v0.1 — model cost optimization for ai agents

Your AI agents don't need a frontier model for every call.

Proviras ingests the LLM calls your AI agents make in production, groups them into task buckets, and replays a sample through a cheaper model. We score the quality difference and send you a report showing exactly which of your agent's calls are safe to downgrade and how much you'll save per month. Connect the SDK in under two minutes.

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How it works

1

Connect your agent via our SDK

Drop the Proviras SDK into your AI agent and every model call it makes — prompt, response, model, cost — starts streaming into Proviras.

$npm install -g proviras-sdk

2

We bucket your agent's calls by task

Your agent's model calls are clustered into recurring task types — summarize, extract, classify, draft — so we can reason about cost per kind of work, not per request.

3

We replay a sample on a cheaper model

For each bucket of agent calls, we shadow-run a representative sample through a smaller, cheaper model alongside the one your agent is using today.

4

We score the quality difference

Every replay is judged for equivalence against your agent's original output. You see a match rate with a 95% confidence interval, not a vibe.

5

You get a savings report

Actionable recommendations: which of your agent's calls are safe to downgrade, the evidence behind each one, and the dollars per month you'll save.

Your savings report

Recommendations

Ranked by monthly savings

Every task your agent runs where a cheaper model is statistically equivalent, sorted by the dollars you'd save per month if you switched.

Summarize support tickets · $312/mo · 97% equivalent

Evidence

Quality stats per agent task

Shadow test counts, equivalence percentage, and confidence interval bounds — so you can decide whether to switch your agent's model with real numbers behind it.

1,842 shadow tests · 14 days · 97% (CI: 95–99%)

Status

Ready, gathering, or skip

Each recommendation is tagged so you know which of your agent's tasks are safe to switch today, which need more data, and which we'd leave on the frontier model.

Ready to switch · Gathering data · Needs review

Ready to find out what your LLM bill should actually be?

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