elmohq/elmo

What it solves

Elmo provides a way to track and optimize how brands are mentioned and cited by AI answer engines (such as ChatGPT, Claude, Perplexity, and Gemini). It solves the problem of "AI visibility"—often called Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO)—by allowing users to benchmark their brand's presence against competitors in AI-generated responses.

How it works

Elmo uses a background worker to run a set of defined prompts (questions potential customers might ask) against various AI engines on a schedule. It captures the responses using scraping providers or direct model APIs, then parses the text for brand names, aliases, and domains. The system stores the raw output and cited URLs in a PostgreSQL database, allowing it to calculate visibility scores, share of voice, and citation trends over time.

Who it’s for

• Self-hosting teams who want full control over their AI visibility data and infrastructure.
• Agencies providing AEO services to clients via white-label deployments.
• Developers and analysts who require auditable metrics and programmatic access via a REST API.
• Businesses looking for a free, open-source alternative to enterprise AI visibility platforms.

Highlights

• Visibility & Mention Tracking: Measures how often a brand is mentioned per prompt and per model.
• Citation Analysis: Tracks which URLs and domains AI models trust and cite for specific industries.
• Query Fan-out Analysis: Records the web searches AI engines perform to ground their answers, showing how queries are rewritten.
• Competitor Benchmarking: Provides a leaderboard and "share of voice" metrics to compare brand presence against competitors.
• Opportunity Generation: Turns visibility data into prioritized recommendations for content creation and seeding to earn more citations.
• Open & Auditable: All scoring and aggregation code is public, ensuring metrics are not a "black box."