Scan a site's AI-crawler access, structured meaning, citation evidence, and task readiness, then return page-level fixes.
At a glance
Owners do not know why models fail to cite them or why agents cannot read product, price, or availability. Robots, JS rendering, schema, and drift are real issues, but brand visibility is not identical to task execution.
Differentiation Opportunity
Agent task execution could be narrower than generic GEO, but AgentReady already occupies the name and workflow.
★ Measured entry keyword
ai visibility audit
Volume/mo
170
KD
0
🔒 The playbook is behind the wall
Free readers get the candidate and its evidence. Members get measured keyword data, the SERP breakdown, rank feasibility, and the evaluation task's full public research and observation plan.
Already a member? Enter your license key
This report unlocks for everyone on 2026-12-15
Target User
SMB site operators and SEO teams that need AI search and task agents to discover, understand, and act on their sites.
Public Research Record
a five-tool matrix on the same public fixture, shared misses, and platform-distribution evidence
no crawler product code.
Differentiation
Agent task execution could be narrower than generic GEO, but AgentReady already occupies the name and workflow.
Primary
Monitoring subscription; 2. one-off technical audit; 3. agency leads. None works without an ownable detection result.
Secondary
Sponsored placements
🔒 The lines above are the model’s basic take — the full playbook is for members
The monetization playbook maps 4 paths — who pays, at what moment, how much — each checked against free alternatives, differentiation, and path friction, with measured CPCs as evidence of willingness to pay.
See how to unlock ↑
Evaluation-Task Public Research and Observation Actions
Free preview · roughly the first quarter
🔒 PROA developer supplies real queries and result requirements, then receives a reproducible fit matrix for Brave, SerpApi, Firecrawl, Lightdrift, and alternatives across fields, attribution, safety, expected cost, and integration constraints.
🔒 PROAn AI team submits an agent endpoint, task rules, and an optional system schema; the tool generates multi-turn adversarial tests, verifies tool choice, arguments, ground truth, boundaries, and recovery paths, then outputs release-to-release regression evidence suitable for CI.
A user selects OS, whether an agent should control recording, whether cloud upload is acceptable, whether zoom/captions/team analytics are needed, and budget, then receives a sourced tool matrix, shortest workflow, permission risks, and a task-fit choice.