Rank++

Use Case 1: Correcting Inaccurate or Outdated AI Brand Knowledge

Problem: AI models like ChatGPT or Claude may provide outdated information or "hallucinate" incorrect details about a company’s pricing, features, or mission because they are relying on old training data or fragmented web mentions.
Solution: Rank++ helps founders take control of their brand narrative by generating structured "truth" sources. By using the AI Entity Graph and Citation Builder, users can create a clear, machine-readable map of their business that AI agents use to verify facts.
Example: A SaaS founder notices ChatGPT tells users their software doesn't have an API (which they recently launched). They use the AI Entity Graph to define their new "API" feature in JSON-LD and the Citation Builder to push updated brand metadata to authoritative directories, ensuring the AI finds the correct information during its next web-crawl or RAG (Retrieval-Augmented Generation) process.

Use Case 2: Rapidly Scaling Structured Data for New Product Launches

Problem: Search engine optimization for AI (AEO) requires detailed Schema markup (JSON-LD) for every page, but manually coding these for dozens of new landing pages is time-consuming for indie hackers and small marketing teams.
Solution: The Schema Builder tool automates the creation of AI-optimized metadata. It can detect URLs from a sitemap and generate the appropriate structured data with a single click, allowing a site to become "AI-ready" in minutes rather than days.
Example: An indie hacker launches a directory of 50 AI tools. Instead of writing code for each page, they use the Rank++ Schema Builder to scan their sitemap and instantly generate 50 unique JSON-LD schemas. They then use IndexNow to notify search engines and AI crawlers to index these structured pages immediately.

Use Case 3: Optimizing Blog Content to be Cited as an AI "Source"

Problem: Content creators write high-quality guides, but AI search engines like Perplexity often cite competitors because their content is better structured for "fact extraction" or has higher E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) signals.
Solution: The Content Optimizer analyzes existing articles to provide E-E-A-T scores and entity suggestions. It identifies exactly what specific terms or factual structures are missing that would make the content more "digestible" for an AI model to use as a primary citation.
Example: A marketing agency writes a guide on "The Future of Remote Work." They run the draft through the Content Optimizer, which suggests adding specific entity tags for "hybrid models" and "asynchronous communication." After updating the post and using the KP Engine to create social prompts for distribution, they see their article appearing as a cited source in Perplexity answers.

Use Case 4: Benchmarking Brand Visibility Across Different AI Models

Problem: Marketers don't know how their brand is perceived across different AI platforms. A company might be recommended by Claude but completely ignored by ChatGPT, making it difficult to know where to focus optimization efforts.
Solution: The Prompt Lab and Insights tools allow users to test and track brand mentions across multiple models simultaneously. This provides a "share of voice" metric specifically for the AI ecosystem.
Example: A Marketing Director uses the Prompt Lab to ask ChatGPT and Claude: "What are the top 5 project management tools for small teams?" They discover their tool is mentioned in Claude but not ChatGPT. They use the Insights dashboard to monitor this gap and prioritize building more high-authority citations in the directories that ChatGPT's crawler weighs more heavily.