OpenCrawl

Teams have valuable data scattered across email, chat, documents, meetings, storage, and dozens of apps like Gmail, Slack, Notion, GitHub, Salesforce, and Zoom. This data is hard to reach and hard to search, making it difficult for people and AI agents to get fast, accurate answers from it. Today this likely means manually searching across many disconnected tools or building custom integrations for each source. The text does not specify exact costs, but the implication is that valuable, personalized data remains effectively unsearchable without a dedicated system.

OpenCrawl connects to a wide range of sources, including email and chat tools, documents, meetings platforms, file storage, and business apps, either through existing connectors or by letting users provide URLs, describe needed web data, connect internal sources, or upload files. Its agent helps with setup, and an ingest layer controls crawling, transformation, indexing, and tuning of the data. The result is a high-performance search index built over hyper-personalized, multimodal datasets, designed to return fast and accurate results for AI agents and for direct queries. As a demonstration, OpenCrawl built a search index over more than 2 million podcast episodes to show the system in action.