Anon

Use Case 1: Tracking Brand Visibility in LLM Responses

Problem: Marketing and SEO teams do not know if AI search assistants like ChatGPT, Claude, or Perplexity recommend their software or describe their features accurately when users ask for recommendations.
Solution: The platform runs live queries mimicking buyer intent across major AI engines to determine if the brand is recommended and how it is described.
Example: A flight school tracks whether ChatGPT lists them when a user searches for "best flying school in South Jersey."

Use Case 2: Identifying AI Crawler Indexing Issues

Problem: A company's website might block AI crawlers or lack the necessary metadata structures, leading to incorrect or missing information in LLM answers.
Solution: The tool scans the domain's robots.txt, sitemap, structured data, and checks for the presence of files like llms.txt or agent.json.
Example: A SaaS provider discovers that Claude and Copilot are serving 404 errors when seeking their /llms.txt file, prompting them to publish one.

Use Case 3: Optimizing Programmatic Agent Workflows

Problem: AI agents researching software on behalf of buyers often drop off when they cannot programmatically sign up or test an API, defaulting to competitors instead.
Solution: The tool evaluates whether headless signup paths exist and flags recommendations to implement APIs like POST /api/v1/register to keep autonomous agents engaged.
Example: An API company notices they lose recommendations because agents cannot bypass manual registration forms, leading them to set up an automated signup path.