Definition
Deep Search is an intensive mode inside an AI search engine that runs a very large number of searches for a single question, reasons across the results, and returns a long, fully cited report. The best-known example is Deep Search in Google AI Mode, which Google announced at I/O in May 2025 as taking AI Mode's query fan-out technique 'to the next level': it 'can issue hundreds of searches, reason across disparate pieces of information, and create an expert-level fully-cited report in just minutes' (Google).
The term overlaps with deep research, and the two are often used interchangeably. The useful distinction is where the feature lives. Deep research usually describes a report-writing agent inside an assistant such as ChatGPT, Claude or the Gemini app. Deep Search describes the same idea built into a search engine and its own index. Google offers it inside AI Mode, and Google's subscription pages list it among AI Mode features for Google AI Pro and Ultra subscribers in select countries. xAI's Grok has a similarly named DeepSearch mode, introduced with Grok 3 in February 2025, that searches the web and public posts on X; it has its own fetcher, Grok DeepSearch.
A normal AI Mode answer already splits a question into subtopics and searches them in parallel. Deep Search plans first, then runs searches in rounds: each round reveals new entities, claims or gaps, which become the next batch of queries. A source is included only if it keeps proving useful across many of these rounds, so the final citation list reflects breadth of coverage as much as rank on any one query.
This changes what wins. A page that ranks first for the head term but says little beyond it may be cited once and then dropped. A brand with brand depth, meaning consistent, detailed coverage of pricing, integrations, use cases and limitations across its own site and third-party sources, stays relevant as the searches move to adjacent angles. Entity coherence matters for the same reason: if your product is described differently on your site, in reviews and in directories, later rounds may treat those as conflicting facts. Original data and detailed specifications do well because they answer the long-tail sub-queries that only an intensive mode generates.
In practice, optimizing for Deep Search means mapping the sub-questions a buyer's research would produce and making sure each has a clear, self-contained answer somewhere you control or influence. Retrieval coverage is the metric to watch: of the sub-queries a research task generates, how many return one of your pages? Our ChatGPT query fan-out data shows how many sub-queries a single prompt expands into in ChatGPT, which is a reasonable proxy for the breadth an intensive mode explores.
Examples of Deep Search
- A buyer runs Deep Search in Google AI Mode on 'best field service CRM for HVAC companies with under 20 technicians' and receives a report citing vendor pricing pages, integration docs, G2 reviews and two trade-association guides.
- A CRM vendor that ranks first for 'field service CRM' is cited once in that report, while a smaller competitor with separate pages for dispatch, invoicing and QuickBooks sync is cited six times.
- A consumer brand checks Grok DeepSearch on its product category and finds the report quotes customer complaints from X threads that never surface in Google results.
- A GEO team lists 40 sub-questions a Deep Search session would likely generate for its category and finds it has no page answering 14 of them, which becomes the next quarter's content plan.
