DTM (Dating to Marry) is a relationship-focused app for people seeking a life partner. It automatically filters profiles based on a user's interests and personality to surface highly compatible matches, ensuring users only see high-quality candidates who are genuinely on the same page.
The core problem was finding and recommending the best-matching users to each individual based on their specific preferences — at scale, and without ever leaving a user with an empty match list.
User data is streamed continuously through MongoDB, and each user is matched against a pool of candidates up to a defined limit. When the best-fit pool is exhausted, the system applies progressive filter relaxation to widen the search. If matches are still unavailable, a retention-based fallback logic kicks in — ensuring users are never left without recommendations for the duration of their active retention period.
We worked with the client to understand their vision for the matching experience, then researched how existing dating platforms structure their recommendation engines. Based on those findings, we built the system using FastAPI for the API layer and MongoDB for real-time data streaming and storage.
By combining real-time data streaming with adaptive filtering logic, DTM continuously delivers relevant matches while keeping the experience personal and responsive.
Enhancing Compatibility Through Intelligent Matching DTM's matching engine is built to stay accurate under changing user pools while never leaving a user without options.
“Great matchmaking isn't just about filtering data — it's about making sure no one is ever left without a possibility.” — Manu Sharma, CEO
Blending Data Intelligence with Human Connection We designed DTM's backend to feel effortless to the user while running sophisticated matching logic behind the scenes.
Delivering Meaningful Matches at Scale DTM transformed how users experience matchmaking — consistent, relevant, and never empty-handed.