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Y Combinator is renowned in Silicon Valley for a lot of reasons, but there’s one service that has quietly become one of its most powerful: an online founder-matching tool.
“I think this is the most valuable digital product that YC has built (i.e. more valuable than Bookface, etc,). It’s astonishing how many founders I meet who met each other on the YC co-founder matching platform,” tweeted seed investor Nikhil Basu Trivedi. (Bookface refers to YC’s famed online collection of how-to startup advice for its program participants.)
Recent Y Combinator grad Hona is an example, although its founders’ meet-cute story is a bit more exciting than just using that tool.
Hona is a GenAI medical records startup. It integrates into multiple electronic records systems and then summarizes a patient’s medical records, helping doctors prep for the patient’s visit.
It was initially founded by two friends who have known each other since middle school, Danielle Yoesep and Adam Steinle. They reconnected after graduating college and respective early careers in tech and biotech. Steinle had been a biomedical engineer, Goldman banker, and big tech product manager at Facebook. Yoesep was a scientist for a biotech startup that had just been acquired. They were hanging out with their high school friends while home for Thanksgiving, chatting about wanting to do a startup when the idea for Hona arose. While neither of them were doctors themselves, both had family members who were doctors or in healthcare and they soon settled on an idea: AI to assist doctors with patient data summaries.
They knew they needed an AI specialist co-founder, so signed up on the Y Combinator Co‑Founder Matching Platform. They found one in Shuying Zhang, who also knew she wanted to do a startup, something in healthcare and AI, and had signed up on the service. Zhang’s background combined biomedical engineering and software development, most recently working on AI at Google, and she was at Amazon prior to that.
What came next was a process that sounds a bit like Tinder for co-founders.
Yoesep and Steinle swiped through profiles in the matching tool as did Zhang. Each of them held several meet-and-greets with potential co-founders. When Zhang met with Yoesep and Steinle, they instantly clicked so well, that the long-time friends offered Zhang a full one-third share of the company.
“We literally met each other and like three weeks later, we’re jobless, trying to build this,” Steinle told TechCrunch.
Having met on Y Combinator, with their backgrounds in tech, they were exactly the type of startup sure to be accepted into the competitive program. They immediately applied to YC for the Summer 2023 batch.
And they were promptly rejected.
So they got to work on their own, building a prototype, showing it to their network of doctors, earning solid reviews, and raising a small seed round.
About four months later, they applied to YC again for the winter 2024 batch, and were accepted. One of the reasons they got in the second time, Yoesep recalled, was that they never changed directions, or never pivoted, to use the hackneyed Silicon Valley term. Another reason was “because of our dynamic during our interview, showing that we had grown close and enjoyed working together,” she said.
Things started cooking for them after that. Medical doctors at Duke and Harvard agreed to test the product and write a white paper, due to publish later this month. Some angels who were known in the tech and biotech worlds invested. And by the time Hona graduated from YC and did its famed Demo Day, it had already raised a $3 million seed round from General Catalyst (which is pursuing healthtech so seriously it bought a hospital system), Samsung, Rebel Fund (founded by Reddit co-founder Steve Huffman and Cruise co-founder Daniel Kan) and 1984 Ventures.
Hona still has a tough road ahead. AI for medical transcription is an increasingly crowded field. Big cloud providers like Google and Amazon are offering such tools and dozens of startups are tackling it, too.
But Steinle says that Hona will compete because it’s “super customizable” to search through medical records for the specific data a particular doctor needs prior to seeing a patient. A cardiologist would get a different summary than a nephrologist. For instance, the upcoming white paper is on kidney stone referrals, so “so we’re pulling stuff like how many millimeters was the stone on the right here?” Steinle describes.
As for Zhang, her advice for others who dream of doing a startup, and are considering using YC’s matching tool, is to “just go out and try,” she says. “Once you start working with people, you will quickly have a good sense whether you get along. You will know right away.”
