Home / Search and AI Visibility Measurement Framework | Hidden Radius

Measure more than rank and clicks

Search and AI Discovery Measurement

Hidden Radius uses a layered metric framework so traffic, citations, recognition, accuracy and business actions are not mistaken for the same outcome.

Published 2026-07-22Last reviewed 2026-07-22Primary-source analysis

Metric families

Five views of performance

Access metrics

Successful crawler responses, blocked requests, fetch errors, sitemap coverage and rendering.

Index metrics

Indexed canonical pages, duplicates, excluded URLs and freshness of processed versions.

Representation metrics

Correct entity, services, locations, products, credentials and source faithfulness.

Visibility metrics

Traditional rank, generated mentions, citations, supporting links and share of observed answers.

Outcome metrics

Qualified referrals, calls, enquiries, bookings, purchases, subscriptions and completed agent tasks.

Observation protocol

Record the environment around the answer

Exact query or prompt.
Platform, mode and interface.
Date, time, language and geographic context.
Account or personalization state where known.
Visible sources, citations and ordering.
Generated claim and the supporting source passage.
Resulting click or business action where measurable.

Recognition scorecard

Measure coherence without inventing a platform score

Name consistencyDo authoritative first-party sources use the current official identity?

Relationship clarityAre founders, brands, products, locations and official links connected correctly?

Conflict severityHow many high-impact contradictory facts remain public?

Source authorityIs the current fact available from the source responsible for it?

Structured alignmentDoes JSON-LD match the visible page?

Observed accuracyDo repeated public answers identify the entity correctly?

Retrieval scorecard

Measure answer support at passage level

Question coverageIs the information needed for the question actually published?

Passage completenessDoes the retrieved section include subject, answer and limitation?

FreshnessIs the current version accessible and dated?

Citation supportDoes the source passage support the generated statement?

Cross-query stabilityDoes the information remain available across realistic wording variations?

Error recoveryCan the source owner correct and republish quickly?

Reporting rule

Use trends and evidence, not theatrical certainty

Generated interfaces change, experiments are personalized and outside systems remain opaque. Hidden Radius reports documented observations across dates and queries, identifies uncertainty and avoids converting a sample into a universal platform rule.

A metric is useful only when the organization knows which decision it changes.

Business connection

End the report with an operational outcome

Which customer needs became easier to answer?
Which inaccurate descriptions were corrected?
Which pages or sources became discoverable?
Which qualified actions increased or decreased?
Which remaining risks require product, legal or operational change?

Source trail

Primary material behind this analysis

NIST AI Risk Management Framework: Generative AI Profile

Cross-sector guidance for governing, mapping, measuring and managing generative-AI risk.

Open the original source at National Institute of Standards and Technology →

AI Features and Your Website

Official explanation of AI Overviews, AI Mode, query fan-out, eligibility and controls.

Open the original source at Google Search Central →

Publishers and Developers FAQ

Official information about OAI-SearchBot, GPTBot, referral tracking and agent accessibility.

Open the original source at OpenAI →

Bing Webmaster API

Official capabilities for crawl, traffic, keyword, link, sitemap and URL-submission data.

Open the original source at Microsoft Learn →