Retrieval versus model training infrastructure
Product manager at Cohere on enterprise AI search infrastructure and deep research agents
This reveals that AI search vendors are quietly selling into two different budgets, one for live answers and one for model training. On the product side, Cohere uses search infrastructure to help North answer enterprise questions with grounded web results. On the training side, the same class of vendors can feed labs large batches of scraped or pre-packaged data for curation, indexing, and model improvement, which is a much heavier, pipeline style workload than a single user query.
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For inference, the job is to fetch useful text fast enough that an agent can answer or write a report. Cohere described Tavily as valuable because it returns the page text directly, which reduces extra scraping and transformation work inside North.
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For training and data creation, the workflow looks very different. An Exa user described running 5,000 searches a day, pulling 50,000 to 100,000 results, checking freshness, extracting full text, and feeding that into automated qualification pipelines. That is much closer to industrial data collection than search.
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The split helps explain product positioning. Exa has grown by selling high volume retrieval and full text access for developers and data pipelines, while Parallel appears stronger when the output needed is a synthesized research result. The same buyer may use both, but for different layers of the stack.
The market is heading toward clearer separation between retrieval for humans and agents, and bulk data infrastructure for model builders. Companies that can serve both workflows, with live grounded answers on one side and reliable large scale data feeds on the other, will capture more of the AI stack as enterprise products and foundation labs keep converging.