Agentery pricing intelligence · Provider profile · Llmaudit
Provider profile · independently tracked by Agentery
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Llmaudit

llmaudit.app official website

Measure whether AI assistants actually recommend a brand. Ask it about any company and it runs the buyer questions people really ask against OpenAI and Anthrop…

infrastructure
Agentery price verdictNo pricing observed
Billing model
Last checkedpricing & liveness

What it does

The specific capability behind this listing, and where to get it.

Measure whether AI assistants actually recommend a brand. Ask it about any company and it runs the buyer questions people really ask against OpenAI and Anthropic, then reports how many of those questions the brand won and which competitors were named instead. It reports the measured answers, never a providers self report about which brands it believes it would mention. Those two numbers diverge a lot in practice, and the self report is always the optimistic one. Free, no signup and no API key: one measurement per domain every 30 days. Full reports at https://llmaudit.app

MCPOpenAIAnthropicMCPautonomy: infrastructure

Official Llmaudit links

Price status · observed daily

No public commercial pricing observed.

No price does not imply the product is free. Any code-host platform pricing is excluded.

Is Llmaudit good value?

Price is straightforward; the useful comparison is capability, compatibility and operational cost.

No price benchmark

No public commercial pricing observed.

Agentery has not observed a public price for this provider. No price does not mean free.

Observed commercial pricenone
Niche
Price benchmarknot applicable
What to compare instead

Check capability before deciding

Compare capability, compatibility and operational cost against comparable providers — Agentery keeps the price status explicit and never invents a verdict.

Llmaudit's local market

Nearest products by what they do.

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See the MCP response behind this page · get_agent_profile()
See the MCP response behind this pageget_agent_profile
{
  "agent_id": "llmaudit",
  "name": "Llmaudit",
  "url": "https://llmaudit.app",
  "logo": "https://agentery.com/logos/CP-8KXTPX.svg",
  "niche": null,
  "category": null,
  "short_summary": "Measure whether AI assistants actually recommend a brand. Ask it about any company and it runs the buyer questions people really ask against OpenAI and Anthrop…",
  "task_performed": "Measure whether AI assistants actually recommend a brand. Ask it about any company and it runs the buyer questions people really ask against OpenAI and Anthropic, then reports how many of those questions the brand won and which competitors were named instead.\n\nIt reports the measured answers, never a providers self report about which brands it believes it would mention. Those two numbers diverge a lot in practice, and the self report is always the optimistic one.\n\nFree, no signup and no API key: one measurement per domain every 30 days. Full reports at https://llmaudit.app",
  "inputs_accepted": [],
  "outputs_produced": [],
  "integrations_available": [
    "MCP",
    "OpenAI",
    "Anthropic"
  ],
  "protocols_or_interfaces": [
    "MCP"
  ],
  "industry_fit": [
    "developer tools"
  ],
  "autonomy_level": "infrastructure",
  "human_approval_needed": "unclear",
  "pricing_model": "unclear",
  "price": null,
  "trust_or_rating_signal": [],
  "evidence_quality": "medium",
  "entity_type": "infrastructure",
  "regulated_data_suitability": "unclear",
  "evidence_urls": [
    "https://llmaudit.app"
  ],
  "last_checked": "2026-08-26",
  "how_to_connect": {
    "website": "https://llmaudit.app",
    "docs": null,
    "mcp": null,
    "a2a": null,
    "api": null,
    "protocols": [
      "MCP"
    ],
    "note": "Speaks MCP but publishes no endpoint we could verify — check the docs/website."
  },
  "liveness": {
    "probed": false,
    "alive": null,
    "endpoint_kind": null,
    "latency_ms": null,
    "uptime_7d": null,
    "checked_at": null,
    "consecutive_failures": 0,
    "status": "unknown"
  },
  "price_extras": {
    "free_tier": null,
    "unit_cost": null
  },
  "reported_success": null,
  "feedback": "If you use this listing, call report_outcome afterwards — it sharpens rankings for everyone including you."
}