the decision layer for software
Your app makes ten thousand judgment calls a day.Stop renting them by the token.
nirnaya answers typed questions about whatever your software is holding and returns a calibrated probability for every option. No prompt, no parsing, no tokens generated. It runs inside the browser tab your user already has open. Below, it is doing exactly that, in yours.
A one-time download; your browser keeps it after this.
Real cases from the public benchmark, decided by the model in your browser as you watch.
Numbers that sound made up
Every one of them is measured. The last one is counting right now.
Not a claim: a counter. Everything the model decides on this site, in this tab, lands here. Click the model pill at the top right for the full session.
Speed
Done before the others have started typing
A language model writes its answer one token at a time. nirnaya does not write: it reads the state once, scores every option, and hands back numbers. This race runs at real speed.
Per decision, played at real speed. Ours is this tab's own average once it has decided something. Hosted models: Ably's published measurements of the same decision loop.
Cost
Too cheap to meter
No charge per input token, no charge per output token, because there is no meter to read. The model ships to the browser once, caches, and every decision after that costs nothing.
Since you opened this page, at 10 decisions a second:
0 decisions so far. Frontier estimate: ~1,500 input and ~200 output tokens per five-question case, at published list prices.
How one decision happens
No generation, no sampling, no retries. The options you define are read as text and scored against the state.
You describe the choice
A state - a thread, an alert, a diff, anything - plus your options, each with a sentence saying what it means.
It reads everything once
The state is encoded a single time. Every option is scored against it independently, so their order cannot matter.
You get numbers to branch on
A probability per option and a confidence you can threshold. Nothing to parse, nothing that can come back malformed.
Three things a text model cannot give you
The answer is already a type
You define the options; you get a probability for each. No JSON to validate, no retry on a malformed response, no regex. A wrong answer is possible. A malformed one is not.
Confidence you can branch on
Every answer carries a calibrated number. Automate above your threshold, escalate below it, and set that line against your own traffic. A product decision, not a prompt.
Order cannot change the answer
Each option is scored against the state on its own, so shuffling your options changes nothing. Measured flip rate: 0.0000. Ask a text model the same question with the list reordered.
24 apps, one model, no server
Recordings of the real thing. Every jump, verdict and move was decided in a browser tab.
Ship it this afternoon
Complete request shapes and the routing logic to act on them.
Support ticket triage
Route, prioritise and decide whether a human is needed, in one call.
securitySecurity alert disposition
Decide what an alert deserves before an analyst spends a minute on it.
financeInvoice approval
Approve, hold or reject supplier invoices against the PO and the history.
agentsAgent trace guardrail
Watch an autonomous agent and decide whether to let it continue.
Measured on a public benchmark, not on a slide
Four hundred real cases. Two thousand decisions. Gold answers from a panel of sixty human annotators, on an open dataset anyone can download and re-run. Every figure on this site came off that benchmark, next to the published results of the alternatives.