about 10 ms.
The free tier allows 200 decisions per minute per IP. One question is one decision.
What it can answer
| type | returns |
|---|---|
choice | one option, with a probability for every option |
noul | P(yes) |
score | a level on an ordinal scale |
number | a value stated in the document, or null if it isn't there |
excerpt | a verbatim span with character offsets |
"cite": true | the passages that support any answer |
How it compares
On our 2,843-ticket benchmark (Azerbaijani, Russian, English, Turkish; clean, corrupted, messy and independently written sets):
| model | avg accuracy | latency |
|---|---|---|
| THX-01 (322M, open) | 98.4 | ~10 ms |
| TypeSafe Jev 1.13 | 97.4 | 331 ms |
| Kev-4B | 92.8 | 830 ms |
The model's confidence is calibrated, so a single threshold lets it handle about 75% of tickets automatically with zero errors on all four test sets.
Run it yourself
The weights are Apache 2.0:
pip install thx01 import thx01 agent = thx01.load("doofz/THX-01") agent.decide("My card was charged twice", { "team": {"type": "choice", "question": "Which team?", "criteria": {"billing": "billing", "tech": "technical"}}}) It runs on a laptop CPU in a fraction of a second.
