Sitch Bets on Intentional Dating with AI-Powered Matchmaker Model
A new dating startup, Sitch, is entering the crowded relationship-tech space with a unique pitch: combining artificial intelligence with human matchmaking insights to deliver a more curated experience for daters tired of endless swiping.
Unlike most dating apps that prioritize speed and volume, Sitch is opting for a more in-depth approach. During onboarding, users answer nearly 50 questions — via text or voice — allowing the app’s AI to generate match suggestions tailored to personality and compatibility. Once two users agree to connect, they’re placed into a chat alongside the app’s AI assistant. Users can then provide feedback after dates, which the system uses to refine future recommendations.
The technology underpinning the app was trained using more than 75 parameters developed by co-founder Nandini Mullaji, a Stanford Business School graduate with family ties to traditional matchmaking. Mullaji, who previously contributed to Bumble’s expansion in India, emphasized the limitations of swipe-based models, arguing that current apps rely on insufficient data to assess long-term compatibility.
Sitch’s monetization strategy departs from the typical subscription model. Instead, users pay per match, with packages ranging from three setups for $89.99 to eight for $159.99. This upfront model, according to investors, allows the company to prioritize matchmaking outcomes over user engagement metrics – something that might make more users willing to give AI a chance even if they generally think that it wouldn’t be a worthwhile addition to their dating experience.
Backed by $7 million in total funding – including $5 million from M13 and a16z’s speedrun program – Sitch is currently live in New York, with plans to expand to additional cities later this year. While larger players like Tinder and Bumble are also exploring AI features, Sitch is betting that users seeking more intentional connections will welcome a return to matchmaking, this time guided by machine learning.
