We'll analyze your AI spend and show where money is being wasted.
Three leaks show up in almost every agent stack. The free teardown measures exactly how big each one is on yours — no guessing from someone else's numbers.
Your agent re-reads its whole history every loop, so you pay for the same tokens again and again — often the single biggest line in the bill.
Formatting, extraction, and boilerplate run on a top-tier model when a far cheaper one would finish them identically.
Existing routers pick a model on price and latency — none check whether the task actually got done, so you can't safely route down.
Each task is scored — reason vs. execute, hard vs. boilerplate — to decide what can safely route down.
Redundant re-sent history is stripped before the call is priced, while caches are preserved.
Easy work goes to a cheaper model in your approved pool; hard reasoning stays frontier. Automatic failover.
Every call is logged against what it would have cost on the frontier — so savings are auditable, per task.
Routers pick a cheaper model. Watt also shrinks what you send it — and proves the quality held.
These are the questions I'd ask before routing a dollar of production traffic. Straight answers — and the things still being built get marked as such.
Default to your existing providers only (Anthropic, OpenAI). Add cheaper models when — and only when — you choose. No traffic goes to any provider you didn't opt into.
Watt learns routing from metadata — task type, model, cost, whether it passed your quality check — never your prompt or output content, and never to train a third party's model.
Watt is a thin proxy with automatic fallback: if anything fails, the call goes straight to your frontier provider. The goal is that a Watt outage degrades to "no savings," never "no service."
Routing decisions run on a small, fast model, and sending fewer tokens often offsets the overhead. Latency is measured per task in your dashboard — if a route is slower, you'll see it.
Compression targets redundant history, not your cached prefixes, so your Anthropic/OpenAI cache discounts keep working alongside Watt's savings.
If your agent speaks the OpenAI API — Claude Code, OpenClaw, LangGraph, your own — it speaks Watt. One base-URL change; your request/response shape is unchanged.
You see your measured baseline and the per-task savings math first. We keep a slice of the verified savings; your net bill still drops. No savings, no fee — and no seat fees or minimums.
Cheap-routed outputs are scored against a quality bar you control. Anything that dips below it is routed back to the frontier model automatically — you can start with the floor so high that nothing routes down until you trust it.
July 2026 cohort · best fit if you spend ≥ $2k/mo on agent tokens · reviewed as applications arrive.
I spent years in M&A and financial due diligence — taking businesses apart to see where the money really goes. I'm now building Watt solo and dogfooding it on my own agent stack. I come at AI spend as a unit-economics problem, not just an engineering one: the goal isn't a clever router, it's a smaller bill you can audit line by line. That's why the first thing I do is measure — not pitch.