👾 Inside Exa: The Startup Building a Search Engine for the AI Age

As artificial intelligence reshapes technology, even the way we search the web is evolving. Traditional search engines like Google are built for humans, but what happens when the search user is an AI? In a recent conversation, we sat down with Thais Castello Branco, Head of Marketing at Exa, to discuss how this San Francisco startup is reinventing search for the age of AI. The discussion shed light on Exa’s vision for AI-driven search, how it stands apart from giants like Google and OpenAI, and what the future holds for searching in an AI-dominated world.

The Evolution of AI Search and Exa’s Role

Traditional search engines rely heavily on keyword matching—finding exact phrases or words on web pages and aligning them with user queries. Yet as large language models and AI agents increasingly become primary users of the web, a new kind of search technology is needed—one capable of interpreting not just the words, but the intent behind a query. 

Exa was founded precisely to meet this emerging need. According to Thais, Exa’s search technology is built on neural networks capable of understanding the deeper meaning behind queries. “Exa is the first web-scale search engine designed to grasp intent, not just match keywords,” she explained. By understanding context and meaning, Exa provides results tailored precisely to the information that both human users and AI applications genuinely seek.

How Exa Differentiates from ‘Competitors’

While Google and Bing prioritize keyword-based ranking, Exa is designed from the ground up to interpret intent—offering precise, AI-tailored results without SEO interference. Thais underscored the limitations of keyword-centric searches: “Even a simple search for AI startups building agents usually yields articles or social posts, rarely delivering the specific companies you want.”

Exa’s neural network-driven technology directly addresses these shortcomings. It processes complex, intent-rich queries with precision, eliminating irrelevant content and dramatically enhancing the quality and specificity of results. Thais emphasized, “We built Exa to understand not just the words, but what you're actually trying to find.”

This innovative approach excels particularly well in sophisticated, multi-criteria searches. For example, a query about AI applications in the legal industry would yield direct listings of relevant startups such as Harvey or EvenUp, rather than general informational articles or unrelated links. This ensures users receive highly actionable and immediately useful information, positioning Exa uniquely in a crowded market dominated by established tech giants.

Additionally, Thais pointed out a critical differentiator: Exa's unique position within the market. “Most players, like Perplexity or ChatGPT, wrap traditional search engines like Google or Bing to serve their results,” she explained. “Exa is really one of the only players—if not the only one—actually training models specifically for AI-driven search. We're redesigning the logic behind how web search should function from the ground up, making Exa fundamentally different from consumer-oriented search platforms.” By focusing intensely on this foundational technology, Exa establishes itself uniquely within the competitive landscape.

Developing Exa’s API and Real-World Use Cases

Exa offers its technology through an API (Application Programming Interface). This means other applications and AI systems can send queries to Exa and get back rich results in real time. Thais described this as a deliberate strategy: “Our API lets developers tap into live web information with a single call. We built Exa as a tool for AI developers from day one.” By providing an API, Exa becomes the search backend for a variety of use cases without requiring users to visit a new search site.

What kinds of applications can leverage Exa’s web search API? The conversation revealed a range of possibilities:

• AI research assistants: An AI agent that writes reports or summaries can use Exa to gather facts from the web, reading dozens of sources and compiling information automatically.

• Business intelligence tools: Companies can integrate Exa to scan news, forums, and public data for mentions of competitors or market trends, all through complex queries that normal search engines can’t handle easily.

• Creative AI applications: From chatbots that answer domain-specific questions (legal, medical, etc.) to educational apps that fetch up-to-date examples or explanations, any AI that needs internet-sourced knowledge can benefit from Exa’s depth of search.

During the interview, Thais gave one example of a query that Exa handled through the API: finding information on CEOs of the top 50 AI startups in the US by market capitalization. It’s the kind of complex, multi-part query that would stump a regular search engine, but Exa can break it down, retrieve the relevant data points from many documents, and return a structured answer. Developers using the API receive those results in a format their software can parse – for instance, as JSON data containing a list of companies and CEO names – enabling automated processing of the information.

This developer-centric approach is what differentiates Exa’s product. "Exa isn’t just a search engine, it’s like a research partner for your AI application," allowing software to query the world’s information much like a person would, but at machine speed and scale. By focusing on the API, Exa can integrate into many platforms and workflows behind the scenes. Thais hinted that a user might interact with an AI-powered app and not even realize Exa is working in the background to fetch the answers – and that’s by design.

