WhiteRank

Introduction to WhiteRank

WhiteRank is an AI search visibility platform designed to help brands understand and improve how large language models (LLMs) such as ChatGPT, Google Gemini, Anthropic Claude, Perplexity AI, DeepSeek, Microsoft Copilot, Meta AI, and Grok perceive, reference, and cite their content. It focuses on entity-level optimization, structured data, and the sources AI systems use to generate answers.

As AI-generated responses increasingly precede traditional search listings, organizations need clear visibility into how AI models interpret their brand, which sources are cited, and which signals influence recommendations. WhiteRank provides AI site audits, LLM visibility scoring, prompt testing, and citation analysis to support teams transitioning from traditional SEO to AI-focused search optimization (AI SEO, LLM SEO, and Generative Engine Optimization).

Key Takeaways

• Tracks brand visibility and citations across multiple AI models (ChatGPT, Gemini, Claude, Perplexity, and others)
• AI site audits with an LLM Visibility Score, entity and topic coverage, and structured data insights
• Prompt testing and visibility simulation to see how models answer and whether they cite the brand
• Citation analysis to identify the source URLs AI systems reference in generative answers
• AI bot analytics to monitor discovery, crawlability, indexing, and entity signals over time
• Topics and Entities mapping to strengthen knowledge graph alignment and entity-first content
• Integrated AI writing assistance for entity-focused content drafting
• Multi-site and competitor tracking with plan-based limits

How WhiteRank Works

WhiteRank begins with a scan of the brand’s website and related entities. It evaluates structured data, entity coverage, and AI crawlability, then runs prompts across supported AI models to measure visibility and citation frequency. Scans typically complete within minutes and produce a consolidated LLM Visibility Score and audit report.

The platform highlights gaps in entity signals, topic coverage, and structured data that may prevent accurate model understanding or citation. It identifies which sources are being cited for brand-relevant queries and where content or schema improvements are needed.

Teams then apply the recommended fixes—such as refining entities, improving schema markup, and aligning content with key topics—and monitor impact over time. Prompt simulators, keyword and competitor tracking, and AI bot analytics help validate changes and maintain AI search visibility as models evolve.

Core Benefits and Applications

• AI visibility monitoring: Understand how models rank, cite, and recommend the brand, and which signals influence answers.
• Entity and schema optimization: Improve knowledge graph alignment, structured data quality, and topic coverage.
• Prompt and keyword tracking: Test prompt variations, track keyword-level visibility, and monitor competitor presence.
• Citation improvement: Identify and influence the sources AI systems use, increasing the likelihood of accurate brand mentions.
• Content operations: Use AI writing assistance to produce entity-first content aligned with audit recommendations.
• Multi-site and agency workflows: Track multiple domains and compare performance across models from a single dashboard.

Pricing Overview

Notes: Most scans complete in 2–3 minutes. Subscriptions can be canceled at any time. Supported models include ChatGPT (OpenAI), Google Gemini, Anthropic Claude, Perplexity AI, DeepSeek, Microsoft Copilot, Meta AI, and Grok.