The Challenge
A real estate SME in India had accumulated one of the more valuable assets a property platform can have: 15 million rows of listing data built up over years of operation. The problem was that the data was nearly inaccessible in practice.
Fragmentation at scale — Listing data was distributed across legacy systems that had never been unified. Years of growth had left the schema inconsistent, with multiple overlapping fields describing the same properties in different formats.
Real-time query complexity — Answering a customer’s question about available 3BHK properties under ₹80L within 5km of a school required complex cross-table joins. The existing infrastructure couldn’t deliver that in seconds — or at all for some query patterns.
Competitive urgency — Larger platforms were beginning to market AI-powered search. Leadership needed to move fast to claim the same positioning before the window closed.
The Solution
Superteams deployed a fractional Agentic AI Team to tackle the problem in stages over 90 days.
Data foundation — Ingested and normalized all 15M+ rows into cloud-native infrastructure with a consistent schema. Duplicate and conflicting fields were resolved. The data became queryable for the first time at full scale.
RAG pipeline with natural language to SQL — Built a pipeline that maps natural-language customer queries to optimized SQL joins across the normalized data. A customer asking “show me 2BHK flats in Koramangala under ₹60L with a gym” gets accurate results in seconds.
Performance layer — Implemented caching, indexing, and query pre-computation for the highest-frequency query patterns to ensure sub-second responses at volume.
AI chat interface — A conversational layer that customers interact with directly, reducing call-center dependency and shortening the sales cycle by surfacing qualified listings faster.
The entire system was built and validated as a working prototype within the 90-day window.
Results
The AI assistant launched with the full 15M+ row dataset live. Query latency dropped 42%. The client became positioned as one of India’s first AI-driven real estate SMEs — a meaningful competitive differentiator in a market where most players were still relying on keyword search.
Qualified leads generated through the assistant reduced sales cycle time by surfacing the right properties earlier in the customer journey.