Tīrtha makes local models smart enough to handle everyday coding for pennies — by running and testing every answer instead of trusting it. Only the genuinely hard problems get escalated to the frontier-class models that cost real money. You get frontier-class accuracy, and you pay the premium price on just a fraction of your calls.
Engine live at api.tirtha.ai — pre-beta, invite-only.
A big model guesses and you hope it's right. Tīrtha doesn't guess — it runs the code. That one difference is what lets a small, local model do frontier-class work.
Most coding is everyday work — CRUD, glue, UI, fixes. A small local model handles it in a moment, for a fraction of a cent.
Tīrtha executes the code against tests before it trusts it. Checking beats guessing — this verification wall is the whole idea.
If the local model's answer fails the check — and only then — Tīrtha sends that problem to a frontier-class model. You pay the premium exactly where it's earned.
One honest note: a brand-new hard problem takes a moment longer, because Tīrtha runs and checks it before trusting the answer. Repeat and routine work comes straight back from cache — about 24× faster — and that share grows the more you use it.
The same checking-not-guessing engine gives developers and enterprises different wins. Pick your path.
OpenAI-compatible, verified output, instant on repeat work — at a discount to calling the frontier yourself. And the accuracy and speed climb the more you run it.
Explore the developer API → For enterprisePay the premium on only the hard minority of calls. The cost curve drops as your cache warms, while the pure-frontier line stays flat. The economics, spelled out — measured, not hand-waved.
See the economics →We're letting in developers and teams a few at a time while we harden the product. Tell us what you're building.