Keenable is building a web search index of over 100 billion documents to power AI agents, with a focus on AI‑friendly infrastructure.

Keenable, backed by Accel, is constructing a massive web search index aimed at powering next‑generation AI agents. The startup plans to crawl and catalog more than 100 billion web documents, creating an infrastructure that is optimized for machine consumption rather than traditional human search.

Why a New Index Is Needed

Current search engines are built primarily for human users, delivering results as webpages and snippets. AI agents, however, require structured, machine‑readable data that can be queried programmatically. Keenable’s index is designed to expose content in formats that facilitate rapid retrieval and integration into autonomous workflows.

The company emphasizes "AI‑friendly" infrastructure, meaning low‑latency access, rich metadata, and APIs that support batch processing and real‑time inference. This approach aims to reduce the overhead that developers currently face when adapting conventional search results for AI applications.

Funding and Backing

Accel led a Series A round that valued Keenable at over $200 million. The investment reflects growing interest from venture capital in the emerging market of AI‑centric data services, where the ability to quickly surface relevant information is a competitive advantage.

Technical Highlights

  • Crawling pipeline optimized for parallel processing across distributed nodes
  • Metadata enrichment with entity extraction, topic tagging, and language detection
  • Vector‑based storage enabling similarity search for semantic queries
  • Public and private API tiers for developers and enterprise customers

Keenable also plans to integrate reinforcement learning techniques to continuously improve relevance based on agent feedback, a feature that could set the index apart from static, keyword‑driven alternatives.

Potential Impact on AI Development

By providing a ready‑made, searchable corpus, Keenable could accelerate the development of autonomous agents in areas such as research assistants, automated customer support, and real‑time data analysis. Developers would spend less time building custom crawlers and more time refining agent behavior.

The company acknowledges the challenges of scaling to 100 billion documents, including storage costs and ensuring data freshness. Nevertheless, it argues that the long‑term efficiencies gained by AI agents will outweigh the operational overhead.

TechCrunch coverage of Keenable’s AI‑focused indexing effort