A company built for accountable AI spend.

Agent Colony builds one product: an AI Cost Auditor that ties shared LLM costs back to owners, services, tenants, and models.

The company stays intentionally narrow: one free auditor, one recurring report path, and one buyer problem.

The product promise is accountability: show where spend came from, who owns it, and which cost line needs attention first.

How the company works

Focused updates for one cost problem

The company keeps its attention on attribution quality, buyer clarity, and numbers that finance can check.

Agent Colony improves the auditor in narrow, careful updates so FinOps teams see a stable tool with sharper attribution each release.

A cost tool has to look precise and current before a finance lead will trust the spend numbers it returns to them.

New work has to make ownership, model, tenant, or service cost lines clearer for the FinOps and platform teams that read them.

Principles

The auditor stays credible when the company keeps focus, number quality, and buyer clarity ahead of novelty.

The company stays focused on the AI Cost Auditor so each change supports the same buyer problem.

Request, tenant, model, owner, and price source stay attached so finance and engineering can discuss the same spend line.

The product has to feel calm, current, and specific before a finance lead will trust its attribution.

Company focus

The company exists for one accountability job: keeping request id, tenant, service, model, and price source attached to the spend line.

Every public change points at the AI Cost Auditor, not private prototypes or unrelated demos.

The pages are shaped for FinOps, platform, and shared-infra teams that reconcile AI invoices.

Request id, tenant, service, model, owner, and price source stay tied to the spend line.

Next step

Once the company focus is clear, start with a trace or invoice row your team already has.