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2026 Comparison

Findabl vs Ahrefs Brand Radar

Brand Radar models your AI share of voice from search-volume data across six engines. Findabl sends real prompts to all four major engines, including Claude, and stores every response so you can audit exactly what was said and why.

F

Findabl

Findabl is AI Citation Intelligence: measurement plus the action layer above it. We send real prompts to ChatGPT, Gemini, Perplexity, and Claude, then produce Actions across four workstreams (entity anchoring, citation velocity, on-page content, measurement loop). Honest baseline, homonym detection, per-target competitive diagnosis, confidence intervals after four weeks of tracking, and one-click self-audit publishing. No modeling, no estimation.

A

Ahrefs Brand Radar

Ahrefs Brand Radar is an AI visibility module built on top of an Ahrefs subscription. It tracks how brands appear across six AI surfaces (ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews, and AI Mode) using a large keyword-modeled prompt corpus, and reports an AI Share of Voice that is weighted by Google search volume. It is strong for SEO teams that want benchmarking at scale, but its headline metrics are modeled estimates, it refreshes most engines monthly, and it does not cover Claude.

Feature-by-Feature Comparison

Feature
Findabl
Ahrefs Brand Radar
Sends real prompts to AI engines
Yes, every scan runs live prompts
Yes, queries are executed in AI interfaces
Headline metric is direct measurement vs modeled estimate
Direct: cited or not cited from the actual response
Modeled: Share of Voice and Impressions weighted by search volume
AI engines covered
ChatGPT, Gemini, Perplexity, Claude
ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews, AI Mode
Tracks Claude (Anthropic)
Stores every raw AI response for audit
Yes, every response saved and audit-ready
Yes, raw responses stored and searchable
Built for regulated industries (legal, finance, pharma)
Yes, audit-ready evidence and per-industry rule checks
Not positioned for compliance use
Citation Gap Analysis (why competitors get cited)
Mentions and citations reporting, not gap analysis
GEO Score (cited vs not cited)
AI Share of Voice instead
Homonym and brand-collision detection
Not public
Actions
Yes, prioritized and testable
No, reporting and benchmarking focused
Refresh cadence
On-demand scans plus daily monitoring on paid plans
Monthly for most engines, 90-day reporting window
Competitor share-of-voice benchmarking at scale
Per-prompt competitor citation tracking
Yes, a core strength across 391M+ modeled prompts
Unlinked mention and social crawling (Reddit, YouTube, TikTok)
Starting price
Free to start, then $49/mo
From ~$828/mo (Ahrefs base plus AI index bundle)
3cubed.ai Research, June 2026

AI recommendations are probability-based

Noriko Yokoi, Thorsten Linz, and the 3cubed.ai Research Team ran a pre-registered study across 26 unaided buyer questions, four AI engines, and three controlled conditions. The result: brand recommendations shift by engine, query, retrieval source, and time.

2,580
total AI runs

26 unaided buyer questions across ChatGPT, Gemini, Claude, and Perplexity.

50%
repeat consistency, live search on

With live web search on, the odds of the same brand leading twice in a row fall to roughly 50%, even for well-known brands.

27%
four-engine agreement

Only 7 of 26 questions produced the same top brand across all four engines.

10%
median appearance rate

The typical brand appeared in just 1 in 10 responses for its own category question.

28.6%
source concentration

The top 10 cited domains account for 28.6% of all AI source citations.

What marketers should measure

  • One-off checks miss the real picture. Brands need repeated measurement across prompts, engines, and time.
  • Engine disagreement is opportunity. If the four engines pick different brands, no one owns that buyer question yet.
  • Gatekeeper publications matter. The highest-leverage move is knowing which source domains AI engines cite most in your category.
  • Appearance rate beats yes-or-no status. The useful question is how often you appear, where, and whether that number is moving.

Put the study to work

Findabl tests your buyer questions across ChatGPT, Gemini, Claude, and Perplexity so you can see your own citation coverage, source domains, and gaps.

Get Your Free GEO Score →Download the ungated white paper →

The Verdict

Brand Radar does real things well: it benchmarks AI share of voice against competitors at scale, crawls linked and unlinked mentions across the web, Reddit, YouTube, and TikTok, and plugs into the Ahrefs SEO suite. But its flagship AI Share of Voice and Impressions are modeled estimates weighted by search volume, most engines refresh only monthly, and Claude is not tracked at all. Findabl takes the opposite approach: it sends real prompts to ChatGPT, Gemini, Perplexity, and Claude, stores every raw response, and shows you the exact citations and source domains the engines used, every one auditable. For a regulated team in legal, finance, or pharma, a stored response you can point to beats a modeled number you have to trust. And at a free entry point rising to $49 a month, Findabl is reachable for the small firms and mid-market teams that an $828-and-up Ahrefs configuration is not built for.

Market Context (2026)

Both tools measure AI visibility, but they sit at different ends of the market. Brand Radar is an enterprise-leaning, benchmark-at-scale add-on inside the Ahrefs suite, priced for SEO teams and agencies. Findabl is direct-measurement AI Citation Intelligence built for teams that need to see and keep the actual AI response, including small firms and regulated industries that an enterprise SEO bundle prices out.

Compare Findabl to other tools

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Honest baseline + four-workstream Actions in 60 seconds. Real prompts, every recommendation grounded in your data.

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