Selected AI platforms are sampled under replayable conditions, showing what they say about your brand, which sources they expose and where the first controllable break appears.

A fixed question panel runs across selected platforms, with raw answers, citations and run conditions retained. We then inspect technical access, entity facts, content support and source exposure behind each material pattern. Content and SEO leads who have visibility signals but cannot yet tell whether the issue sits in access, representation, content, sources or measurement.

You receive a dated, platform-separated snapshot from the approved sample. Every material finding points back to its prompt, page, request or first-party record.

Some of the 500+ brands we've worked with

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The approved sample is collected under documented conditions before interpretation begins. Controllable layers are tested next, and an independent reviewer replays the decisive evidence trail before a finding is accepted.

How we hold ourselves to it

Raw answers over scores
Findings stay per platform
A baseline with its limits stated
No blended verdict

Frame questions, platforms and evidence rules
We source real audience questions, tag journey and market dimensions, define platform-specific run conditions and write the evidence dictionary before collecting answers.
Versioned sampling protocol and metric dictionary by platform, market and journey stage.
AI assist
Builds candidate questions from approved search, sales, support and research inputs and flags coverage imbalance.
Human gate
The strategist and client owner approve representativeness, exclusions and the distinction between mention, recommendation, citation and outcome.
Capture repeated answer and source observations
We run the fixed sample repeatedly, retain raw answers and citations and keep platform, mode, date, locale and session conditions separate.
Cross-platform response ledger with raw outputs, linked citations, run conditions and observed variance.
AI assist
Executes the documented sample, normalizes captures and reports missing or failed runs without filling gaps.
Human gate
A specialist validates raw captures, removes UI or collection artifacts and confirms comparable conditions.
Audit technical and representation constraints
We verify indexation, bot-purpose controls, live WAF/CDN access, rendering, textual availability and the accuracy of exposed brand facts.
Technical-access and representation evidence pack with HTTP, render, log and fact checks.
AI assist
Tests actual request paths, compares source and render states and proposes the narrowest diagnostic for each suspected block.
Human gate
Technical and entity owners approve whether a constraint is reproduced, unknown or disproved.
Trace entities, claims and source support
We connect each material answer statement to cited or recurring source pages and assess whether the source supports, qualifies or contradicts it.
Entity-claim-source matrix with support status, provenance, correction path and owner.
AI assist
Extracts claims, citations and source context into a traceable matrix without treating a link as proof.
Human gate
Domain and editorial reviewers validate claim support and approve ethical owned or third-party source actions.
Reconcile visibility with first-party outcomes
We compare observed presence and citations with first-party referral and conversion records while keeping dark attribution, assist effects and vendor estimates explicit.
Layered visibility-to-outcome reconciliation with known attribution gaps and no causal claim.
AI assist
Joins only approved data at documented granularity and labels observed, estimated and unavailable fields.
Human gate
Analytics and business owners approve attribution rules and prevent direct traffic from being relabelled as AI without evidence.
Validate findings and assign evidence labels
An independent specialist samples the evidence trail, re-runs decisive checks and verifies every finding’s status, owner and next decision.
A final register that keeps observed, proxy, hypothesis and constraint findings apart, with the accepted findings and the validation backlog.
AI assist
Replays recorded prompts and diagnostics, identifies unsupported interpretations and preserves non-results.
Human gate
The audit lead accepts only findings that another specialist can reproduce from raw evidence and declared constraints.

Raw observations, proxies, hypotheses and constraints keep their own labels through delivery. Every unresolved item leaves with an owner and the smallest useful next check.

AI search visibility audit
A platform-separated baseline of presence, description, recommendation, citations, claim accuracy, technical access and first-party outcomes.
Accepted when
Shows which layer has an evidenced problem and prevents a blended score from becoming the diagnosis.
Cadence: Evidence linked
Evidence-labeled constraint map
A URL- and owner-level map of reproduced access, representation, source, analytics and policy constraints.
Accepted when
Lets the organization fix a confirmed blocker, validate an uncertain one or avoid work on a disproved assumption.
Cadence: Owner assigned
Prioritized validation backlog
A sequenced list of the smallest checks needed to resolve unknowns before a larger implementation is funded.
Accepted when
Turns uncertainty into bounded tests with expected evidence, owner, effort and stop rule.
Cadence: Scoped and owned
Executive decision and non-action brief
A concise record of what is supported now, what remains unknown and which attractive ideas should not be pursued yet.
Accepted when
Gives leaders a defensible start, defer, reject or re-audit decision without promising third-party outcomes.
Cadence: Decision logged
Reproducible primary observation
Every finding records exact prompts, raw outputs, URLs, request evidence, log windows, sources and first-party records needed for independent reproduction.
Accepted when
Lets a second specialist replay the exact run instead of taking the finding on trust.
Cadence: Fully reproducible
Findings that cleared the acceptance bar
A finding ships only when it traces back to exact prompts, pages, sources, logs and an accountable owner. Rankings, recommendations, citations, referrals and conversions are measured separately and stay out of the audit's claims.
Accepted when
Keeps the audit's claims inside what the sample can carry, so a start, defer or reject decision stays defensible.
Cadence: Claims stay bounded

A few screenshots or a moving vendor score won't show where the problem begins. The audit fixes the sample first, then separates findings across access, representation, content support, source exposure and measurement.

A good fit when

The panel includes invented or cherry-picked prompts — Markets, personas, journey stages and languages do not yet form a representative tagged sample.
A visibility signal has no live-site evidence — Crawler, render, Search Console, analytics or server-log checks have not been reconciled.
Brand facts and source pages do not line up — Approved entity facts lack a dated inventory of owned and external pages that support or contradict them.
The competitor frame is missing — Mention volume cannot be tied to the business priorities behind the sample.
Platforms show different answer and citation patterns — Repeated runs cannot be compared until brand roles, recommendations and cited pages stay separated by market.
A brand is visible but its technical path is unclear — Indexability, CDN/WAF access, rendered text and entity facts point to different explanations.
An answer claim lacks a supporting page — Owned and third-party sources disagree on whether the exposed statement is actually supported.
Visibility has no first-party business context — Referral, assisted outcomes and team capacity cannot be read together while missing referrers stay unresolved.

The situation before this work starts, drawn as a Zeo scene

Better handled as other work when

You need the audit to reserve a future position — It records today's sample and uncertainty, while tomorrow's answer remains outside its control.
You need one universal rank across platforms — The audit sees samples and first-party evidence, but it cannot reveal hidden model internals.
You want findings to ship as production fixes — The audit hands over priority, but each owning team must review the change before release.

GEO work starts with recording what AI answers actually say about a brand today, then moves to the parts you can influence. The consultants below work on the specific capability this page covers.

Content we've produced on this topic

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Profound
runs the fixed panel and keeps raw answers, citations, and run conditions attached
Screaming Frog
checks technical access, one of the four inputs behind a confirmed pattern's interpretation
Notion
the handoff document itself, built to show its workings rather than assert a conclusion

Priority audiences, their questions and the relevant site access define the starting sample. The audit traces material findings to evidence and isolates the unknowns that need another test.

Scope the visibility baseline

We retain raw answers and citations for the approved question panel. Brand roles and recommendations remain attached to the full response. Platform, session, locale and timestamp conditions stay with each run. Failed runs remain visible, and no universal rank is created.

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By industry

By tool

AI Search Visibility Audit & Baseline

Collect first, interpret second

Frame questions, platforms and evidence rules

Capture repeated answer and source observations

Audit technical and representation constraints

Trace entities, claims and source support

Reconcile visibility with first-party outcomes

Validate findings and assign evidence labels

A handoff that shows its workings