CONCEPT PROJECT · ACADEMIC ASSIGNMENT
PropIQ
Role
UI/UX Designer
Timeline
1-Week Sprint · Feb 2026
PLATFORMS
Android / iOS
TOOLS
Figma
Problem Statement
Property search in India is broken for most users. Platforms like MagicBricks, 99acres, and Housing.com are built around filters — location, budget, BHK type, furnishing, amenities. For someone buying or renting a home for the first time, navigating 8–10 filters before seeing a single result is overwhelming. Users either drop off mid-search or end up with results that don't match what they actually wanted.
The core problem: traditional platforms make users think like a database, not like a person.
The Solution
PropIQ replaces filters with conversation. Users simply type what they're looking for — for example, "I want a 2BHK apartment in Hyderabad under 50 lakhs" — and the AI does the rest. It understands natural language, asks smart follow-up questions, and surfaces relevant listings instantly.
The tagline designed around this idea: "Your smarter way to discover the perfect property."
Competitive Analysis
None of these platforms let users search in natural language. PropIQ's core differentiator is removing the assumption that users know what filters to apply in the first place.
User Flow
The full conversational flow: user opens the app → AI greets them and asks what kind of property they're looking for → user enters their requirements → AI asks additional clarifying questions if needed → AI shows results.
From there, two branches: if matching properties exist, the user can select one, view details, and either contact the agent/save the property or go back to results. If no properties match, the app asks whether to save the search as an alert and notify the user later — turning a dead end into a reason to come back.
Wireframes
Low-fidelity wireframes were used first to test the conversational structure and information hierarchy, before moving into high-fidelity screens built in Figma for both Android and iOS.
Why It Was Designed This Way
Four key decisions, each aimed at reducing cognitive load, building trust in the AI, and making sure users never hit a dead end:
Entry point — Chat as the primary home screen.
Replaces a search bar with a chat interface — everyone already knows how to send a message. Quick-reply chips remove blank-page anxiety for first-time users. (Impact: Cognitive load)
Conversation flow — One question at a time.
A single clarifying question per turn, with Yes / No / Skip options. Skip is critical — it respects the user's time and prevents drop-off on irrelevant questions. (Impact: Completion rate)
Empty state — No results become a saved alert.
Dead ends turn into re-engagement hooks: when nothing matches, PropIQ saves the search and commits to notifying the user later. (Impact: Retention)
Navigation — A persistent chat bar everywhere.
The "Describe your dream property…" input lives on every screen — list, details, saved — so users can restart a search at any point without navigating back. (Impact: Navigation friction)
What This Project Taught Me
AI products are conversation design problems. The visual UI is almost secondary — what the AI says, when it asks follow-ups, and how it recovers from failure carry as much design weight as the screen layouts.
Edge cases are features, not afterthoughts. The no-results alert was the most impactful screen designed in this project — treating failure states as product opportunities is what separates good UX from average UX.
Simplicity requires more decisions, not fewer. Removing filters didn't simplify the design problem — it shifted complexity into conversation flow, AI tone of voice, and response design. Simpler interfaces hide deeper design thinking.
What I'd Explore With More Time
User interviews with first-time renters in Hyderabad
Voice input UX design
Map view on the results page
Handling ambiguous or incomplete queries
AI tone-of-voice testing
This was a 1-week academic assignment covering competitive analysis, user flow mapping, and low-to-high fidelity wireframes — usability testing and further iteration were outside what the assignment required. Working within that scope still surfaced a real opportunity: the conversational approach clearly has potential, and it's a direction I'd like to take further on my own — starting with actual user interviews with first-time renters in Hyderabad, then testing how the AI's tone lands in practice.