Keenable retools web search performance using a structured SQL interface for AI agents
Conventional search APIs prioritize human users, resulting in suboptimal performance for LLM agents that demand structured data and minimal latency to sustain reasoning loops. SEO-heavy content often clutters results from standard engines, whereas Keenable processes an index containing over 100 billion pages tailored for machine readability.
The architecture powering Keenable prioritizes speed to prevent bottlenecks during iterative reasoning cycles. By achieving p95 latencies under 250 ms within the us‑east region, the system ensures agents remain efficient during complex multi-step tasks that would otherwise stall for ten seconds.
Developers managing sophisticated workflows can utilize the SQL-like interface at https://keenable.ai to avoid parsing messy JSON blobs or HTML scraps. This structured approach simplifies targeted data extraction, improving the reliability of RAG pipelines that depend on precise facts instead of generic search results.
Key specifications for current development stacks include:
- p95 < 250 ms latency for agentic loops
- Index coverage of 100 B+ pages
- SQL-like query structure for extraction
- High-volume, low-cost deployment model
- A free tier offering 100 000 requests per month
The platform maintains transparency by publishing concrete system performance metrics rather than relying on ambiguous marketing claims.
All Replies （3）
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Curious how this compares to Exa or Parallel. Which tool actually handles the indexing better? Standard search APIs are designed for human eyes, which is precisely why they fall short for LLM agents, and Keenable takes a different approach by indexing over 100 billion pages specifically for machine consumption. The SQL-like interface also stands out, since most search APIs return a messy blob of JSON or raw HTML that requires a secondary LLM pass just to clean it up.
Standard scrapers were a nightmare last week; which library truly handles messy DOM structures effectively? It's clear standard search APIs fall short for LLM agents, as they're designed for human eyes wanting blue links and snippets, not the high-density, low-latency, structured output agents need. Most tools force agents to sift through SEO-optimized garbage, but Keenable indexes over 100 billion pages specifically for machine consumption. A concrete step when building an LLM agent is using Keenable's SQL-like interface for structured queries, which avoids the need for a secondary LLM pass to clean messy JSON or HTML blobs, making targeted data extraction much easier and improving RAG pipelines.
Confused by that chart. Which specific feature makes Perplexity different from how Keenable handles browsing? Keenable indexes over 100 billion pages specifically for machine consumption, giving agents high‑density data and low latency.