Ongoing incident: your AI bill. severity: high  /  cause: tokens  /  status: fixable See the fix

Your AI bill got out of hand. Call the guys.

We find the waste in your LLM and agent spend, ship the fixes, and keep your agents current as new models arrive. You pay a share of what you save, and nothing if we save you nothing.

Run the free audit Talk to an engineer

Free, takes about ten minutes, and runs in your browser. Your logs never leave your machine.

Run the audit on this sample to see every finding.

Signs it's time to call the guys

Tick the ones that sound familiar. Be honest, nobody's watching. (We're not tracking this either. It's just a checklist.)

0 of 6Go on, tick a few.

What would you do with the money?

Slide to your monthly AI bill. This is a rough estimate at 45%, a bit under what the same playbook cut last time. The free audit gives you the real number.

Back in your pocket, per year
$270,000
You keep, after our 25%
$202,500
That's roughly
One senior engineer, paid for by tokens you weren't using.

Estimate only, at 45% savings. Engineers counted at $200K a year, fully loaded.

We bring receipts.

Inside an AI-native software company, we rebuilt the agents and workflows the whole company runs on. Here's what changed.

$105K $48K

Monthly AI workflow spend across the company. 54% lower.

$2,500 $22

Monthly cost of one AI pull-request reviewer, rebuilt.

15 min 3 min

Time per code review, after the same rebuild.

Founder's in-house results before The Tech Guys. The same work also freed about 1,000 engineering hours a month.

Audit. Fix. Own.

Most teams know the bill is too high. What stops them is proving a cheaper version is just as good, so nobody touches it. Proving it is our whole job.

  1. Audit the spend

    Export a week of LLM logs from Langfuse, Helicone, your gateway or your own logging. The audit shows which workflows cost what, and where the waste is, in dollars.

  2. Fix it, with proof

    We build evals from your real traffic, then change what's wasteful: model choice, caching, context, agent loops, batching, steps that should be plain code. Changes arrive as pull requests, tested side by side with what you run today.

  3. Own your model

    For high-volume work, we train a small model on your own traffic. It runs at a fraction of the price, you own it, and it keeps improving from your production data.

Deterministic-first engineering

Use the simplest thing that works reliably for each step. Code handles the exact parts, a small model handles routine judgment, and the frontier model only sees the hard cases.

Your most expensive model doesn't need to read every ticket that says “password reset”. Cheaper, faster, and every step can be tested.

Where your money is hiding

Token prices keep falling. Bills keep rising, because agents use more tokens every month. These are the leaks we find most often.

Frontier models doing routine work

up to 85% off

Classification, extraction and short answers running on the most expensive tier.

Long prompts, no caching

~90% off cached input

The same instructions and documents paid for at full price on every call.

Agents resending everything

30–50% off

Each step drags the full history along. Context grows several times over within one run.

Runaway loops and retries

all of it

Runs that take forty steps instead of eight, and failed calls you still pay for.

Background work at real-time prices

50% off

Nightly jobs and backfills nobody waits for, billed as if a user were watching.

The same answer, generated again

~90% off

Identical requests answered from scratch instead of from a cache.

Agents age like milk, not wine.

A better, cheaper model ships every few months. Your agents still run on the model, prompts and prices you chose when you built them.

New models, old prices

Each generation does more for less. Agents pinned to last year's model keep paying last year's rate.

Retirements

Providers retire old models on a schedule. An agent pinned to one stops working on a date you didn't choose.

Prompt drift

Prompts tuned for one model often misbehave on the next, so teams put off upgrading.

Patches pile up

Every fix adds instructions, retries and context. The loop gets slower and more expensive each month.

With Keep current, we re-run your evals on every major model release and ship the upgrade as a pull request once it passes.

We only get paid if your bill shrinks.

Our fee comes out of money you were going to spend on tokens anyway. Finance approves it on the first read. Possibly with a smile.

Audit

Free

Self-serve in your browser, or we run it with you on a call.

  • Spend by workflow and model
  • Top fixes with a dollar estimate
  • Candidates for your own model

Fix

25% of savings

Of verified monthly savings, for 12 months.

  • Evals built from your real traffic
  • Changes shipped as pull requests
  • Side-by-side test before switching
  • Savings measured per completed task

Keep current

From $1,500/mo

Monthly, after the fix or on its own.

  • Re-tested on every major model release
  • Upgrades shipped as pull requests
  • Old models replaced before they're retired
  • Cost and quality alerts

If we don't save you money, you don't pay us.

Your data stays home.

We work the way a careful engineer on your team would.

  • The audit runs entirely in your browser. Nothing is uploaded.
  • Fixes land as pull requests in your repo. You review and merge.
  • Everything runs in your cloud, on your provider accounts.
  • Any access we need is scoped, agreed in writing and revocable.
  • Models we train on your traffic belong to you.

Fair questions

Can't I just paste my traces into ChatGPT and ask?

You can, and the advice will be decent. Then someone still has to build the evals, change the code, prove nothing broke, ship it and keep it current next quarter. That someone is us, and we only get paid if it works.

Won't a cheaper setup make the output worse?

Not if you measure it. Before anything ships, we sample a few hundred real requests, keep today's outputs as the baseline, and score the new version against them. Changes that fail your quality bar don't ship.

How do you measure "verified savings"?

Cost per completed task over the 30 days before, against cost per completed task after, multiplied by your current volume. If your traffic doubles, you still only pay on the efficiency we added.

What logs do you need?

A week of LLM calls with model, input and output tokens, and ideally a workflow name and trace ID. Exports from Langfuse, Helicone, LiteLLM, Portkey or your own logs all work. JSONL, JSON or CSV.

Isn't this just a model router?

Routing is one fix out of many. The bigger savings usually come from restructuring agent loops, caching, batching and moving predictable steps into ordinary code. Routers can't do that, and they don't prove quality on your workload.

Is this worth it for a small bill?

The audit is free at any size. The Fix makes most sense from about $10K a month in AI spend, including coding agents and internal tools.

Why not ask our model provider?

They are paid per token. We are paid when you use fewer of them, and we'll use whichever provider, model or plain code does the job best.

Find out what you're overpaying, in about ten minutes.

Bring a week of logs. We'll bring the receipts. If it's worth fixing, call the guys.

Run the free audit Talk to an engineer