What Is Exa AI? The $700M Search Engine Built for AI

Exa AI is the first search engine designed specifically for AI agents and large language models. Unlike Google, which optimizes for human clicks and ad revenue, Exa returns clean, semantically-relevant results that AI systems can actually use.

Founded by two Harvard roommates who thought Google was outdated, Exa has raised $107M at a $700M valuation. Their bet: AI will soon search the web far more than humans do.

I use Exa daily through Claude Code's MCP integration. It's the search layer powering many of the AI tools you're already using—Cursor, Notion AI, and thousands of developer applications.

What Exa AI Does

Exa is a search API that understands meaning, not just keywords.

Traditional search engines match your query against indexed pages using keyword frequency and backlinks. Exa uses neural embeddings—the same transformer technology behind ChatGPT—to understand what you're actually looking for.

The core difference: When you search "startups disrupting healthcare with voice AI," Google returns pages containing those keywords. Exa returns pages that conceptually match your intent, even if they use different words entirely.

Three Main Products

Key insight: Exa isn't trying to replace Google for everyday searches. They're building the search infrastructure layer that AI applications need to access real-time web information.

The Founder Story

In 2021, Will Bryk told his father he wanted to build a search engine.

"What's wrong with you? Have you heard of Google?" — Will Bryk's father

He did it anyway.

Two Harvard Roommates vs Google

Will Bryk and Jeff Wang met freshman year at Harvard. They were roommates who shared an obsession: what if they could build better search than Google?

Will studied CS and physics, did ML research, and led the robotics club. Jeff studied CS and Philosophy, ran a GPU cluster from his dorm room, then spent three years at Plaid building data infrastructure.

When GPT-3 dropped in 2020, Will had an epiphany. Google's algorithm couldn't handle complex queries. But transformers and embeddings could understand meaning, not just keywords.

They bought a million dollars worth of GPUs. Got into Y Combinator's Summer 2021 batch. Started training transformer models under the name "Metaphor."

The ChatGPT Pivot That Saved Them

They launched their consumer search engine in November 2022—exactly two weeks before ChatGPT dropped.

The timing could have killed them. A consumer search product launching right when everyone shifted attention to chatbots? Disaster.

Instead, it validated their thesis: AI was about to search the web more than humans. They pivoted from consumer to API, targeting developers building AI applications.

Within months, 4,000+ developers signed up.

The Numbers

The name "Exa" represents 10^18—significantly smaller than Google (10^100). As Will puts it: "Less means better. We're making a much more curated experience."

How Exa Works

Exa's technology differs fundamentally from traditional search.

Traditional Search (Google)

1. Crawler visits webpages
2. Creates inverted index mapping keywords to pages
3. Ranks by keyword relevance + backlinks + engagement
4. Returns blue links optimized for human clicks

Exa's Neural Search

1. Crawler visits webpages
2. Converts each page into a high-dimensional embedding vector representing semantic meaning
3. When queried, finds content with similar meaning vectors
4. Returns clean, structured content optimized for AI consumption

The key difference: Google matches words. Exa matches meaning.

Search Types

Exa offers three search modes:

• Neural search: Uses embeddings to find semantically similar content. Best for complex, natural language queries.
• Keyword search: Traditional exact-match search. Best for specific terms, names, or code.
• Auto search: Exa chooses the best approach based on your query.

Content Extraction

Beyond search, Exa can extract and return:

• Full text: Cleaned HTML content from any webpage
• Highlights: Specific passages matching your query
• Summaries: AI-generated summaries of page content
• Metadata: Publication dates, authors, domains

This content extraction is what makes Exa valuable for RAG (Retrieval-Augmented Generation) applications. Instead of just returning links, it returns the actual information AI systems need.

Exa vs Google: Key Differences

Why this matters: Google optimizes for engagement and ad clicks. Exa optimizes for returning the highest-quality results because they're paid per query, not per ad impression.

