Claude Code, Codex, and your production LLM features leave a paper trail — their logs.
Step one of controlling that spend is knowing exactly where it leaks: upload your
logs and get back a dollar-ranked audit of the waste and how to prevent it.
No SDK, no proxy, no integration.
Your data: “analyzed then deleted; nothing retained beyond 7 days; never used for training.”
What you get
A dollar-ranked list of waste findings — missing prompt caching, retry storms
(the same request paid for again and again), oversized models, prompt bloat,
unbounded output caps, and chatty agent loops (agents re-sending the same
context in bursts of small calls).
Every finding backed by evidence rows (token counts, never your prompt text)
and a concrete fix — the audit is step one of a prevention path, not a
one-off dashboard.
A client-ready PDF and a private web report, delivered from a deterministic
rules engine — no AI guesses anywhere in the math.
Why now
79% of enterprises overran their AI budgets last year (DoiT/Sapio Research, 2026),
and 98% of FinOps teams now manage AI spend, up from 31% two years ago
(State of FinOps, 2026). Dashboards need integration and an operator.
Routers pick a model; we find the other five kinds of waste — and prove it
in dollars.
What we never do
No SDK to install, no proxy in your request path, no gateway migration.
No prompt or completion text is ever stored — the engine keeps token counts only.
No AI reads your logs: the analysis is deterministic rules, so it cannot hallucinate.
AI spend control — APIs, agents, and AI seats.
The audit is step one. Leave your email for early access.