Memory Lab: Can an AI Broker Remember What Clients Really Want?
Memory Lab: Can an AI Broker Remember What Clients Really Want? Built for the Hindsight Hackathon, with persistent agent memory. A client tells a property broker: "3BHK, Velachery, ₹15,000." The broker shows four flats. The client rejects all four. 🏠 Here's the funny part: none of the rejections were about budget or location. ❌ "No parking." ❌ "Not north/east facing." ❌ "The road is too narrow."…
An AI broker designed for the Hindsight Hackathon may soon be able to remember clients' real preferences, not just what they say upfront. Known as Kumar's Notebook, the prototype lets brokers log every property shown, the client's reaction, and the reason for rejecting it. The AI then uses this information to better match listings with clients' true desires.
In tests, the AI narrowed down property suggestions for clients who rejected flats for reasons like no parking, non-north/east facing orientation, or narrow roads. It even recognized hidden preferences not overtly expressed by clients, such as a dislike for ground floor locations. The model can learn from as few as two rejections with the same reason, quickly adapting to a client's true needs.
While still a prototype using fabricated data, the system could potentially reduce the time and site visits needed for property searches. By learning from past interactions, the AI broker aims to move beyond relying solely on client input to suggest properties that truly align with what clients want. Developers plan to expand the prototype with voice notes, WhatsApp notes, and pilot testing with real brokers to measure real-world impact.
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