Marketing Strategies in the AI Industry

Despite having cutting-edge technology, Exa faces a classic startup challenge: how to gain traction in a market dominated by tech giants. As Head of Marketing, Thais shared her strategies for getting the word out. Rather than trying to compete head-on with Google’s brand or ad budget, Exa’s marketing has been more targeted and community-driven. “We focus on the developer community and AI enthusiasts. They’re the ones who immediately see the value,” she explained. This has meant engaging with AI forums, demoing at industry events, and publishing technical insights that showcase Exa’s unique capabilities.

One key strategy has been to highlight concrete achievements and metrics. For example, Exa has publicized cases where it outperformed Google and OpenAI’s own search features in terms of depth and accuracy of results. Sharing these comparisons on social media and in blog posts generated buzz among AI developers curious to try the new engine. “AI users will see through hype quickly, so we let the results speak for themselves,” Thais said. By releasing examples of complex searches (and how Exa solved them) or launching tools like Websets (which presents Exa’s search results in a nifty tabular format), the company attracted early adopters who then spread the word.

Marketing an AI product can require educating the audience as much as promoting the brand. In Exa’s case, part of the marketing involves explaining why a specialized search engine for AI is needed in the first place. Blog articles, webinars, and even interviews like this one serve to inform potential users about the limitations of traditional search in an AI context – and how Exa addresses them. Thais emphasized the importance of trust: “We’re asking users to rely on our search for critical tasks, so we have to build credibility at every step.” This means transparency about how the system works and strong support for the developer community.

Another tactic has been leveraging word-of-mouth and networks. Exa came out of Y Combinator and secured backing from notable investors, which gave it a stamp of credibility early on. The team has leaned into those networks to find pilot customers and advocates. They also keep an active presence on X (formerly Twitter) and LinkedIn, where AI practitioners congregate. By sharing insights, responding to user feedback, and showcasing new features, Exa’s small team can punch above its weight in terms of marketing reach. It’s a playbook that many AI startups are adopting: be agile, show value, and turn your users into your evangelists.

The Vision for AI Search in the Next Few Years

Looking ahead, Thais painted an exciting picture of what web search might become in the near future. In her view, the next generation of search won’t revolve around a user typing a query into a browser and scrolling through links. Instead, search will be woven into the fabric of AI assistants and autonomous agents. “We envision a world where you don’t search for information anymore – instead, your AI assistant just knows how to find exactly what you need, when you need it,” Thais said. This hints at a future where AI not only retrieves information but does so proactively, anticipating needs.

In practical terms, the search engine of the future could function more like a conversation. You might ask a question in plain language (or even just think it, as brain-computer interfaces advance), and an AI will interact with a search backend like Exa to gather information, cross-verify facts from multiple sources, and present a synthesized answer. All of this could happen in seconds, invisible to the user. Search results might no longer be a list of links, but a directly usable insight or action.

Thais suggested that in five years, much of the internet will be accessible through what she calls the “agentic web” – a web tailored for AI agents to navigate. In this vision, websites may start publishing information in formats easier for AI to consume, and search providers will compete on how well they serve AI clients (in addition to human users). Exa is positioning itself for that scenario. “We started with the premise that AIs are going to be doing the searching. That might seem niche now, but it’s going to be mainstream sooner than people think,” she said.

Of course, human users stand to benefit as well. As AI-powered search becomes more prevalent, regular web users could enjoy more precise answers with less digging. Imagine never having to click through pages of results – your search engine or digital assistant simply tells you the answer with confidence and cites the sources. Achieving that level of trust and accuracy is the holy grail of search. Reinforcement learning and advanced AI models will be key to getting there, as they help systems learn from mistakes and avoid pitfalls like misinformation or hallucinated answers.

This conversation underscored that the search landscape is on the cusp of major change. A startup like Exa, with its bold AI-first approach, shows how much innovation is possible when you rethink an old problem (web search) through a new lens (AI agents and machine learning). Whether Exa becomes the next household name in search or powers the behind-the-scenes searches in our apps, its vision is a sign of the times. As AI increasingly becomes both the user and the searcher, the nature of web search will fundamentally change. Instead of humans sifting through pages of results, intelligent systems like Exa will surface precise answers in real time. The question isn’t just how we’ll search in the future—but whether we’ll need to search at all.