Take a labelled brand-response snapshot
Record what one named model execution says about a brand at a specific point in time.
AI Brand Visibility Checker
Free, no signup. Compare what one model says from parametric memory with what it says when constrained to your supplied context, then inspect answer, fact, support, citation, and freshness drift.
Your input is sent to the AI model to generate a result — it is not stored afterward. The model checks send the brand research question and, for the grounded column, extracted supplied context to the bounded Workers AI endpoint. Saved snapshots stay in this browser's localStorage. Anonymous run-level outcome counters may be used for aggregate research; URLs, domains, IPs, and identifiers are never included, and no statistic is released below 100 runs.
Sample report
Illustrative unavailable-state report — no brand response invented
Workers AI model · retrieval off · degraded
No live model result is available. Add a BYO key in a future run or review the supplied facts manually.
Brand mention: not evaluated
Other model profiles
Not evaluated — no provider-specific run occurred; do not infer a zero mention or citation rate.
How to use it
- Enter the brand and known aliases.
- Add facts you want to compare literally with the response and select one research framing.
- Run the model check and record the model, retrieval state, and mode with the result.
- Save a local snapshot only after reviewing it; compare repeated runs as samples, not a universal score.
Site passport Local context for this saved site
Local data
Saved targets, named lists, and recent check summaries remain only in this browser.
Saved snapshots
No snapshots saved in this browser yet.
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What the results mean
- Brand mentioned is a normalized brand-or-alias text match in the sampled response.
- Retrieval off means no grounded web citations were requested.
- Simulation identifies a completed point-in-time model observation; degraded/not evaluated preserves an unavailable or refused run.
- Stated, not clearly stated, and possible conflict compare your browser-local fact text with the closest raw-answer sentence. They are observations about this answer, not factual verdicts.
How it works
A transparent probe builder turns the selected framing into a bounded request for one Workers AI profile with retrieval off. Local functions match the brand and aliases, compare supplied facts, label unrun model profiles not evaluated, and serialize reviewed snapshots to localStorage.
Features
- Four repeatable research framings.
- Brand and alias mention matching.
- User-supplied fact comparison.
- Explicit model, retrieval, and unavailable states.
- Up to 20 device-local snapshots.
Limitations
One model response is unstable and not representative of ChatGPT, Gemini, search systems, other prompts, regions, or dates. Retrieval is off, so this run cannot measure citations. Literal fact matching is not factual verification, and local snapshots are not continuous monitoring.
AI answers may continue to reflect cached or previously retrieved material for days to weeks after a fix. Recheck over time before treating an unchanged answer as proof that the fix failed.
Frequently asked questions
Is this an AI visibility score?
No. It records one retrieval-off response from one Workers AI model under a selected framing. One sample cannot represent product-wide visibilityLLM visibility (or AI visibility) is the aggregate measure of how often and how prominently a brand or page shows up in AI-generated answers — across AI Overviews, ChatGPT, Perplexity, Copilot, and Gemini. It's the AI-search analog of organic visibility, but it's driven by different signals. or market share.
Is a brand mention the same as a citation?
No. A mention is matched brand text in the response. This retrieval-off run supplies no source citationAn AI citation is the visible source link an AI answer engine shows next to its generated text — the clickable reference that credits the web page it used. A citation's presence is a separate thing from whether the cited page actually supports the statement, and from being retrieved (read behind the scenes) or merely mentioned (named without a link); citation is driven more by brand mentions and being retrievable than by traditional ranking., and the tool does not convert mentions into citation rates.
Why are other model profiles marked not evaluated?
They were not queried. The tool preserves that unavailable state rather than treating an unrun provider as zero visibility.
Where are saved snapshots stored?
Up to 20 snapshots are stored in this browser’s localStorage. They are device-local and are not a shared monitoring history.
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Where this tool helps
Common use cases
Separate mentions from citations
Distinguish a brand name appearing in an answer from a source link that actually supports the response.
Compare repeat samples over time
Save local snapshots and look for directional changes after content, entity, or authority work.
Document model-specific gaps
Capture missing facts or weak representation without generalizing one sample to every AI search surface.
