We exist so your engineers can keep their AI tools — without paying frontier prices for routine work.
Why we exist
AI coding tools are worth it. Most of their bill isn't.
Coding agents changed how engineering teams work, and usage-based pricing means the bill scales with every file an agent reads. Most of that spend goes to reading, searching, and boilerplate: work that needs none of a frontier model's intelligence.
Cutting the tools isn't the answer, and neither is telling engineers to use a cheaper model and hoping they remember. The routine work has to be routed somewhere cheaper by rule, and the result has to be checked on your own code.
TokenShunt does exactly that. We measure where your tokens go, route the routine work to cheap models, prove the output is still right, and leave your platform team able to run it without us.
What we value
01
Rigor
Evidence before scale. Every claim measured, every decision reproducible, every result reported against a baseline.
02
Honest numbers
If routing won't pay off for you, we say so in the first call. A clear no is worth more than a stretched yes.
03
Measurable outcomes
Tokens, cost, and quality, measured against your own baseline. If we can't measure the improvement, we don't claim it.
04
Transfer, not dependency
Your team should be able to run everything we build. Enablement is part of every engagement, not an upsell.
Careers
Make AI tools cheaper without making them worse.
Small, senior teams. Real engineering organizations. Clients who measure everything. If that sounds like home, we'd like to hear from you.
Introduce yourselfDisciplines we hire for
- Platform engineers — model routing
- Developer-productivity engineers
- Forward-deployed engineers
- Evaluation engineers — code generation
- Security & compliance
Start the conversation
Stop paying frontier prices for routine work.
Tell us which coding tools your engineers use and roughly what you spend. We'll show you where the tokens go and what routing would change.
We respond within 1 business day. Mutual NDA available before any data discussion.