How it works

Explore, test, build, ship. In that order.

Each step earns the next. If the numbers don't justify building anything, we tell you and you've lost fifteen minutes.

  1. 01 / Explore

    Free 15‑minute AI cost review

    Tell us which AI tools you use, roughly what they cost and what you most want fixed: cost, speed, security or reliability. You get first recommendations on the call.

    Free
  2. 02 / Test

    Paid technical audit

    We baseline cost, quality and latency on representative, permission-approved data, then test caching, routing and smaller models against it. You get the evidence, the risks and the full running cost.

    Fixed scope, quoted after the review
  3. 03 / Build

    Proof of concept or production build

    Fine-tune a small model on your workload, add a router in front of your frontier API, or automate a manual workflow with a human approval step. Only where the audit says it pays.

    Only if the audit supports it
  4. 04 / Ship

    Deploy, monitor, improve

    Run it in your own cloud or on a dedicated managed endpoint, with drift alerts, a monthly cost-and-savings report and model upgrades as better open weights are released.

    Optional ongoing care

The router

A small model handles the routine. Three checks catch the rest.

A gateway sits between your app and your models. Easy, familiar requests are answered locally. Anything long, unusual, low-confidence or malformed is sent on to your existing frontier model before your user ever sees it.

Your appOpenAI-compatible Frontier modelyour existing contract Small modelin your cloud Gate 1pre-flight Gate 2confidence Gate 3schema
88%answered locally
12%escalated
21%of all-frontier token cost*

*Illustrative, assuming a local request costs about a tenth of a frontier one before hosting. The audit replaces every assumption here with your measured numbers.

Before the small model runs · ~12 ms

Gate 1: pre-flight

Very long prompts, or ones that look unlike anything in your training data, skip straight to the frontier model so no GPU time is wasted.

First 30 tokens, held back

Gate 2: confidence

The opening tokens are checked for calibrated confidence before anything streams to the user. Low confidence means a silent hand-off.

Before the response returns

Gate 3: schema

JSON, SQL and tool calls are validated against your schema. Anything invalid is retried on the frontier model automatically.

Find out what your AI bill should be.

Fifteen minutes, three questions, no system access. If there's nothing worth shifting, we'll tell you that too.