Traditional search engines were designed for human consumption, prioritizing readability and ranking for users who scan pages. As AI agents increasingly rely on web data to ground their responses, the current search infrastructure is becoming a bottleneck. Keenable, founded by former Yandex and Amazon search engineers, is building a web-scale index specifically for AI agents. By optimizing index structures for machine retrieval rather than human browsing, they aim to solve the high costs and latency issues associated with using general-purpose search APIs for agentic tasks.

Technical Differentiation and Cost Efficiency

Keenable’s core value proposition lies in its ability to narrow search space rapidly, which is critical for maintaining performance at scale. Founder Andrey Styskin notes that enterprise search solutions often fail or become prohibitively expensive when scaled to the entire web because they lack task-specific fine-tuning. The company is also developing a proprietary "WebQueryLanguage" designed to help AI systems synthesize information across multiple web sources, even when no single document contains the complete answer. This approach is intended to compete with the bundled, restrictive search APIs currently offered by major tech incumbents, who are increasingly limiting access to their search data to avoid cannibalizing their own AI products.

Market Positioning and Growth

With $26 million in seed funding led by Accel, Keenable is positioning itself as a specialized infrastructure provider for AI labs and inference providers. While the company acknowledges the extreme difficulty of competing with established search giants, they argue that the "innovator's dilemma" makes incumbents vulnerable in the agentic search space. By focusing on cost-efficient, high-speed retrieval, Keenable aims to capture the growing segment of developers who need reliable, programmatic access to the web for AI grounding.