Chatbots have come a long way. For years, they were limited to responding with predetermined replies that followed a simple logic structure. But customers can have complex problems, and no tree-diagram of possible replies can have enough branches to account for all the edge cases that arise. Thankfully, the advent of large language models (LLMs) has finally rendered chatbots useful. Armed with mountains of data, startups are now leveraging generative AI to create custom chatbots for all sorts of businesses and use cases, particularly those where people want to be sure about what they’re buying.
Thailand’s HD is building chatbots aimed at one such industry: healthcare. The company started as a marketplace for third-party healthcare and surgery services and sees a strong case for developing conversational AI for the healthcare customer journey.
“The products we are selling are not the typical stuff you buy on Amazon. They are hospital services, so people shop the same way as they do offline,” co-founder Sheji Ho told TechCrunch.
Even though each product has a description on HD’s marketplace HDmall, Ho says people still prefer to ask first: “90% of the chat messages are people asking about product information. The chat commerce process [is similar to] the offline experience,” he explained.
To advance its AI ambitions, HD recently raised a $5.6 million Series A round led by SBI Ven Capital, a subsidiary of the Japanese financial giant SBI Group, through its joint fund with Kyobo Securities from South Korea and NTU Singapore’s NTUitive. M Venture Partners, FEBE Ventures, Partech Partners, Ratio Ventures, Orvel Ventures, and TA Ventures also participated in the round.
Ho says HD is working on building the “Sierra AI of the Southeast Asian healthcare industry.”
Over five years, Ho and his team saw that the faster HD’s representatives responded to inquiries, the higher the conversion rate. “So there’s a very good case to use AI to automate that process,” he said. The company expects conversational AI to not only help cut costs, but also allow staff to focus on higher-value tasks, like answering more complex customer questions.
But Ho and his team seem to have a realistic view of what they can achieve. It will not be able to match U.S. firms that have “nearly limitless access” to powerful GPUs, talent and venture capital, so the company is focusing on building vertical AI, with local data being its moat.
“Emerging markets need to compete and take advantage of AI by using the data they have — proprietary data that nobody else has,” said Ho. “We see that happening in other places, too. Some call this vertical AI, where they use a vertical domain-specific data that is proprietary to a certain business or industry. Then they build on top of that, and they enhance the model to the point where they have an AI application that is practical and they can start monetizing.”
HDmall. Image Credits: HD
HD therefore plans to train chatbots with the sea of anonymized transaction, chat, FAQ, and product catalog data it has accumulated over the years. Currently, 30% to 40% of the company’s transactions are done through chat commerce with customer service workers.
The company is planning to use the new capital to roll out a chatbot for its marketplace within three months and to open up the technology for third-party use by the end of this year. Potential customers are hospitals and clinics that need 24/7 customer support. The startup has already worked with some 2,000 healthcare providers in Asia, which will enable it to fine-tune its base language model for the healthcare domain. Eventually, the chatbot service will give the company a new SaaS revenue stream in addition to its marketplace commissions.
Like many other startups, HD cut costs and aimed for sustainable growth during the COVID-19 pandemic. The company “didn’t necessarily need to raise,” as it was heading toward profitability on 2x year-on-year growth after the pandemic was over, but Ho also saw an opportunity to move faster when others were slowing down.
“You hear people saying, ‘You should raise money when you don’t have to raise.’ If we raise now, then everything else will be cheaper. For example, customer acquisition is cheaper because everyone else stopped advertising in a recession. Talent acquisition also [costs less] because companies are unfortunately laying off people.”
Globally, startup valuations have been on a decline for the last few years. HD hasn’t escaped that wave, but Ho says he recognized the benefit of accepting a more moderate valuation early on.
“I think it’s pointless for companies to worry about valuation at such an early stage. We’ve seen that over the past few years, especially 2021, when companies started the race at such high valuations,” he said, pointing as an example to Indian health tech unicorn, Pristyn, which lost half of its valuation after a period of frenetic growth.
“Because they raised at such a high valuation, they were forced to grow super aggressively, and that leads to founders and companies cutting corners. You can’t cut corners when you’re in healthcare and you’re dealing with people’s lives,” Ho said.
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