Lettertrace: open-source tool for tracking brand mentions in AI models

Lettertrace launched during the week of August 10, 2026, as a free, open-source platform for tracking how AI assistants mention and recommend your brand. The tool queries Claude, ChatGPT, and Gemini with automatically generated variations of topics relevant to your audience, then analyzes which products and companies appear in responses, how prominently, and with what sentiment.

The platform is self-hosted, MIT-licensed, and runs on the user’s own API keys from Anthropic, OpenAI, and Google. Data stays in the user’s own Supabase instance. There is no per-seat pricing or vendor markup, and monitoring runs on scheduled automated cycles. Results are surfaced as share-of-voice metrics against competitors, with trend tracking over time.

For product managers, the significance is that discovery patterns are shifting. A growing portion of users now ask AI assistants which tool to use for a specific task before running a web search — and the products that get recommended, or don’t, often have little visibility into how they appear in these responses. Traditional SEO analytics and share-of-voice dashboards measure organic search and media mentions. They capture nothing about what Claude says when a user asks which project management tool to adopt, or what ChatGPT recommends when a designer asks which prototyping app to learn.

Lettertrace is among the first open tools built specifically for this monitoring gap. The category it occupies — sometimes called AEO (AI Engine Optimization) or GEO (Generative Engine Optimization) — is still forming. Product teams at software companies that compete in crowded categories now have a free, instrumentable way to track whether their positioning holds in AI-generated recommendations and how it compares to competitors over time.

Product managers working on distribution, positioning, or GTM strategy are the primary audience. The tool is also relevant for anyone responsible for content or messaging who wants to understand whether their brand’s stated positioning translates into how AI models describe the product in practice.