Tooling
Profound
AI-visibility platform for marketers tracking brand mentions and citation share across LLM surfaces. Added Documents (June 2026) and a redesigned Sentiment tool to move beyond surface-level metrics.
What it is
Profound is an AI-visibility platform built for marketers to track how often a brand is mentioned and cited across large language model surfaces. In June 2026 it added a Documents feature and a redesigned Sentiment tool.
Why it matters
As discovery shifts from blue links to generated answers, knowing whether and how a brand surfaces inside LLM responses is the foundation of AI search visibility work.
How it works
Practitioners run prompts representative of buyer questions, then review which sources the model cites and how the brand is described, using the Sentiment tool to gauge tone and Documents to organise supporting evidence.
When it applies
Reach for it when you need to measure and improve a brand's presence inside AI assistant answers rather than traditional search rankings.
Examples
- Tracking citation share for a SaaS brand across comparison style prompts.
- Comparing how favourably an LLM describes your product versus a named competitor.
- Auditing which third party pages an assistant pulls from when recommending your category.
How it is measured
- Brand mention frequency across LLM surfaces
- Citation share relative to competitors
- Sentiment of brand references in answers
- Source domains cited for category prompts
Related terms in Tooling
- Ahrefs Brand RadarAhrefs' AI-visibility module tracking brand mentions and impressions inside AI answers. Surfaces shifts in how brands appear across ChatGPT, Gemini, and other LLM surfaces.
- Project GlasswingAnthropic's research initiative supporting organisations applying AI to high-stakes problems. Expanded in June 2026 to approximately 150 new organisations across 15 countries.
- SISTRIXSEO visibility platform widely used for post-core-update analysis. Source of the analysis that showed Google's May 2026 update favoured intent-aligned pages and the GPT-5.5 citation-pattern shift.