Autumn vs. Exa for People and Company Search

Autumn and Exa increasingly overlap: both give developers APIs for finding people and companies with natural-language queries, retrieving current web information, and returning structured results for agents.

They begin from different abstractions. Exa is a search engine for AI applications. Its core primitives are web search, page contents, answers, and research. Autumn is centered on people and company entities, the relationships between them, current signals, research tables, and the evidence attached to individual values.

Neither abstraction is universally better. The right choice depends on whether your product primarily needs webpages and broad web retrieval or resolved people and company intelligence.

Choose Exa when the web page is the primitive. Choose Autumn when the person or company is the primitive.

Comparison at a Glance

This comparison uses publicly documented capabilities as of August 13, 2026. It is not a latency, recall, or accuracy benchmark. Test both products on your own workload before making a production decision.

Disclosure: Autumn publishes this comparison and is one of the products evaluated.

What Exa Is Built For

Exa describes itself as a search engine made for AIs. Its API can find webpages, retrieve clean content, produce grounded answers, and run deeper research with structured output and citations.

The Search API supports general web search and categories including people, companies, research papers, news, personal sites, and financial reports. Search can return page content, highlights, summaries, or schema-constrained output.

The Contents API handles known URLs. It can extract clean markdown, summaries, and highlights, crawl subpages, and control cache freshness with maxAgeHours. Exa publicly documents different latency-quality modes for search, ranging from instant retrieval to deeper reasoning.

That makes Exa a strong primitive when your agent needs to search across arbitrary domains and consume the underlying pages.

Exa People Search

Exa now has a dedicated People Search category. Its documentation says it searches more than one billion professional profiles, refreshes that index weekly, and supports natural-language constraints such as role, skill, company, location, and seniority.

The people category returns relevance-ranked profile results through the same search endpoint. Developers can request highlights or use deeper search modes with an output schema to extract fields.

The documentation also lists constraints: people search does not support publication-date filters or general include/exclude-domain behavior, and structured attributes are primarily encoded through the natural-language query. Exa recommends Websets for list building and enrichment at scale.

What Autumn Is Built For

Autumn is designed around research on people and companies. Its public product examples include work history, public projects, social and professional relationships, company formation, hiring, funding, domain activity, founder movement, and warm paths.

The output is organized as profiles and tables rather than only search results. A person or company can carry current fields, historical context, signals, relationships, and sources collected across multiple public pages.

Autumn’s citation architecture is field-oriented. Retrieval creates a source registry; values retain the source IDs behind them; and validation checks whether cited text supports the value for the correct entity. That matters in workflows where one row combines facts from a professional profile, a company site, a filing, a repository, and a news page.

Autumn also emphasizes early company intelligence. Its Brex customer story describes finding accelerator cohorts, incorporation events, and funding signals before they appear in conventional startup databases.

The product’s current index covers more than one billion people and roughly 110 million companies. Basic entity enrichment can complete in roughly 400 milliseconds. Complex public-web research is asynchronous and typically takes 4–10 minutes, reflecting a different tradeoff from low-latency web retrieval.

That research can use strategies a human investigator might invent but struggle to run at scale: matching usernames or URL slugs, connecting founders to non-public companies through filings and domain traces, or mapping technologies and vendors through security trust centers and subprocessor lists.

The Central Difference: Retrieval vs. Entity Work

Consider this request:

1Find technical founders who recently left infrastructure companies,2appear to be building a new startup, and have a warm path to our team.3Return the evidence behind every match.

With a general web-search API, your application can run multiple searches, retrieve relevant pages, extract a schema, resolve duplicate identities, join people to companies, derive relationship paths, validate sources, and rank the results.

With an entity-centric research system, more of that work is part of the product abstraction. The expected output is already a collection of resolved people, companies, signals, and evidence.

That distinction affects engineering ownership. Exa gives developers flexible retrieval primitives. Autumn aims to own more of the domain-specific research loop.

Where Exa Has the Advantage

General Web Breadth

Exa is not limited to people and companies. The same API can search research papers, news, financial reports, technical documentation, personal sites, and arbitrary web content.

Retrieval Controls

Exa exposes clear options for search depth, page contents, highlights, summaries, subpage crawling, and cache freshness. Those controls are useful when developers want to compose their own retrieval and reasoning system.

Low-Latency Retrieval and Public Performance Documentation

Exa documents latency bands for several search modes and is the more natural fit when low-latency webpage retrieval is the central requirement. Autumn publishes approximate operating modes—fast enrichment and longer asynchronous research—but does not currently publish equivalent throughput guarantees.

RAG and Agent Tooling

When the core job is “give this agent high-quality web search and clean page text,” Exa maps directly to the requirement.

Where Autumn Has the Advantage

People and Company Context

Autumn is opinionated about the entity model: identities, work history, company facts, relationships, public signals, and research tables are first-class outputs.

Multi-Source Field Evidence

Field-level lineage is useful when a result contains many claims from different sources. It supports inspection, correction, and downstream policies at a finer level than attaching links to an entire answer.

Emerging Company Signals

Autumn’s product is designed to connect time-sensitive public events—incorporations, new domains, hiring pages, profile changes, launches—to the people and companies they describe.

Ready-Made Research Workflows

Teams that want people and company research as a service may prefer a higher-level task and table abstraction over assembling search, crawling, extraction, resolution, and validation themselves.

Autumn stops at intelligence and research. It does not provide outbound sending or sequencing; sales teams connect accepted results to third-party engagement tools.

When to Use Both

The products can be complementary.

Use Exa for broad discovery and arbitrary page retrieval, then use Autumn to resolve people and companies, build profiles, connect signals, and maintain field-level evidence. Or use Autumn for the core entity workflow and Exa when a task needs content outside the people-and-company domain.

A combined architecture might look like:

1agent goal2  -> Autumn people/company search3  -> Exa broad web retrieval for uncovered questions4  -> entity and evidence store5  -> application decision

Do not add two providers merely for the appearance of coverage. Define the failure each one addresses and measure whether the second provider improves accepted results.

Questions to Test

Run the same labeled tasks through both systems:

• Find a known person with an ambiguous name
• Find a cohort defined by role, experience, and location
• Find people based on a very recent company signal
• Return structured fields from multiple public sources
• Identify contradictions between an old profile and a current company page
• Explain why each result matches
• Provide auditable evidence for every required field
• Return a useful partial result when a field cannot be verified

Measure entity precision, candidate recall, freshness, source coverage, cost, time to usable output, and integration effort. A general retrieval benchmark alone will not tell you which product better supports an entity workflow.

Bottom Line

Exa is the stronger default when you need a general search engine and content-retrieval layer for AI. Autumn is the more specialized choice when your product needs people and company entities, relationships, current signals, structured research, and field-level evidence.

The overlap is growing: Exa now offers dedicated people and company categories, while Autumn exposes its people-and-company research agents through APIs. The durable decision is not which marketing category sounds larger. It is which abstraction removes the most difficult work from your application.

For a broader vendor view, read Best People Search APIs for Builders in 2026 and Best Company Search APIs for Builders in 2026.

Developers can compare the abstractions directly through the Autumn API documentation and Exa’s official documentation linked above.