about us
Stop overpaying for model calls.
Proviras helps teams running AI agents stop overpaying for model calls. We find the calls in your traffic that don't need a frontier model — and prove it with real evidence before you switch.
The problem
Most companies building with LLMs are spending more than they need to.
Teams reach for frontier models like GPT-4 or Claude Opus on every task, even when a smaller, faster model would do the job just as well. The problem isn't carelessness — it's that there has been no safe way to know which calls could be downgraded without breaking quality.
How Proviras works
We test cheaper models against your real traffic — quietly, in the background.
Proviras watches your agent in production, groups its calls by what they're actually doing, and shadow-runs cheaper models against those calls. Then we tell you exactly where you can save money — with proof, not guesses.
What you get
Where your budget goes
See which tasks are eating your spend, broken down by what the model is actually being asked to do.
Safe downgrades, not guesses
Every recommendation comes with sample size and a 95% confidence interval — not a hunch.
Side-by-side evidence
Compare the original output and the cheaper model's output for real calls before you decide to switch.
Co-founders
Alex Rivera
CEO & Co-founder
Previously led infrastructure engineering at a series-B AI startup. Spent five years building developer tooling before co-founding Proviras. Passionate about making agent observability a first-class concern for every team shipping AI products.
San Francisco, CA
LinkedIn →Jordan Kim
CTO & Co-founder
Background in distributed systems and ML platform engineering. Built agent orchestration pipelines at scale before realizing there was no good way to understand what agents were actually doing in production. That gap became Proviras.
New York, NY
LinkedIn →Stop paying for intelligence you don't need.
Connect Proviras and find out, with evidence, which of your model calls could be cheaper without losing quality.
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