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Mudra landscape Mudra is a frontier AI research lab that automates Answer Engine Optimization (AEO) so your company ranks in AI search, across frontier models like ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and more. Instead of guessing how models see your site, Mudra measures AI visibility, fixes your technical structure, and deploys agents that keep you ahead as AI search evolves.

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Why Mudra exists

AI models are becoming a primary discovery channel. People no longer just “Google”; they ask models for product recommendations, vendor comparisons, deep technical answers, and “what should I use for X?” questions. Models answer using their internal knowledge, past crawls, and the sites they trust. If your company isn’t visible and machine readable in that AI layer, you lose qualified traffic to better structured competitors—even if your product is stronger. Mudra exists to solve this: we are an AEO (Answer Engine Optimization) infra + agent layer focused on AI visibility, not just traditional SEO.

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How Mudra is better than generic AEO

Mudra focuses on the root problem: how AI systems actually perceive, crawl, and rank brands, not just how your content looks on the surface. As a frontier AI research lab, we constantly test edge-case behaviors across models to understand their real ranking signals and how they evolve. Instead of guessing from outside, Mudra integrates at the infra level—your code, structure, and AI-facing files. That lets us run controlled experiments, ship structured changes, and observe how models update their internal knowledge, citations, and crawl patterns. Over time, this forms a closed loop: measure → ship → observe → adapt. This approach is designed for the shift toward an agentic, continuously crawling world. Rather than treating AEO as a one-off checklist, Mudra acts as infra and an agentic layer that keeps your site in sync with how modern AI systems actually learn and decide who to trust.

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What Mudra does for you

  • See how AI models describe and rank your brand — understand how often you are mentioned, where you rank in answers, and how you compare to competitors across key prompts.
  • Measure and improve your technical structure — check titles, headings, structured website data, llms.txt, and other AI-facing signals that make your site easy for models to parse and trust.
  • Track AI referral traffic — attribute traffic coming from AI-driven journeys so you can prove that AI visibility is not just a vanity metric.
  • Deploy agents that work in the background — let agents maintain llms.txt, schema, and AI-optimized content, and surface high-trust conversations where you should be present.
  • Align growth with the questions that matter — monitor the prompts and themes your buyers actually use, and build content and infra around those questions instead of guessing.

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Who Mudra is for

Mudra is built for teams that care about long-term, compounding growth through AI search:
  • B2B SaaS and AI companies
  • Developer tools and infra platforms
  • Data and analytics products
  • Agencies or studios managing multiple domains
If your customers are already asking AI models which tool to use—or if you expect that shift to happen soon—Mudra gives you the infrastructure and agents to be on the right side of that change.

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How Mudra makes you win

  1. Audit AI crawlability and visibility — we assess your structured data, AI visibility, and deeper metrics to understand whether you’re being mentioned by AI, how you’re being mentioned, and which sources AI models prefer to cite and crawl.
  2. Process and analyze the signals — we pinpoint where you appear, where you can improve, and which actions and agentic enhancements will help you rank in AI search.
  3. Deploy agents and orchestrated content workflows — technical-structure agents optimize schema, semantic understanding, and relationship graphs; handle citation acquisition and outreach to authoritative sources; and elevate crawlability so your brand becomes a canonical reference. In parallel, multi-agent content workflows boost AI visibility by producing and distributing AI-ready content across the queries and surfaces where models pull answers.