Managing a real estate inventory of 50 units in a spreadsheet is possible. Managing 500 is painful. Managing 5,000 is impossible — without purpose-built software.
The inventory management problem
Real estate inventory has unique challenges: units change status multiple times (Available → Reserved → Under Contract → Sold → Resale), prices change with market conditions and phases, units can be held for multiple agents simultaneously (causing double-booking), and every status change needs a timestamped audit trail for compliance.
Core data model
Every unit in Zold has: Project (developer + location + launch date), Building/Tower, Floor, Unit number, Type (apartment/villa/penthouse), Bedrooms, Area (sqm), View, Base price (and price per sqm), Payment plans (multiple per unit), Current status, Status history (every change with actor + timestamp), Reservation history, and Uploaded files (floor plan, view photos, specs PDF).
Reservation management
The 48-hour reservation hold is the most critical inventory management process. When an agent reserves a unit: the unit status immediately changes to Reserved in real time (preventing double-booking); a countdown timer starts for exactly 48 hours; the system notifies the agent and manager via WhatsApp and push notification; at T-6h and T-2h, reminder notifications fire; at T+0h, if no payment confirmation is recorded, the unit automatically reverts to Available — no manual intervention needed.
Bulk import
For large launches, Zold accepts Excel imports. The expected format: one row per unit, with validation for duplicate unit numbers, invalid status values, and missing required fields. Errors are returned as a downloadable report — the user fixes and re-imports.
Pricing and payment plans
Off-plan projects have payment plans: 10% down + 1% monthly for 3 years is a typical structure. Zold stores each payment plan as a structured record tied to the unit, enabling the commission calculator to correctly compute future installment commissions.
The pgvector advantage
Zold uses pgvector to store semantic embeddings of each unit's description and features. When a lead says "I want a quiet unit with Nile view and a study room under 4M EGP", the property matching agent uses vector similarity search to rank units — not keyword matching. This dramatically increases the relevance of property recommendations.
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