GEOScore vs Relativity
Side-by-side comparison to help you choose.
| Feature | GEOScore | Relativity |
|---|---|---|
| Type | Product | Product |
| UnfragileRank | 31/100 | 35/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 1 |
| Ecosystem | 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 5 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Crawls and analyzes website content against indexing and retrieval requirements for three distinct AI search engines (ChatGPT, Perplexity, Gemini) using engine-specific crawling patterns and citation detection algorithms. The scanner likely simulates how each engine's retrieval-augmented generation (RAG) pipeline would discover, parse, and rank the site's content, then surfaces visibility gaps specific to each platform's indexing behavior and content preference signals.
Unique: Focuses exclusively on AI search engine indexing and retrieval requirements (ChatGPT, Perplexity, Gemini) rather than traditional Google SEO, requiring engine-specific crawling simulation and citation detection logic that differs fundamentally from Googlebot-centric tools like SEMrush or Ahrefs
vs alternatives: Addresses an emerging SEO reality that traditional platforms ignore; while Semrush and Ahrefs optimize for Google, GEOScore optimizes for the AI search engines that are becoming traffic drivers for content-heavy sites
Executes a fixed set of 11 automated technical checks against the scanned website, likely covering content structure (headings, semantic HTML), indexability signals (robots.txt, meta tags, canonical URLs), citation formatting (author/date/source attribution), content freshness, and mobile responsiveness. Each check is scored as pass/fail or partial, aggregated into a composite visibility score that indicates readiness for AI search engine discovery and ranking.
Unique: Audit checks are specifically calibrated for AI search engine requirements (citation formatting, content structure for RAG pipelines, indexability for non-Google crawlers) rather than generic SEO best practices, requiring domain knowledge of how ChatGPT, Perplexity, and Gemini parse and rank content
vs alternatives: More targeted than Lighthouse or PageSpeed Insights (which focus on performance/UX) and more AI-search-specific than Moz or Ahrefs (which optimize for Google); fills a gap in SEO tooling for an emerging traffic channel
Disaggregates the audit results by AI search engine (ChatGPT, Perplexity, Gemini), surfacing engine-specific optimization gaps and recommendations. This likely involves mapping the 11 technical checks to each engine's known indexing behavior and content preferences (e.g., Perplexity may prioritize fresh content and source attribution differently than ChatGPT), then generating tailored remediation suggestions for each platform.
Unique: Disaggregates visibility and recommendations by AI search engine rather than treating them as a monolithic 'AI search' category, acknowledging that ChatGPT, Perplexity, and Gemini have different indexing behaviors, content preferences, and citation requirements
vs alternatives: More granular than generic 'AI search readiness' scores; enables users to optimize strategically for the engines that matter most to their traffic, rather than applying one-size-fits-all recommendations
Provides a freemium model where users can run one or more full 11-point audits without payment, removing friction for initial discovery and experimentation. The free tier likely includes the core audit and per-engine breakdown but may exclude features like historical tracking, competitive benchmarking, or detailed remediation guidance that are reserved for paid tiers.
Unique: Removes friction for initial discovery by offering a full audit (11 checks, multi-engine breakdown) at no cost, betting that users will upgrade to paid tiers for historical tracking, competitive benchmarking, or ongoing monitoring
vs alternatives: Lower barrier to entry than Semrush or Ahrefs (which require paid subscriptions for any meaningful audit); similar freemium approach to Moz's free SEO tools but specialized for AI search rather than Google
Implements a web crawler that fetches and parses website content using patterns optimized for AI search engine indexing behavior. The crawler likely respects robots.txt and crawl-delay directives, extracts semantic content structure (headings, paragraphs, lists, tables), detects citation metadata (author, date, source), and analyzes content freshness and mobile rendering. Results are stored in a structured format for analysis against the 11-point audit checks.
Unique: Crawling patterns are optimized for AI search engine indexing (e.g., extracting citation metadata, analyzing content structure for RAG pipelines) rather than traditional SEO crawling (e.g., link analysis, keyword density), requiring different parsing logic and metadata extraction
vs alternatives: More specialized than generic web crawlers (Screaming Frog, Semrush) which optimize for Google SEO; focuses on signals that matter for AI search engine discovery and ranking rather than traditional SEO metrics
Automatically categorizes and codes documents based on learned patterns from human-reviewed samples, using machine learning to predict relevance, privilege, and responsiveness. Reduces manual review burden by identifying documents that match specified criteria without human intervention.
Ingests and processes massive volumes of documents in native formats while preserving metadata integrity and creating searchable indices. Handles format conversion, deduplication, and metadata extraction without data loss.
Provides tools for organizing and retrieving documents during depositions and trial, including document linking, timeline creation, and quick-search capabilities. Enables attorneys to rapidly locate supporting documents during proceedings.
Manages documents subject to regulatory requirements and compliance obligations, including retention policies, audit trails, and regulatory reporting. Tracks document lifecycle and ensures compliance with legal holds and preservation requirements.
Manages multi-reviewer document review workflows with task assignment, progress tracking, and quality control mechanisms. Supports parallel review by multiple team members with conflict resolution and consistency checking.
Enables rapid searching across massive document collections using full-text indexing, Boolean operators, and field-specific queries. Supports complex search syntax for precise document retrieval and filtering.
Relativity scores higher at 35/100 vs GEOScore at 31/100. However, GEOScore offers a free tier which may be better for getting started.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Identifies and flags privileged communications (attorney-client, work product) and confidential information through pattern recognition and metadata analysis. Maintains comprehensive audit trails of all access to sensitive materials.
Implements role-based access controls with fine-grained permissions at document, workspace, and field levels. Allows administrators to restrict access based on user roles, case assignments, and security clearances.
+5 more capabilities