Best AI for Deep Research — category guide

What the category covers

Deep research AI sits between a chat assistant and a human analyst. A chat assistant answers from what it already knows, or from a few searches. A deep research tool runs a longer process: it plans, searches many sources, reads them, and writes a report with citations. The output takes longer to produce and is longer to read, but it is closer to the work a junior analyst would do. The useful question is not whether a tool writes well, but whether its evidence holds up.

Professionals use the category for four kinds of job. The first is sizing and forecasting, where a number must come from named sources. The second is company and deal work, which relies on filings, transcripts and contracts. The third is regulatory and policy work, which depends on official texts and their effective dates. The fourth is evidence review, which covers papers, trials and reports. Each job rewards a different set of sources, so the best tool depends on the job. Teams often combine tools. A common setup uses one tool for public data, another for paywalled content, and a third for the team's own files, with a person checking the results that reach a decision.

Sub-jobs and what each needs

• Market sizing. Needs government series, industry reports and a clear market definition. Put the unit and the year range in the brief.
• Due diligence. Needs filings, transcripts, contracts and the target's own documents. A data room tool helps when the file count is large.
• Policy tracking. Needs the official text, the effective date and any pending changes, with each claim tied to the rule that says it.
• Science and health review. Needs papers, trial records and regulatory data. A qualified reviewer should judge what the results mean for patients.
• Newsroom checks. Needs the original statistic or document, plus a record of who published it and when.

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