Cairo: TECHz – Ashraf Gaber
During the NEO-GEN Prop-Tech Conference 2025, Eng. Mahmoud Hegab, Chief Executive Officer at Makkook.AI, outlined a practical framework for deploying operative AI across real estate development and construction, starting from a central question: why do most AI initiatives fail to deliver real return on investment?
Hegab opened with a stark data point: only 11% of enterprise AI initiatives achieve tangible ROI, and just 6% of executives clearly understand what AI can and cannot do, a gap that explains the disconnect between AI hype and real market outcomes.
From Gut-Driven Decisions to Data Platforms, Hegab structured his keynote around five active pillars in the property ecosystem: developers, regulators, contractors, facility managers, and smart cities, assigning to each a clear set of pain points and a corresponding stack of specialized AI platforms.
For developers, he highlighted three core challenges: slow, intuition-driven portfolio decisions; limited, manually constructed product-mix scenarios; and static pricing and payment models that do not keep pace with market velocity. In response, he presented three main engines:
• Portfolio & Investment Engine: This engine evaluates land plots, analyzes their highest and best potential uses, and simulates hundreds of development scenarios per plot using historical data, macroeconomic indicators, and live market feeds. It is designed to cut feasibility study cycles by an estimated 85 – 95% and improve ROI-related decision-making by 5 – 8%.
• Product Mix Optimization Engine: It designs the optimal unit mix for each project or phase based on demand signals, affordability indices, and competitor data, all within zoning and brand/architectural constraints. According to the figures presented, it can reduce the risk of slow-moving unit types by 7 – 12% and increase project absorption rates by 5 – 8%.
• Dynamic Sales Engine: This engine applies dynamic pricing to units, down payments, and installment plans, integrated with CRM, ERP, inventory, and live market data. The reported impact is a 15 – 25% improvement in inventory turnover and a 2 – 5% uplift in realized project revenues.
Regulation and Construction: Cutting Permit Time and Error Margins
On the side of governments and regulators, Hegab focused on four structural obstacles: lengthy permit cycles, heavy manual review of complex drawings and BIM models, strict safety and code-compliance requirements, and bureaucratic friction coupled with human bias in approvals. Here, the Permit Code Compliance Engine takes center stage, automatically reading BIM and CAD files, checking them against local building and zoning codes, and generating auditable violation reports. The system aims to reduce permitting cycle time by 75 – 85% while providing full rule coverage and transparency levels approaching 99.9% in decision logs.
For contractors, Hegab described fragile profit margins, multi-site scheduling complexity, and weak real-time visibility as the dominant risks. His proposed stack includes a Bidding Intelligence Engine that extracts accurate BOQs from BIM models and enriches estimates with historical actual-cost data, cutting bid-preparation time by 70 – 80% and reducing technical and pricing errors by 8 – 15%. A complementary Scheduling & Resource Allocation Engine pushes resource utilization into the 85 – 97% range and lowers overtime and delays by 20 – 30%, while computer-vision tools on site cameras and drone footage help cut safety and quality violations by 40 – 50% and rework by 20 – 30% through real-time monitoring of compliance and progress.
Facilities and Smart Cities: From Reactive to Predictive Operations
In facility management, Hegab addressed the constant flood of maintenance requests and fragmented tools that keep operations reactive rather than proactive. The Resource Scheduler & Tenant Engine prioritizes tickets by severity, SLA level, and tenant impact, routing field teams based on skills and real-time availability, and connecting directly to tenant feedback channels. As presented, this can push resource utilization above 85% and SLA compliance beyond 98%.
This is coupled with a Predictive Maintenance Engine that monitors critical assets such as elevators, HVAC systems, pumps, and generators around the clock, learning from sensor data and maintenance logs to detect anomalies and trigger work orders before failures occur. The claimed effect is up to a 95% reduction in breakdowns and a 15 – 25% decrease in operating expenses.
At the smart-city and sustainability layer, Hegab questioned the ROI of “green” initiatives in the absence of robust measurement, then highlighted an Energy Optimization Engine that orchestrates energy use across buildings and districts, integrating district cooling/heating and smart meters. This platform targets energy savings in the range of 15 – 30% and a similar reduction in emissions. He also showcased a Traffic & Mobility Management Engine, driven by live camera and sensor data, that manages gates, internal roads, and parking, delivering reported improvements of 25 – 40% in safety and user experience and cutting queues and travel time by 10 – 25% via a unified city operations layer.
Integrated Technical Architecture and Research Roots Makkook. AI’s solutions rest on a four-layer architecture: an internal and external data layer (land banks, cost curves, sales and customer data, competitor benchmarks, macroeconomic and environmental data), an integration layer with enterprise systems such as CRM, ERP, inventory and permit portals, an intelligence layer of forecasting, optimization, and simulation models, and finally an output & ROI layer that translates model outputs into actionable recommendations and operational dashboards.
The company’s trajectory, as presented by Hegab, follows a gradual evolution: a research phase between 2007 and 2017 that produced 16 scientific AI papers, a consulting phase from 2018 to 2023 involving 20 mega projects, and its current form as a full-stack enterprise AI studio serving sectors including energy, manufacturing, supply chain, construction, and retail, supported by a scientific committee of academic and industry experts.
Through this lens, Hegab offered a concrete model for moving from generic AI narratives to embedded, operative AI systems tied directly to performance and ROI metrics – leaving the proptech sector, as he framed it, with a clear choice: remain in the realm of pilots and experiments, or commit to AI platforms architected from day one around measurable return on investment.
