Findabl vs Peec AI
Peec models AI visibility from search data. Findabl uses direct measurement via real prompts to ChatGPT, Perplexity, Claude, and Gemini.
Findabl
Findabl is AI Citation Intelligence — measurement plus the action layer above it. We send real prompts to ChatGPT, Perplexity, Claude, and Gemini, then produce a 30-day action plan across four workstreams (entity anchoring, citation velocity, on-page content, measurement loop). Honest baseline, homonym detection, per-target competitive diagnosis, confidence intervals on every reading, and one-click self-audit publishing — no modeling, no estimation.
Peec AI
Peec AI is a visibility platform that models AI citations from search-derived signals and extrapolated share-of-voice across standing prompt libraries. The output is a modeled estimate of visibility — not the actual AI answer. Popular with agencies that want clean dashboards over methodological rigor.
Feature-by-Feature Comparison
The Verdict
Peec’s dashboards look great, but the numbers underneath are modeled from search data — not captured from real AI prompts. The University of Toronto finding that AI engines share only 4–15% of their cited sources with Google’s top results means search-derived modeling is structurally unreliable for AI citation tracking. Findabl sends real prompts, stores every response, and gives you citation data you can actually audit.
Market Context (2026)
Peec, Profound, and Scrunch all rely on modeled metrics of varying kinds. Findabl is the direct-measurement alternative: every citation comes from a real prompt to a real engine, stored and audit-ready.
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Honest baseline + four-workstream action plan in 60 seconds. Real prompts, every recommendation grounded in your data.
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