Knownwise
Knownwise helps brands and marketing teams track how ChatGPT, Claude, Gemini, and Perplexity answer buyer questions about their category. Its dashboard records brand mentions, competitor recommendations, answer position, sentiment, and cited sources, making it possible to compare visibility across AI assistants over time. Users can run a free site audit that scores AI readiness and highlights issues affecting how assistants read and cite a website. Paid plans schedule recurring answer tracking across all four assistants, with plan-based prompt and answer limits, alerts, and analytics connections. On eligible plans, an agent can prepare website fixes as GitHub pull requests for review; users approve changes before they go live. The service also offers a free brand check and audit for evaluating AI search visibility.
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Depra AI
Depra AI is a web-based AI visibility tracking tool for consumer brands and teams that want to understand how AI search answers describe their products. It checks buyer questions in ChatGPT with web search, Gemini, Google AI Overviews and Perplexity, then stores the answers and cited pages for review. Users can compare brand mentions with competitors, monitor visibility, average position, sentiment and share of voice, and review suggested actions for improving how their sites appear in AI answers. The platform supports questions in English and Hinglish across 40 markets, with reporting features that include CSV exports and cited-source analysis. Plans are organized for individual brands, growing teams and agencies managing multiple client projects. A read-only MCP server also lets supported AI applications retrieve workspace and project data.
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Lettertrace
Lettertrace is an open source AI visibility monitoring platform that measures how often Claude, ChatGPT, Gemini, and Google AI Overviews mention and recommend a brand. Users add the topics their buyers ask about, and the platform automatically generates natural prompt variations to monitor how AI assistants actually answer those questions. Every run becomes a datapoint, allowing teams to track visibility, prominence, sentiment, and share of voice per topic and model over time. Competitor benchmarking shows how often rival brands appear, which companies AI models recommend, and how a brand performs against each competitor. Reports turn raw AI answers into the metrics AEO and GEO teams need, including brand visibility, share of voice, sentiment, recommendations, and competitive performance. Scheduled monitoring can run daily or weekly to build actionable trend lines, while multi-model analysis makes it possible to compare results across answer engines side by side.
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Spegla
Spegla measures whether AI assistants recommend your brand when buyers ask them for a product in your category.
Each run generates ten buying questions a real customer would ask, then sends every one to ChatGPT, Claude, Gemini and Perplexity through their official APIs. That is forty answers per run, collected the same way each time so two runs are comparable. Your brand name never appears in any of the questions, so a mention only counts if the assistant raises it on its own.
You get a 0-100 visibility score combining how often you are named with how early you appear, your mention rate, the rival brands recommended in your place, the sources each assistant cited, and the full text of every answer.
Spegla runs in 15 European languages, with questions written in the market language rather than translated from English. Tracking re-asks the same questions weekly or monthly so scores stay comparable over time.
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