AI for Real Estate

The Problem

Agents manually match properties to clients based on basic criteria like bedrooms and budget. High-value buyers have nuanced preferences — lifestyle, neighborhood character, investment potential — that standard filters miss entirely. 60% of viewings result in immediate rejection.

Our Solution

AI learns buyer preferences from browsing behavior, viewing feedback, and transaction history — not just stated criteria. Proprietary matching algorithm weighs 50+ property attributes including location scoring, comparable sales trajectory, and lifestyle compatibility.

Expected Outcome

Viewing-to-offer ratio improved 40%. Time to close reduced by 3 weeks on average. Monaco agency matched €15M+ properties to international buyers with 85% first-viewing satisfaction rate.

The Problem

Manual valuations take 4-6 hours per property and vary ±15% between analysts. Luxury clients expect instant preliminary estimates. Outdated comps in fast-moving markets like Monaco or Côte d'Azur lead to mispricing.

Our Solution

AI analyzes comparable sales, market trends, property features, renovation history, and hyper-local factors (street-level data, proximity scoring). Combines hedonic pricing models with real-time transaction feeds.

Expected Outcome

Valuation accuracy improved to ±3% (vs ±15% manual). Generation time reduced from hours to seconds. Agents price competitively from day one. Integrated valuation methodologies compliant with RICS and TEGoVA standards.

The Problem

Commercial lease review requires reading 50-100 page documents per property. Key clauses (break options, rent escalation, maintenance obligations) are buried in legal language. Portfolio managers with 200+ leases cannot track critical dates manually.

Our Solution

AI extracts key terms, dates, and obligations from lease documents automatically. Builds a structured database with automatic alerts for critical dates, renewal windows, and compliance deadlines.

Expected Outcome

Lease review time reduced 80%. Zero missed critical dates across a 200-property portfolio. Annual savings of €300K in legal review costs for a mid-size property manager.

The Problem

Reactive maintenance costs 3-5x more than preventive approaches. Building managers rely on tenant complaints and scheduled inspections. Equipment failures in luxury properties damage reputation and tenant retention.

Our Solution

AI integrates with building management systems (BMS) and IoT sensors to predict HVAC, elevator, and plumbing failures before they occur. Prioritizes maintenance by impact on tenant experience and cost.

Expected Outcome

Maintenance costs reduced 25%. Equipment downtime cut by 60%. Tenant satisfaction scores increased 15%. Predictive accuracy of 85% for major system failures, 30 days in advance.