Exa is building a perfect search engine
Problem
Most of the valuable data on the web can't be found
The world needs perfect web search. You should be able to find all the data you want, no matter how complex your need.
For example, "every space-themed podcast episode this week", or "all the European competitors to my company ranked by employee count". You can't do these because today's search engines use old search technology that's optimized for ads.
Exa is the first search engine optimized to return exactly what you ask for. We sell our search usage-based to customers -- no ads, just quality. We're an applied AI lab and we've barely begun. Our ultimate goal is perfect search.
Novel neural search architectures
We train novel architectures for web search using end-to-end neural networks. Unlike keyword methods, neural methods get better with more compute and will win in the long run.
We're lucky to now own 18 8xH200 nodes worth of research compute... also known as an exa-cluster :)
Web-scale infrastructure
Building a search engine from scratch requires building massive-scale infrastructure. There are 100s of billions of webpages (roughly an exabyte!) that need storing, processing, indexing, and serving at high throughput.
Building this is fun but quite difficult. That's why search tools, like SearchGPT, actually rely on 3rd party search engines under the hood.
Funded by top investors
Lightspeed, Nvidia, YCombinator
Advised by top researchers
from OpenAI, Google, and Bing
We're an SF team of builders and researchers
Will was one of the first engineers at Cresta where he built real time AI products. He studied CS and physics at Harvard, where he researched human/AI interaction and led the robotics club. Will considers himself an expert in both embedding models and chocolate chip cookies -- the jury is still out on which is more critical for company operations.
Jeff spent three years building data and web infra at Plaid. He studied CS and Philosophy at Harvard, where he ran a GPU cluster in his dorm room and was roommates with Will. The team estimates that 20% of social analysis in San Francisco traces back to one of Jeff's many viral tweets.
Ben previously did quant trading at SIG and before that took the hardest math course in the country at Harvard. When we find frisbees, tailor made suits, or scribbled math formulas lying around the office, there's usually a Ben behind it.
Hubert previously worked on projects like particle simulations and automated wheelchairs. He studied CS in the Yao Class at Tsinghua University. Hubert's appetite for clean microservice architecture is perhaps only matched by his appetite for Haribo sour candy.
Shreyas previously worked on various projects, from training neural networks in Haskell to building a game streaming engine. He studied CS at the University of Waterloo. You can typically find Shreyas analyzing the price/performance of AWS services or crushing the team in basketball, sometimes at the same time.
Isabelle previously was at Mckinsey consulting for tech companies. She studied politics and philosophy at the University of Melbourne. Isabelle has the remarkable ability to work intensely on one screen while at the same time watching a technical lecture on another. The team is unsure whether this comes from McKinsey training or is just an Australian thing.
Eugene previously designed and built LLM products at Palantir. He studied CS at Minerva University. Eugene loves two things and hates one -- designing beautiful frontends, optimizing high performance backends, and eating vegetables.
Michael previously worked on ML and privacy at various companies, including Apple. He studied CS at Harvard University. He also somehow finds time to cook chef-level meals and have PhD-level knowledge on nearly everything -- both of which the team enjoys consuming.
Thais was previously VP of Marketing at Nate, Hubla and most recently co-founded her own startup in Brazil. She studied Economics and Econometrics at the University of Chicago. She might beat Exa's search in the amount of restaurant recommendations or Taylor Swift references she can provide on the spot.
Vishal was previously a management consultant at McKinsey & Co. and on the strategy team at TikTok Australia. He studied EECS and Finance at Monash University, Australia. Vishal is that rare person who can both code up a product and sell it to anyone. Given his messianic startup skills and obsession with Dune 2, some believe the similarity between 'Vishal' and 'Paul' is more than a coincidence.
Felix previously worked on opensource projects from next-gen text editors to composable knowledge management systems. He (almost) studied CS and philosophy at UIUC until he realized that he is already a beast. The only job Felix should not do is corrosion engineering, because he deeply wishes to convert the world into rust.
Stacey previously worked on business development at Awesomic. She got a bachelors and masters from Taras Shevchenko National University of Kyiv, basically the Harvard of Ukraine. Stacey goes by many names at Exa -- workplace operator, recruiting coordinator, chief happiness officer -- but perhaps her most beloved name is "greatest cookie baker of all time". These cookies are unfairly delicious.
Elizabeth previously worked in product at Invert, building biotech software that helped grow cells. Before that, she worked with Shell to figure out their transition to clean energy. Elizabeth is someone who can do everything from automating bioprocesses to optimizing developer experiences, still finding the time to almost get eaten by bears on her weekend trail hikes.
Joshua previously studied CS at the University of Chicago, where he solved ML problems on 3D reconstruction. These days, he builds virtual worlds and massive lego datasets (and even bigger Exa datasets). Given his ML abilities and all the cities he's lived in across the Midwest/East Coast, some believe Joshua has neurally solved the Travelling Salesman Problem in polynomial time.
You previously worked on some project that demonstrated exceptional skill. You studied CS at somewhere, but far more importantly want to learn by joining a startup working on massive-scale ML/infra. You are excited to tackle a mission as old as ancient greece -- organize the world's knowledge -- and recognize that to do that You must meet Us and become We.