# Llmrefs (llmrefs.com): agent-readiness 69/100, grade C

> Rank 125 of 1000 on Qomvia (rubric v2.3.0). Last scanned 2026-09-30T03:50:44.524Z. Page: https://qomvia.com/site/llmrefs-com  JSON: https://qomvia.com/api/score/llmrefs-com

## Summary
Category: Software and SaaS
{
  "known": true,
  "confidence": "medium",
  "summary": "LLMrefs is an analytics platform specifically designed to track brand visibility, rank, and citations within generative AI answer engines. It helps marketers and SEO professionals optimize their content's performance across platforms like ChatGPT, Google AI Overviews, Perplexity, Claude, and Gemini.",
  "offerings": [
    "Generative AI search analytics",
    "Brand visibility tracking in AI",
    "AI search rank and citation tracking",
    "Competitor benchmarking for AI search",
    "Keyword optimization for generative AI engines",
    "API access for data export"
  ],
  "audience": "Digital marketers, SEO professionals, Content strategists, Brand managers, Agencies, Ambitious growth teams",
  "geography": null,
  "strengths": [
    "Specialized focus on generative AI search analytics",
    "Covers multiple major AI answer engines",
    "Provides competitor benchmarking capabilities",
    "Focuses on identifying sources and citations in AI outputs",
    "Offers API access for data integration"
  ],
  "unknowns": [
    "Specific pricing tiers and detailed feature breakdown",
    "Company size, age, or funding status",
    "User base size or specific case studies/success metrics",
    "Geographic market focus or global reach",
    "Specific methodologies or AI models used for tracking"
  ],
  "alternatives": [
    "semrush.com",
    "ahrefs.com",
    "surferseo.com",
    "frase.io",
    "marketmuse.com"
  ],
  "business": "Analytics platform providing insights into brand visibility, rankings, and citations within generative AI answer engines for digital marketers and SEO professionals.",
  "competitors": [
    "glose.ai",
    "marketmuse.com",
    "frase.io",
    "brightedge.com",
    "conductor.com",
    "seoclarity.net",
    "contentatscale.ai"
  ]
}

## Score by group
- Machine access: 24/24
- Content legibility: 23/26
- Discovery surface: 9/20
- Identity & policy: 5/12
- Agent-facing performance: 8/8
- Agent protocols: 0/10
Failing checks:
- [blocker] No API or feed to read
- [blocker] No MCP endpoint to call
- [improvement] No stated policy for automated access
- [improvement] No machine-readable contact
- [improvement] No Content Signals

## Add-on packs
- E-commerce & product feeds: 15/100 (not part of the public score)

## AI mentions and rivals
Named in 1 AI assistant answers.
- frase.io: 87/100 https://qomvia.com/site/frase-io
- brightedge.com: not measured
- conductor.com: 68/100 https://qomvia.com/site/conductor-com
- seoclarity.net: not measured

## Named for
- 1. Which AI visibility monitoring platform should a small or medium-sized company use to track brand mentions, competitors and recommendations across LLMs? https://qomvia.com/answers/1-which-ai-visibility-monitoring-platform-should-a-small-or-medium-sized-company
- best tool to check if AI agents can read my website https://qomvia.com/answers/best-tool-to-check-if-ai-agents-can-read-my-website

## FAQ
Q: Is Llmrefs readable by AI agents?
A: Llmrefs (llmrefs.com) scores 69 out of 100 for agent readiness, grade C, measured today, on 30.09.2026. 0 of 3 agent classes we test can use the site, and 2 checks currently block agents outright.

Q: What stops agents from using llmrefs.com?
A: The heaviest problems are: No API or feed to read; No MCP endpoint to call; No stated policy for automated access; No machine-readable contact; No Content Signals. Each one is scored from a public HTTP response, and the fix for each is listed in the signed-in report.

Q: How is the Llmrefs agent-readiness score calculated?
A: Qomvia fetches public pages of llmrefs.com as an AI agent would and runs the core checks every site gets across 6 groups: Machine access, Content legibility, Discovery surface, Identity & policy, Agent-facing performance, Agent protocols. Checks are weighted by how much an agent loses when they fail, and the score is expressed out of 100. E-commerce & product feeds checks are scored separately (15/100) and do not affect the public score or rank.

Q: How often is this score updated?
A: Public scores are re-checked when the site is re-scanned; tracked domains are re-scanned weekly and their owners are alerted when a check regresses. This page shows the scan from today, on 30.09.2026.

## Related
- Leaderboard: https://qomvia.com/leaderboard — category ranking: https://qomvia.com/leaderboard/saas-software
- Score another site: https://qomvia.com/