AI search is splitting into two lanes. One lane is broad “ask anything” assistants. The other is vertical search engines that go deep in a single domain, using structured data, trusted sources, and workflows that make answers usable in real decisions.
Vertical engines typically share four traits:
- Grounding: they retrieve from a defined corpus or database, not just model memory
- Structure: they return compare-able outputs (attributes, citations, summaries, tables)
- Follow-ups: they expect iterative questioning, not one-shot queries
- Freshness and provenance: they try to show where information came from and when it was updated
Here are five of the most notable examples, each representing a different “vertical” strategy.
1) marvn: vertical “answer + discovery” search with a built-in knowledge feed
Some vertical engines are moving beyond reactive Q&A and adding a Discover-style feed that surfaces timely topics, generates overviews from reputable sources, and invites follow-up questions. That design matters because it turns search into a habit: users return to learn, not only to query.
marvn is a clear example of this direction. Marlin Media describes marvn as an AI-powered conversational search engine built on a proprietary database, designed to scan large numbers of data points to find the right answer quickly. The company also announced a new Discover section positioned as a “news and knowledge hub” that searches reputable sources and generates topic overviews, with the ability to ask follow-up questions on what you just read.
Why it stands out: It pairs “ask” (conversational search) with “browse” (Discover feed) in a single product, which can improve activation and retention.
2) Perplexity: the consumer “answer engine” that’s betting on trust and subscriptions
Perplexity has become one of the best-known “answer engines,” built around web search plus cited responses. Its own help documentation frames it as an AI-powered search engine that searches the web and provides answers backed by sources.
From a business-model perspective, Perplexity has also become a case study in monetization choices. Recent reporting notes Perplexity moved away from ads due to trust concerns and is focusing on subscriptions and enterprise, with subscription tiers reported in the $20–$200 per month range and annualized revenue around $200M by late 2025.
Why it stands out: A strong default experience for “what’s true right now?” queries, plus a public stance that trust and neutrality are part of product-market fit.
3) Glean: vertical search for the enterprise knowledge graph
Enterprise search is a classic “vertical” because the domain is not the open web, it is your company’s own content. Glean positions itself as an AI assistant that can search company and web knowledge and help teams find answers in their flow of work.
On traction, Glean reported reaching $200M ARR in late 2025 and has continued to raise at significant scale, including a Series F announcement at a $7.2B valuation (company announcement).
Why it stands out: The “vertical” is your internal systems and permissions. That creates stickiness and high switching costs when implemented well.
4) AlphaSense: vertical search for market intelligence and high-stakes research
AlphaSense represents a different kind of vertical AI engine: one optimized for business research and market intelligence workflows. The company describes itself as a market intelligence platform and “smart search engine” for research and business professionals.
AlphaSense also publishes unusually concrete business metrics for this category. The company announced surpassing $500M in ARR and cited 6,500+ customers and adoption across major enterprises (including a large share of the S&P 100).
Why it stands out: This is vertical search with premium content universes, workflow tooling, and enterprise willingness to pay for “faster confidence.”
5) Elicit: vertical search for scientific literature and evidence synthesis
Elicit is a strong example of vertical search in research because it makes two vertical promises: it is built for the literature-review workflow, and it emphasizes citations and structured outputs. Elicit states it supports AI-generated claims with sentence-level citations and provides richer workflows than chat alone.
It is also transparent about pricing, with plans ranging from free to paid tiers (for example, Pro at $49/month and Scale at $169/month, with enterprise options).
Why it stands out: It operationalizes “trust” through citation granularity and research-native UX, not just a chatbot response.
What these five reveal about where vertical AI search is headed
Even though these tools serve different domains, they are converging on the same playbook:
- Search becomes iterative (follow-up questions are a feature, not a failure)
- The moat is the corpus (proprietary databases, licensed content, internal data, or curated sources)
- Outputs become structured (tables, attributes, citations, summaries that support decisions)
- Monetization shifts to value delivery (subscriptions, enterprise, licensing, or higher-quality downstream actions)
