Reaudit is brand visibility for the agentic era — the platform that makes AI agents discover, cite, and recommend your brand. It tracks how ChatGPT, Claude, Perplexity, Gemini, and Microsoft Copilot cite your brand across 11 AI engines, generates GEO-scored content in 10+ languages, and ships a 180-tool MCP (Model Context Protocol) server — the largest published marketing MCP on npm — so AI agents can operate your entire marketing stack via natural language.

What is the agentic era?

The agentic era is the shift from humans browsing websites to AI agents discovering, comparing, and recommending products on their owner's behalf. ChatGPT, Claude, Perplexity, and Gemini already answer ~25% of online searches without showing blue links. AI recommendations convert ~4.4x higher than traditional search results. Soon those same agents will operate marketing tools and run workflows autonomously via the Model Context Protocol (MCP). Reaudit is built for both halves of the transition — visibility today, agent-operated marketing tomorrow.

Which AI engines does Reaudit track?

Reaudit tracks brand visibility across 11 AI engines: ChatGPT (OpenAI), Claude (Anthropic), Perplexity AI, Gemini (Google), Google AI Overviews, Google AI Mode, Microsoft Copilot, Meta AI, Grok (xAI), DeepSeek, and Mistral AI. It monitors brand mentions, citations, sentiment, share of voice, citation probability, and ranking position on each engine, updated daily at 3 AM UTC.

What is the Reaudit MCP server?

The Reaudit MCP (Model Context Protocol) server exposes 180 tools that let AI assistants like Claude Desktop, Cursor, ChatGPT Desktop, OpenAI Codex CLI, OpenClaw, Windsurf, Gemini CLI, and any MCP-compatible client operate the entire Reaudit platform via natural language — running audits, generating content, publishing to WordPress, running creator outreach, attributing revenue, and more. It is the largest published marketing MCP server on npm (@reaudit/mcp-server), with OAuth 2.0 + PKCE authentication and 5 interactive MCP Apps.

How is Reaudit different from SEMrush, Ahrefs, and Moz?

Traditional SEO tools like SEMrush, Ahrefs, and Moz track Google blue-link rankings and backlinks. Reaudit tracks how brands appear in AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and other AI engines — a fundamentally different signal that traditional tools don't measure. Reaudit also closes the loop with revenue attribution (mapping AI bot crawls and AI referrals to real Stripe revenue) and ships a 180-tool MCP server so AI agents can operate the entire platform via natural language.

How is Reaudit different from other AI visibility tools like Profound, AthenaHQ, or Otterly?

Most AI visibility competitors stop at the monitoring dashboard. Reaudit ships the monitoring AND the full marketing operating system on top: GEO-scored Content Factory, 21-step GTM Strategy Builder, Spark creator outreach with 3PL shipping, Revenue Attribution from AI traffic to Stripe, CRO suite, Paid Intelligence, and a 180-tool MCP server (largest in marketing). Reaudit is the only AI visibility platform that is also MCP-native — built so AI agents can operate the platform end-to-end via natural language, not just read its data.

What is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO), also called Answer Engine Optimization (AEO), is the practice of optimizing content to be cited and recommended by AI-generated search results from engines like ChatGPT, Claude, Perplexity, and Gemini. Unlike traditional SEO which targets ranking in Google's blue links, GEO targets being the brand the AI agent surfaces and cites when answering a user's query.

All use cases

Reporting & Automation Only on Reaudit

GitHub Auto-Fix PRs for SEO & GEO Issues

Your agent finds the issues and opens the pull request that fixes them.

command

Run an audit on [website] with the Reaudit MCP, propose SEO and GEO fixes for the critical issues, and open a pull request on our connected GitHub repo with the changes — meta tags, schema markup, robots.txt rules for AI crawlers, llms.txt. Summarize each change in the PR description so the team can review and merge.

Tools this workflow calls:run_audit propose_seo_fixes open_seo_fix_pr

What this use case can do for you

Most audits die in a backlog because the findings never become code. This closes the loop: the agent audits the site, generates the actual fixes, and opens a reviewable pull request — turning "23 issues found" into "1 PR awaiting review".

Everything ships through your normal code review process. Nothing lands on the site without a human approving the diff.

What you get

Audit with critical issues identified
Concrete code fixes: meta tags, schema, robots.txt, llms.txt
Pull request opened on your connected repo
Per-change explanation in the PR description

Run This Workflow Today

Connect the Reaudit MCP to your agent in under 60 seconds, paste the command, done.

Connect Your MCP Browse All Use Cases

GitHub Auto-Fix PRs for SEO & GEO Issues

What this use case can do for you

What you get

Related use cases

Full SEO + GEO Audit to Client PDF

llms.txt + Agent Readiness Setup

Find Pages Losing AI Citations

Run This Workflow Today

GitHub Auto-Fix PRs for SEO & GEO Issues

What this use case can do for you

What you get

Related use cases

Full SEO + GEO Audit to Client PDF

llms.txt + Agent Readiness Setup

Find Pages Losing AI Citations

Run This Workflow Today