Playbooks
Do — ordered procedure for one job, with decision points and validation.
Machine-Readable Infrastructure
Audit Content Extraction
One audit for what machines actually receive from your pages — whether critical content survives the fetch-and-render pipeline, and whether it is structured so retrieval systems extract the right claim.
Enable AI Search Access
Workflow for configuring robots.txt, X-Robots-Tag, and crawler controls so AI crawlers can access, render, and cite your pages across Google, OpenAI, Bing, and Perplexity.
Parse Log Files for AI Bot Behavior
Workflow for analyzing server access logs to identify AI crawler activity, measure crawl rates, and detect unexpected bot behavior from GPTBot, OAI-SearchBot, and others.
Structured Data Audit and Remediation
Workflow for finding broken, mismatched, or manipulative schema implementations and fixing them by page type, entity graph, and visible-content alignment.
Entity Authority
Content & Answer Optimization
Build an AI-Visible Content Page
Workflow for building pages structured for AI retrieval, citation, and grounding. Covers content architecture, semantic structure, schema, and extractability requirements.
Ship Author Trust for Expert Content
Workflow for implementing E-E-A-T signals on expert content. Covers author schema, bylines, About page structure, and citation-ready biography markup.
Sunset Content Safely
Workflow for consolidating, redirecting, or removing content while preserving link equity and preventing indexation signals from degrading neighboring pages.
AI Visibility Measurement
Build and Maintain a Prompt Library
Workflow for building and maintaining an AI visibility prompt library from scratch. Covers sourcing from evidence, grouping by job, target mapping, platform fit, locking, governance, and periodic auditing.
Diagnose a Drop in AI Visibility
Diagnostic workflow for classifying AI search impression drops. Covers crawl access, content eligibility, snippet restrictions, and measurement gaps.
Set Up AI Visibility Measurement
Workflow for building an honest AI visibility measurement program. Covers first-party data, locked prompt libraries, budget-based tooling choices, scoring, and reporting discipline.