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You know what AI coding costs. We tell you what that cost fixed

RidgeLine attributes AI tokens directly to code commits, letting you govern and control your spiralling AI SDLC costs.

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Take back control of your AI software engineering costs.

AWS Partner Network

Runs entirely in AWS London. Your source code is never stored.

A new problem emerges when adopting AI coding tools

I am spending all this money on tokens every month and I have no idea what it's being used for.

Every leader is seeing AI costs increase every month. None of them know what features or bug fixes they get in return for those tokens.

If you can't see where the tokens went…

  1. You can't back the right projects

    Token spend that can't be attributed can't be prioritised. The work that moves the company forward competes blind with the work that doesn't.

  2. Shadow AI goes unseen

    Unsanctioned models and personal keys inside your dev team are a security exposure long before they're a budget one.

  3. Adoption stalls in the dark

    You can't drive AI adoption across the org if you can't see which teams have it working and which have quietly stopped.

  4. Features have no price tag

    Without knowing what a feature or a bugfix actually cost, every build-or-drop decision is a guess dressed as a judgement.

But worst of all…

You are at the mercy of your next AI bill

with no idea how big the next one will be.

Take back control

The solution is RidgeLine.

It's a tool that lets you observe and control your agentic coding spend in realtime.

  1. 01

    See where AI is being adopted

    A full census of AI-assisted coding across every repository and every engineer so you can drive adoption where it's missing and shape it where it's already running. You can't have an AI assisted software strategy if you don't know where it's being used.

  2. 02

    Attribute every token and every pound

    Usage and cost traced down to the line of code written, the feature deployed or the bug fixed, including the token waste that never got deployed at all. With visibility comes the ability to direct your AI token spend to projects that matter, features that will move the needle, or constrain it for fixed cost BAU projects.

  3. 03

    Improve the quality of agentic engineering

    Show engineers how to get more from their repository harness, the skills they invoke, the models they pick and the value they actually retrieve from each run. Maximise the value of every token spent.

  4. 04

    Govern your AI coding

    Set guardrails on spend by project lifecycle, on which models may be used, on which AI accounts are authorised and be told when something steps outside them. Take back control of your spiralling AI costs to ensure engineers are working within sensible guardrails.

How it works

Three step setup.Start small.Start free.

  1. Step 01Free tier

    Install in minutes. See your estate the same day.

    Install in minutes and immediately see where AI is being used across your code estate — which repositories, which people. The step is tiny, and there's a free tier to try it at no cost.

  2. Step 02Realtime

    Turn on token tracking. Watch the burn as it happens.

    Realtime visibility of token burn, sliced by repository, pull request or person. Finally see the token and financial price of every bug and every feature you ship.

  3. Step 03Guardrails

    Govern the spend. Stop being at the mercy of the bill.

    Set token budgets, prioritise spend by project lifecycle, catch shadow AI, and surface the token waste that never makes it to production. Guardrails in place means the next bill is a decision, not a surprise.

Get on the list before the bill gets bigger.

RidgeLine is opening to a small first group of estates. Leave an email and we'll come to you with a look at your own spend.

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