Watch the full workflow
AI Brand Visibility Checker walkthrough
Read the transcript
AI Brand Visibility Checker
A single model answer can mention a brand, omit it, or be unavailable, but none of those outcomes is a universal visibility score. I’ll show you how to run a retrieval-off observation, compare it with supplied context, inspect mention and fact labels, preserve unavailable states, save local snapshots, understand limitations, and plan responsible follow-up.
Step 1
This checker records one point-in-time response from one Workers A-I model under a selected framing. Retrieval-off tests parametric memory. Supplied-context grounding uses text you provide. Neither column reproduces a consumer product, live search index, regional behavior, or product-wide visibility.
Step 2
Use it to document what one model says under a repeatable question, check whether aliases appear, compare user-supplied facts with raw wording, or study how a supplied page changes the response. Keep the brand, framing, model route, and date with every sample.
Step 3
Enter the brand, aliases, known facts, and one research framing. Select Run retrieval-off check. The tool sends the brand question to the bounded endpoint, records the actual model state, and preserves degraded or refused runs as not evaluated instead of treating them as a negative brand result.
Step 4
The observation card records query time, requested and resolved model versions, retrieval off, simulation or degraded mode, selected framing, exact prompt, warnings, and raw response. Read this provenance before interpreting mention status or comparing the run with another date.
Step 5
Brand mention is a normalized text match for the brand or alias in this single response. Retrieval was off, so no grounded web citation was requested. A mention frequency of one out of one is not a market share, citation rate, recommendation rate, or probability.
Step 6
Known facts stay in the browser and are compared locally with the closest raw-answer sentence after the response returns. Stated, not clearly stated, and possible conflict describe literal alignment with this answer. They do not verify which fact is true.
Step 7
Other model profiles are explicitly not evaluated because no provider-specific query occurred. Do not turn an unrun product into a zero mention, zero citation, or negative result. Unavailable evidence must remain unavailable in reports and trend lines.
Step 8
Paste page context and select Compare memory versus supplied context. Both columns use the same research question and model route; only the context condition changes. The grounded column is not live web retrieval, and an unavailable paired run produces no inferred drift.
Step 9
When both answers are substantive, the lab compares answer overlap, supplied-passage lexical support, known-fact wording, and dates. If either column is unavailable, the result says not evaluated. Passage matches are not independent factual verification, and changed wording is not automatically improvement.
Step 10
After reviewing a run, save a local snapshot. Up to twenty snapshots live only in this browser’s local storage. Repeated entries can show mention and response changes, but this is not shared or continuous monitoring and can disappear when local data is cleared.
Step 11
One response is unstable and cannot represent Chat G-P-T, Gemini, search systems, other prompts, regions, or dates. Retrieval-off runs cannot measure citations. Literal fact matching is not factual verification, and cached consumer answers may lag upstream page changes.
Step 12
Download the C-S-V with provenance, raw answer, mention observation, facts, and warnings. Verify important claims using independent sources. Repeat the same framing and model route on a planned cadence, then compare samples alongside real citations, referral traffic, customer research, and human review.
Track samples—never pretend they are market share.
Review and export the observation, repeat the exact framing over time, and compare only like-for-like runs. Validate facts independently, use real referral and citation evidence where available, and leave unqueried products as not evaluated. A mention is text in one answer—not a citation rate or visibility score.
Common issues & how to fix them
- Warning Known brand fact is missing from the sampled answer Fix: Publish the missing fact consistently on authoritative first-party pages and profiles using clear entity-and-attribute wording.
- Error Sampled answer contradicts a known brand fact Fix: Correct the conflicting fact across first-party and trusted third-party sources, then retest multiple model samples.
- Warning Competitor receives stronger coverage Fix: Build authoritative category content and independent mentions that clearly connect the brand to the tested use case.
- Warning Answer lacks authoritative source support Fix: Support the answer’s important brand claims with crawlable first-party evidence and corroborating authoritative sources.
- Information Model samples disagree materially Fix: Run more samples across prompts and models, then act only on facts or visibility gaps that repeat consistently.