Understand the operating layer.

The words we use, defined once.

AI inference management

AI inference management is the process of measuring, routing, governing, and optimizing the model calls used by AI applications, agents, and automated workflows.

Inference

One run of an AI model: the model reads an input and produces an output. Each run uses compute and usually costs money per token.

Crouton Unit

A structured record created from one messy AI, business or website signal. It carries claim, source, confidence, route, policy, action and impact.

AI Layer Diagnosis

A first-pass map of inference load, routing waste, governance gaps and operational risk, built from sample data.

AI Visibility Layer

A machine-readable map of crawl paths, schema, feeds, evidence and answer-ready data that helps search engines and AI assistants interpret a business.

Evidence Brief

A reusable proof object that connects a claim to source-backed evidence.

Model routing

Rules that decide which AI model handles each request based on task class, risk, quality, latency and cost.

Agent fan-out

The spread of one user task into many model calls, tool calls, retrievals and retries inside an AI agent system.

Generative engine optimization (GEO)

The practice of making content visible and usable to generative AI engines through retrieval, interpretation and citation, not only traditional search rankings.

WebMCP

A browser proposal that lets a web page register tools an AI agent can call directly, such as checking availability or booking, instead of scraping the page.