# RankScale — Track your brand's visibility across ChatGPT, Gemini, Perplexity, and 17+ AI engines.

> Source: CitedIndex — https://citedindex.com/rankscale (structured, researched, re-verified)
> Facts last verified: 2026-07-24

RankScale is a GEO (generative engine optimization) platform that tracks how brands appear in AI-generated answers across ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, Copilot, DeepSeek, Grok, and Mistral. It gives marketers a visibility score, competitor benchmarking, and citation tracking across 17+ AI engines and 240+ countries. Clients include Bosch, UBS, Cartier, and O2, and pricing runs from an Essentials plan to an Enterprise tier with REST API access, positioning RankScale as an AI-search analytics suite for agencies, publishers, and enterprise marketing teams navigating the shift from traditional SEO to answer-engine visibility.

| Fact | Value |
| --- | --- |
| Website | https://rankscale.ai |
| API | Yes |
| Best for | Enterprise marketing teams and agencies that need broad AI-engine coverage |
| Not for | Solo operators or small teams wanting a simple pass/fail AI-visibility check, or anyone needing hard traffic/revenue attribution tied directly to AI mentions. |

## Verdict

RankScale is a credible, actively used GEO/AI-visibility platform with broad engine coverage and genuine enterprise customers, but its credit-based pricing gets expensive fast for API and white-label access, and it stops short of proving ROI.

## How it works

1. Scheduled prompts run against 17+ AI engines, including ChatGPT, Perplexity, Google AI Overviews and AI Mode, Gemini, Claude, Copilot, DeepSeek, Grok and Mistral.
2. Each account gets brand dashboards charting a visibility score, share of voice and sentiment trend over time, with competitors auto-identified for benchmarking.
3. A citation-analysis module surfaces which sources the AI engines actually quote or link to for your prompts.
4. Usage draws down a shared credit pool — each engine query against a tracked prompt consumes a fraction of a credit, with pricier engines costing more per query.

## Who it's for

- Enterprise marketing teams and agencies that need broad AI-engine coverage (17+ engines) and competitor benchmarking, and can absorb credit-based pricing that scales toward $385-$780/month for API and white-label access.
- Agencies on the Growth plan that want white-label reporting and a REST API to manage AI-visibility reporting across many client brands from one account.
- Content teams that want to reverse-engineer what earns a citation, using the citation-analysis module to see which sources the AI engines already quote.

## Strengths and weaknesses

- ✓ Covers 17+ AI engines (ChatGPT, Perplexity, Gemini, Claude, Copilot, AI Overviews, and more) from the entry-level plan with no per-engine surcharges
- ✓ Citation analysis shows which sources AI engines actually cite, useful for shaping content strategy
- ✓ Used by large brands including Bosch, UBS, Cartier, O2, Otto, StepStone, and OMR, plus agencies like Coalition Technologies, Dentsu, Publicis Sapient, and WPP Media
- ✓ Publishes named, agency-attributed case studies with quantified results rather than only aggregate marketing claims, MiniFinder reached the #1 AI-search spot in 90 days, SoWork hit 100% visibility and a 63% market lead in 90 days, and an AI SMS platform generated $280K in pipeline from AI search
- ✗ No traffic attribution: RankScale can't connect AI visibility to actual website sessions, leads, or revenue, there's no GA4 integration or conversion tracking
- ✗ Credit-based billing is unpredictable, engine costs vary from 0.25 to 2+ credits per query, and reviewers report exhausting monthly credits quickly on smaller plans
- ✗ Visibility scores can swing 10-15% week-to-week even without site changes due to underlying AI model volatility, and sentiment analysis isn't always accurate
- ✗ Client-ready reporting exports are limited, one reviewer found no way to export a clean overview list, making agency client reporting mostly manual
- ✗ Self-reported user counts are inconsistent: RankScale's own homepage states "2,000+" in its stats bar but "1,000+ active users" repeated in the hero, features, and closing call-to-action
- ⚠ No GA4 or analytics integration for connecting AI visibility to traffic or revenue
- ⚠ Diagnoses AI-visibility issues but provides no automated content fixes or schema deployment
- ⚠ REST API is gated behind the Growth ($385/mo) and Enterprise ($780/mo) tiers only
- ⚠ Sparse third-party review coverage on G2/Capterra makes independent at-scale reliability hard to verify

## Key features

- **Brand Visibility Dashboard** — Central dashboard tracking a visibility score across monitored AI engines, filterable by engine and timeframe.
- **AI Rank & Citation Tracking** — Monitors rankings, share of voice, and which sources AI engines cite when answering brand-relevant prompts.
- **Competitor Benchmarking** — Auto-identifies competitors and compares visibility metrics side-by-side across the same AI responses.
- **Page Audits** — Analyzes 200+ technical and content signals, including 94+ deterministic technical checkpoints, to assess how AI-ready a given page is.
- **Prompt Research** — Estimates search volume and decodes user intent for GEO-relevant prompts via semantic analysis.
- **Rankscale MCP** — A read-only remote MCP server that lets Claude, ChatGPT, Cursor, and OpenAI Codex query live visibility, competitor, sentiment, and citation data via natural language, without dashboard or export access; it cannot modify brands, search terms, or settings.

## Use cases

- **Enterprise brand-visibility monitoring** — A marketing team at a large brand tracks how often and how favorably it's mentioned across ChatGPT, Gemini, and Perplexity, and benchmarks that visibility against named competitors over time.
- **Agency AI-search reporting** — An SEO agency on the Growth plan uses the REST API and white-label dashboards to report AI-visibility metrics across dozens of client brands from one account.
- **Content AI-readiness audits** — A content team runs page audits to identify technical and content gaps that may be preventing pages from being crawled or cited by AI engines.

## Integrations

Looker Studio · REST API (Growth & Enterprise plans) · MCP (Claude, ChatGPT, Cursor, OpenAI Codex)

## Sources

- https://rankscale.ai/
- https://rankscale.ai/pricing
- https://rankscale.ai/mcp
- https://rankscale.ai/case-studies
- https://max-productive.ai/ai-tools/rankscale/
- https://checkthat.ai/brands/rankscale/reviews