"Google's algorithm is not powerful enough to handle queries of any substantial complexity." — Will Bryk

Who Uses Exa

Enterprise Customers

• Databricks — Finding large training datasets
• AWS — Integrated into AI services
• Cursor — The AI coding assistant uses Exa for web search
• Notion AI — Powers search capabilities
• Vercel — Integrated into their AI SDK

Common Use Cases

1. AI Agents — Real-time web browsing for autonomous agents
2. RAG Applications — Grounding LLM responses in current web data
3. Research Tools — Automated research with source citations
4. Lead Generation — Finding companies matching specific criteria
5. Content Verification — Fact-checking LLM outputs against real sources

This kind of AI-first infrastructure is exactly what's driving the new wave of AI revenue strategies in SaaS—companies building entirely new business models around AI capabilities rather than just adding AI features to existing products.

Developer Integrations

Exa integrates with the tools AI developers already use:

• Vercel AI SDK
• LangChain
• LlamaIndex
• CrewAI
• Anthropic tool calling (Claude)
• OpenAI function calling
• Model Context Protocol (MCP)

Personal note: I use Exa through Claude Code's MCP integration. When I need to research a topic or find specific companies, the results are noticeably cleaner than traditional search. No SEO spam, no ad-stuffed pages—just the actual content I'm looking for.

Pricing

Exa uses pay-as-you-go pricing with no minimum commitment.

Free tier: $10 in free credits, no credit card required.

For comparison: running 1,000 searches with content extraction costs roughly $6. That's viable for most AI applications without enterprise-level usage.

How to Get Started

Step 1: Get Your API Key

1. Go to exa.ai
2. Click "Try the API for free"
3. Create an account
4. Copy your API key from the dashboard

Step 2: Install the SDK

Python:

pip install exa-py

JavaScript/TypeScript:

npm install exa-js

Step 3: Make Your First Search

Python example:

from exa_py import Exa

exa = Exa(api_key="your-api-key")

# Semantic search
results = exa.search(
    "AI startups disrupting restaurant ordering",
    num_results=10,
    type="neural"
)

# Get full content from results
contents = exa.get_contents(
    [result.url for result in results.results],
    text=True
)

Key parameters:

• type: "neural" (semantic), "keyword" (exact match), or "auto"
• num_results: 1 to 1000+ results
• include_domains: Filter to specific websites
• start_published_date: Filter by publication date

Step 4: Try Advanced Features

Find similar pages:

similar = exa.find_similar(
    "https://example.com/article-you-like",
    num_results=10
)

Get direct answers:

answer = exa.answer(
    "What is the market size for voice AI in restaurants?"
)

The Marketing Hack That Went Viral

This is worth mentioning because it shows how Exa thinks differently.

Felicia Tang (Chief of Staff) and Will Bryk got tired of LinkedIn outreach for recruiting. So they went physical.

What they did:

• Designed math puzzles on Canva
• Had an engineer create each problem in 10 minutes
• Scootered 10 miles around San Francisco posting puzzles on bus stops and billboards
• Will crashed into Felicia's scooter and hurt his wrist
• They kept going anyway

The results:

• Dozens of qualified inbound candidates (engineers from Google, Apple, Retool)
• Organic reposts on blogs including a Caltech board
• One guy used it to ask Felicia out (she said no, but respected the hustle)
• Covered by Business Insider

They called it "nerd-sniping"—filtering for curiosity, tenacity, and fun. The kind of people who would stop to solve a math puzzle on their commute.

Frequently Asked Questions

Key Takeaways

• Exa is a search engine built for AI, not humans. It uses neural embeddings to understand meaning rather than just matching keywords.
• $107M raised at a $700M valuation. Founded by two Harvard roommates who thought Google was outdated—and proved it by growing from $1.1M to $12M ARR in 15 months.
• Already powering tools you use. Cursor, Notion AI, and thousands of AI applications rely on Exa for web search capabilities.
• Different business model than Google. Exa charges per query, so they're incentivized to return quality results—not show ads.
• Easy to get started. $10 free credits, Python/JS SDKs, and integration with major AI frameworks like LangChain and Claude's MCP.

The bet Exa is making: AI will soon search the web more than humans. If that's true, optimizing search for AI systems—not human clicks—is the right move. Based on their revenue growth and the companies already using them, that bet appears to be paying off.