Google Search does not use llms.txt for visibility, while Chrome treats it as an optional browser-agent summary. Learn when a small test is worthwhile.
Design a governed AI analytics context packet with metric definitions, approved schema paths, user access, decision requirements, source status, and versions.
Build a text-to-SQL truth set that tests execution, results, business meaning, permissions, clarification, traces, data mutations, and production regressions.
Turn an existing AI-search baseline into a source plan by mapping answer claims, classifying citation gaps, judging feasibility, and defining responsible rechecks.
Choose a BI dashboard for stable monitoring, an AI analyst for adaptive inquiry, or both through a shared metric, access, trace, and decision contract.
Build CRM workflow observability with business invariants, run IDs, freshness and reconciliation checks, actionable alerts, named owners, and safe recovery.
Improve ChatGPT visibility with crawler access, sourceable answers, consistent facts, stronger source coverage, repeated observations, and referral measurement.
Choose a single-agent or multi-agent design by testing security boundaries, context separation, ownership, parallel work, handoffs, and operating cost.
Learn how to prove AI crawler access with server evidence, robots checks, response inspection, rendering comparisons, and a layer-by-layer diagnostic path.
Prioritize SEO, AEO, and GEO by dependency: fix discovery and eligibility, improve answer clarity, strengthen source evidence, then measure generative visibility.
