Exa
Exa: neural search that turns the live web into structured, AI-ready data
Exa is a search engine built for AI, using embeddings-based neural retrieval to find pages by meaning rather than keyword matching. Its API returns clean, parsed content, semantically ranked results, similarity lookups, and LLM-generated answers with citations — giving applications and agents a reliable way to read the current web.
We integrate Exa as the retrieval layer wherever our systems need fresh, relevant web content rather than stale training data. Through its search, contents, find-similar, and answer endpoints, we pull semantically matched pages, extract clean text and highlights, and feed them into RAG pipelines, enrichment jobs, and content-generation workflows with proper source attribution.
Our data engineers wire Exa into scheduled and event-driven pipelines — deduping and normalizing results into our warehouse, scoring relevance, and caching parsed content to control cost. We pair it with our LLM stack so retrieval, extraction, and synthesis run as one governed flow, with citations preserved end to end for accuracy and auditability.
We use Exa's neural search and find-similar to surface competitor pages, launches, and coverage by concept, then pipe parsed results into dashboards so clients see movement in their category as it happens.
For programmatic content programs we ground each draft in Exa-retrieved sources with highlights and citations, so AI-assisted articles are built on current, verifiable web material rather than model memory.
We enrich CRM records by running company and person queries through Exa, pulling structured signals from live pages to prioritize accounts and personalize outreach for demand-gen campaigns.
Traditional APIs match keywords; Exa uses embeddings to retrieve by meaning, so a natural-language or descriptive query returns conceptually relevant pages. It also returns clean parsed content and citation-backed answers, which makes it far better suited to feeding LLMs and agents.
Yes. Beyond ranked URLs, Exa's contents endpoint returns parsed text, highlights, and summaries, so we can pass retrieval straight into RAG or generation steps without building our own scraping and cleaning layer.
We cache parsed content, tune result counts and search type per query, and route only the queries that genuinely need live web retrieval through Exa, so spend tracks real value rather than blanket crawling.
Let's grow.
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