"I want something quiet, with a garden, near a good international school, and not too far from my office in New Cairo." Parsing this request with a keyword search returns anything with "garden" and "New Cairo" — potentially thousands of irrelevant results. AI property matching returns the 3–5 units that actually match.
The limitation of SQL keyword search
Traditional property search uses SQL WHERE clauses: SELECT * FROM units WHERE area='New Cairo' AND bedrooms=3 AND price < 4000000. This works for explicit filters but fails for implicit requirements like "quiet", "good school district", or "investment potential".
Vector embeddings for property matching
Zold uses Google's text-embedding-005 model to encode every property listing into a 768-dimension vector. This vector captures the semantic meaning of the unit's features — not just keywords. A unit described as "south-facing corner apartment with panoramic golf course view, 3 minutes from Gate Academy" will have its vector positioned near units with similar characteristics, even if the exact words don't match a query.
Hybrid retrieval architecture
The most accurate results come from a two-stage approach: first, SQL hard filters eliminate impossible matches (unit is Sold, price is wildly outside budget, area is completely wrong); then pgvector similarity search ranks the remaining candidates by semantic similarity to the buyer's requirements.
Re-ranking with buyer context
Raw similarity scores don't account for buyer-specific priorities. Zold's property matching agent applies a learned re-ranking: if the buyer mentioned "for my kids' schooling", school proximity is weighted more heavily; if they said "investment", rental yield data and capital appreciation history adjust the final ranking.
Acceptance rate as a success metric
The property matching agent's accuracy is measured not by "did it return results" but by "did the agent accept the recommendations". Acceptance rate of 91% means agents see the AI's suggestions as genuinely useful — not noise to scroll past.
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