Proprietary Indexing for Agent Research

Owning the crawl and index layer lets Parallel compete on answer quality, not just on API wrappers. For an AI agent, the hard part is not getting 10 blue links, it is finding fresh pages, pulling the full text, cleaning it, ranking it for the exact task, and handing structured context to the model. That is why Parallel can go deeper on multi step research than products that mostly relay third party search results.

• In practice, third party search often leaves extra work for the customer. Cohere described moving from Brave to Tavily because its prior API often returned URLs or snippets, which then required another fetch and cleanup step before the model could use the page text. Parallel is aiming to own more of that pipeline directly.
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• The closest comp is Exa, which also built its own index and has grown to an estimated $10M annualized revenue by September 2025, with funding of about $107M. Tavily took the lighter model, aggregating multiple sources in real time instead of maintaining a full index, which lowers infrastructure cost but gives it less control over raw coverage and ranking.
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• This matters most for deep research agents because they need recall and consistency over long chains of steps. The Cohere interview describes Parallel through Manus as taking 10 to 15 minutes to build dense reports with tables and segmented breakdowns, which suggests the product is spending real compute and retrieval effort to gather better source material before the model writes.
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The market is heading toward a split between cheap search routing and premium proprietary indexes built for agents. As more AI apps need reliable multi hop research, companies that control crawling, extraction, and ranking will have a better shot at becoming core infrastructure, especially if they extend from the open web into domain specific corpora like filings, journals, and legal sources.