LLM Pulse developer hub

Grow your brand across AI search

Use LLM Pulse visibility data in dashboards, AI assistants, reporting workflows, and practical AI search programs.

Explore the API Read the guide View examples

REST API MCP OpenAPI Mentions and citations

Need the product? Start with the live LLM Pulse app, then use these docs to connect the data. Open llmpulse.ai

Built for AI search workflows

Short, public-facing documentation for teams evaluating LLM Pulse or building on top of its AI visibility data.

Query mentions, citations, responses, recommendations, sentiment, source intelligence, Search Console data, webhooks, and reporting endpoints.

Connect AI clients through OAuth or a Scale+ API key so teams can ask questions about live visibility data.

Understand answer engines, citations, query fan-out, brand mentions, and the practical habits that improve discoverability.

LLM Pulse exposes the same visibility data in different ways. Pick the surface that matches the job.

REST is the best path for BI dashboards, internal tools, data warehouses, custom reporting, and automations that need predictable HTTP endpoints.

MCP is the best path when a human wants to ask questions about live LLM Pulse data inside an AI assistant.

The AI search guide explains what to measure, why citations matter, and how teams can turn visibility findings into better content.

LLM Pulse turns AI answer activity into measurable data that teams can monitor and improve.

Mentions How often an AI answer names the brand.

Citations Which URLs AI answers use as sources.

Share How visibility compares against competitors.

Sentiment Whether answers frame the brand positively.