Known's Voice AI Aims to Drive Real-World Dates

While developing an app that used AI to book restaurant reservations for dates, Celeste Amadon and Asher Allen discovered a more significant opportunity: encouraging real-world meetings. This core idea is now attracting investor interest.

The pair built a voice-powered AI onboarding system for their app, eliminating lengthy forms. They found users enjoyed talking, leading to an average onboarding session of 26 minutes. This insight was the genesis of Known, a San Francisco-based dating startup.

"We realized that for the first time, we could gather enough authentic information about a person to suggest truly compatible dates. By streamlining this process and reducing rejection rates, we could create an experience that gets people on more real dates," Amadon explained.

Early testing indicated they were onto a promising concept.

In its San Francisco pilot, Known reported that 80% of its introductions led to in-person dates—a significantly higher rate than traditional swipe-based apps. Encouraged by these results, the startup secured $9.7 million in funding from investors including Forerunner, NFX, Pear VC, and Coelius Capital. This marks Forerunner's first investment in a dating app.

"Celeste has a deep, thoughtful understanding of her target user—primarily young women. While others focus on the male demographic, she addresses the nuanced, often unspoken desires of young women that they wouldn't typically list in a static profile. A conversation can reveal those subtleties, a service that previously required a $10,000 matchmaker," said Eurie Kim, a partner at Forerunner, in an interview with TechCrunch.

Amadon stated she has long been driven by scalable social impact and views the modern dating landscape as one of the defining challenges for her generation.

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"The loneliness epidemic in the U.S. is widely documented, and I genuinely believe it's our generation's most pressing issue," said Amadon, who left Stanford with Allen to build the company.

The app, currently in beta testing in San Francisco, uses its voice AI onboarding to ask users a series of conversational questions. Amadon notes this approach yields deeper user insights for better matches, with one user's session lasting an hour and 38 minutes.

According to Known, typed responses are often edited, while voice interactions feel more natural and personal. The AI can ask dynamic follow-up questions. For example, if someone recently moved to the city, it might inquire about their initial likes and dislikes.

After onboarding, the AI suggests potential matches. Users can query AI agents about these profiles and express interest. Once a match is mutual, both parties have 24 hours to accept the introduction and another 24 to agree on a date. This structure aims to minimize endless texting and ghosting, pushing users toward meeting offline. Post-date feedback helps the AI refine future recommendations.

Known hasn't abandoned its original concept; the app also recommends restaurants based on user preferences. Through AI chat and calendar integration, users can share their availability for first dates. During the beta, the company charged $30 per successful date, but it plans to experiment with different pricing models.

The startup currently employs three full-time engineers and four go-to-market specialists, supported by several contractors. Amadon, with a background in political internships, and Allen, who previously worked on product at AI shopping app Phia, plan to expand their team with the new funding.

Known is active in San Francisco and plans a broader launch early next year.

Several other new startups, like Overtone and Hinge CEO Justin McLeod's new venture, are also leveraging AI for deeper user insights and matchmaking. Some aim to replicate expensive matchmaker services at a lower cost. Established players like Tinder, Bumble, and Hinge are integrating AI features to retain users. Despite the growing competition, Amadon welcomes it.

"Regarding other dating startups, I'm thrilled to see more builders in this space because it signals a necessary shift away from the swipe-based model. Most of what I've seen is quite different from what we're building at Known," she said.