Use case · Any size · agents at scale

AI product teams

Most of your token spend goes on your agents rediscovering the same things.

AGENTS IN PRODUCTION, AND A TOKEN BILL THAT GROWS WITH USAGE

The situation

Agents rediscovering
the same things.

Your agents work against your own systems, and every request begins by reconstructing context that was reconstructed a thousand times before.

Adding tokens stopped improving the answers a while ago. It kept increasing the bill.

What it costs today

What you pay for
context, twice.

Paying repeatedly for the same knowledge

The cost of understanding your system is incurred per request, per agent, forever.

Cost per outcome, not per call

The number that matters is what one completed customer result costs, and it is worse than the per-call price suggests.

Quality that does not scale with spend

More context stopped helping, which means the problem is not the amount.

What you ask

What your agents
need to be told.

01

What does this agent need to know to complete this task?

02

Where are we paying AI to understand the same thing again?

03

How do we reduce cost per successful customer outcome?

04

Which parts of our context budget are actually contributing to the answer?

Worked example

An agent handling a support escalation about double charges

01

The agent asks why a customer could be charged twice.

02

It receives the paths through the product that produce that outcome, with the evidence, instead of a retrieved pile of payment code.

03

It resolves the case against what the product does rather than against what the snippets implied.

Outcome

Fewer tokens per case and fewer cases escalated back to a human because the answer was wrong.

A WORKED EXAMPLE OF HOW THE WORK GOES, NOT A CUSTOMER CASE STUDY. WHEN WE PUBLISH A CUSTOMER RESULT IT WILL BE MEASURED AND NAMED.

What changes

What changes when
context is established once.

Context established once

The knowledge is not re-derived per request, which is the line item that scales with your usage.

Cost per outcome falls

On the published benchmark, the same model answered better on a third of the context, at a third of the cost.

Model changes stop being migrations

The intelligence is not baked into weights, so a new model inherits it.

The measured part

Measured, and the same
whatever your size.

92%answers correct, against 78% for the highest-scoring alternative
69%context required
63%cost per question
Where to start

Company · from $999 / month

Multiple projects, company sources and higher usage. See also ensemble for production agent workloads.

Connect a repository.
Measure the difference.