Recall vs LLMrefs
Recall measures how ChatGPT, Copilot, Perplexity, Gemini, and Google AI Overviews answer the questions your buyers ask, and keeps the evidence: the stored answer behind every number, the pages it cited, and which of the four causes of an absence applies. Then it measures whether the fix worked. LLMrefs is an AI visibility tracking tool covering the same engines.
Recall
Measures how ChatGPT, Copilot, Perplexity, Gemini, and Google AI Overviews answer your buyers’ questions, and keeps the evidence: the stored answer behind every number and the pages it cited.
LLMrefs
Keyword-based tracking with auto fan-out prompts
About LLMrefs
LLMrefs takes a keyword-first approach to AI visibility tracking, automatically generating relevant prompts from your keywords (fan-out). This makes it more familiar for SEO professionals transitioning to AEO. Lists eBay, HubSpot, and NVIDIA among its users.
Popularity grade: B
- Keyword-based tracking — familiar for SEO pros
- Auto fan-out prompt generation from keywords
- Trusted by eBay, HubSpot, NVIDIA
Feature comparison
| Feature | Recall | LLMrefs |
|---|---|---|
| Core Tracking | ||
| Multi-LLM Tracking | ||
| AI Visibility Score | ||
| Citation Analytics | ||
| Competitor Benchmarking | ||
| Brand Mention Tracking | ||
| Platform | ||
| White-Label / Agency | ||
| Open Source | ||
| Content Generation | ||
| Advanced Analytics | ||
| Prompt Volume Estimates | ||
| Sentiment Analysis | ||
| AI Crawler Analytics | ||
| Geographic Tracking | ||
| Social Media Tracking | ||
| Shopping Tracking | ||
| Multi-Language | ||
| Actionable Insights | ||
| Action Recommendations | ||
| Content Gap Analysis | ||
| AI Site Audits | ||
| AI Keyword Research | ||
| Reporting & Integration | ||
| Email Alerts | ||
| Data Export / API | ||
| BI Connectors | ||
Key differences
Only in Recall
- White-Label / Agency
- Geographic Tracking
- Multi-Language
- Action Recommendations
- Email Alerts
Both offer
- Multi-LLM Tracking
- AI Visibility Score
- Citation Analytics
- Competitor Benchmarking
- Brand Mention Tracking
- AI Site Audits
- Data Export / API
Only in LLMrefs
- Prompt Volume Estimates
- Social Media Tracking
- Content Gap Analysis
- BI Connectors
What you get with Recall
Evidence, not a black-box score
Inspect the underlying prompt, answer, citations, engine, locale, and run outcome behind each metric.
The cause, not the score
An absence has four causes and four different fixes. Every recommendation names which one it answers, so the work is chosen by the reason rather than the size of a number.
Your data stays yours
Every answer, citation, and measurement exports in full at any time. You own the record and can take it with you wherever you want to use it.
Matched before and after
Lock the prompt, engine, locale, scoring method, and sampling plan before a change ships, then report the result with its uncertainty.
/ FAQ
Frequently Asked Questions
- What is the difference between Recall and LLMrefs?
- LLMrefs is an AI visibility tracking tool. Recall is an answer-engine measurement platform: it runs your questions repeatedly, stores every answer and the pages each one cited, and reports a share of answers with the confidence interval around it. Where you are missing, it names the cause — the engines cannot read your pages, cannot tell who you are, are not reading sources that mention you, or describe you in the wrong terms — because each of those has a different fix.
- What do you get with Recall that a visibility score does not give you?
- The working behind it. Every figure opens onto the answers it was computed from, with the cited pages captured at the moment they were cited. Recommendations are tied to a named cause rather than a lower score, and once you ship one, Recall re-measures the same questions and reports whether the answer actually changed — including when the result is inconclusive.
- Is LLMrefs open source?
- No, LLMrefs is a closed-source hosted product. Recall is closed-source and hosted as well; what it publishes is the evidence under each number — the stored answer, the pages cited, and the interval around the figure.
- Can I self-host Recall?
- Not at the moment. Recall runs as a hosted platform, with each customer's data isolated at the database level. Measuring answer engines means paying for every answer collected, which is why it ships as a service rather than something you run yourself.
Ready to track your AI visibility?
Start with the buyer questions closest to revenue or deal flow, preserve every answer and source, then measure whether the work changed the shortlist.