Understanding AI search visibility
CiteGraph is an AI search visibility and citation tracking platform built for founders, marketers, and SEO teams. It tests questions potential buyers ask across ChatGPT, Claude, Gemini, and Perplexity, showing which products get mentioned, which pages get cited, and where visibility gaps exist. Its focus is practical rather than superficial.
Measuring what AI assistants see
CiteGraph creates buyer-style questions instead of relying on keywords, then runs each question repeatedly across multiple AI engines. Users can review answers, cited sources, competitors, blind spots, and share of voice. It also provides specific fixes and drafted content for missing answers. However, its English-only questions limit localization and regional analysis.
This platform emphasizes evidence by keeping answers verbatim and displaying citation sources, while its methodology uses official grounded-search APIs rather than scraping chat interfaces. Weekly scans help track changes, and its MCP server lets coding agents access scan findings. The main con is that sampled results cannot fully represent every AI response or market.
A practical AI visibility tool
Overall, CiteGraph is a focused tool for understanding how AI assistants discover and recommend software. Its combination of repeated testing, citation mapping, competitor tracking, and actionable recommendations makes it more useful than a simple visibility score. For teams prioritizing AI-driven discovery, it offers a structured way to investigate and improve visibility.
