Lower AI costs.
Raised standards.
Frontier models are expensive. Smaller models don't always get it right. With Kadari, there is no compromise: verifiable proof that optimizes costs and reduces risk.
We prove it
before we count it.
Kadari is a verification layer for your LLM spend: a deterministic judge proves a smaller model would have matched your frontier model's answer — on your own quality bar — so you lower AI costs with evidence, not promises.
Three numbers you can act on: what you spend today, what the identical answer costs for less, and what the difference becomes at scale.
Same result, a smaller bill — measured, not guessed.
Proof, not promises
We don't estimate a saving and ask you to believe it. We prove a smaller model would have matched the answer you'd get today — don't commit to promises, commit to the proof.
Track every decision
Customer support is one example of the work Kadari can prove. For every real answer, Kadari's judge confirms whether a smaller model would have produced an equivalent response and flags the ones it can't. If we can't back a number, we refuse to take the risk.
Replay any decision
Pull up any request and see exactly how the judge ruled. The same inputs give the same verdict today, next week, or in an audit a year from now.
Savings with the math shown
Not just “you saved.” You see what each answer cost on your trusted model, what it would have cost on a smaller one, and the difference.
Your quality bar, in view
You set how often a smaller model has to match your trusted model. That bar sits beside your measured match rate, so “are we safe right now” is always answerable.
We have no customers yet, so there's nothing live to show — and we'd rather say so. This is how a Kadari trace reads, run on a synthetic workload. Yours would come from your own traffic.
How Kadari's judge decides
For each real request, the judge weighs your model's answer against the answer a smaller model would produce — projected, never run. Only when it can prove they would match is the saving counted; on any doubt, the request stays on your model.
Logs to Proof
in Four Steps.
Wrap
Two lines around your existing client. Your app runs exactly as before — nothing is re-run, nothing is auto-uploaded.
Observe
With only one shared log, Kadari finds the high-volume work a smaller in-family model could likely handle.
Prove
A deterministic judge proves a smaller model would have matched your trusted model's answer. We keep it on your model on any doubt — and only count what passed.
Show
What was safe to cut, what we left alone, and exactly where a smaller model would have differed. Everything surfaced and never hidden.
Where the work routes
Your provider. Your models.
Our receipts.
We built Kadari on the principle of cost optimization without gambling on quality or fully exposing data. We don't sell a model or push you toward one. Our focus is to show you where your spend goes and prove what's safe to cut. Backed by numbers and always-on rigorous testing.
You and your provider exchange live traffic directly — it never passes through Kadari. Everything stays inside your data boundary except the one log you choose to share, and that log is all Kadari ever reads.
A referee, not a vendor
Model neutrality is fundamental to our business because we don't give favors or try to upsell anything.
We measure equivalence, not vibes
Every saving is checked against your current model's answer, scored by a version-pinned judge. We claim a bound you can audit, never “lossless.”
The decision trace is the product
For any request you can open the full trace: your model's answer, the judge's verdict that a smaller model would have matched it, what it would have cost, and when sampled, the exact diff we checked. Replayable to the same verdict later, by an auditor.
Route up on any doubt
Set a quality floor we can never cross, then dial how hard we chase savings above it — one switch turns us off entirely. The instant we're unsure, we stay on the model you trust, inside your provider and contract. We never switch you across providers.
Wrap one call.
Nothing else changes.
Kadari ships one thing: a lean, zero-dependency Python client that records the calls you already make — to a local log, on your own machine. It never alters the call and carries none of our analysis engine. We send design partners the wheel directly; install it offline in one line.
See your own traffic.
Free.
We're taking on a small number of design partners. We'll observe your traffic and show where you overpaid, in your own numbers. You'd be among the first to shape the product with us.
A free first look
Wrap your calls, share one local log, and we return an estimate of what appears routable — no probes, no spend, and you make the final call.
Start hereProof on your own traffic
We instrument one workload in shadow — no production routing — and turn that estimate into a proven, probe-backed number you can audit. A small cohort, worked closely.
Become a design partnerNot sure it fits?
Tell us about your workload and bill. Even if your domain falls outside our coverage, we'd love to discuss.
Get in touchSee where you're
overpaying.
One real workload, instrumented in shadow, with the proof on your screen. We reply personally — usually within two business days.