The real estate and construction sector has been experiencing a profound structural transformation for several years, accelerated by the digitization of value chains, increasing urbanization, energy performance requirements, and new user expectations regarding the use of built spaces.
This sector, historically characterized by a high capital intensity, low digitalization, and long cycles, now faces increasing project complexity, volatility in material costs, demands for transparency from investors, and the need to construct and operate smarter, more sustainable, and more flexible buildings.
In this context, artificial intelligence proves to be a key lever for modeling, anticipating, optimizing, and automating every stage of the life cycle of a real estate asset or a construction project. It allows for analyzing the dynamics of the real estate market, predicting valuation trends, automating the technical management of buildings, optimizing load plans and resources on construction sites, and enhancing operational safety.
It acts both as a strategic decision aid, an operational efficiency tool, and a catalyst for the transition to a smarter and more resilient city.
What NeuriaLabs brings to the real estate and construction sector
NeuriaLabs provides stakeholders in the real estate sector (developers, landlords, asset managers, property managers) and the construction sector (project owners, general contractors, design offices, technical operators) with specialized expertise in algorithmic valuation of real estate data, predictive operation optimization, risk modeling, and intelligent maintenance of infrastructures.
We develop solutions capable of processing massive volumes of heterogeneous geographic, financial, technical, behavioral data and transforming them into concrete performance, profitability, and sustainability levers.
Our support is part of an integrated transformation logic: from the land prospecting phase to architectural design, from construction execution to technical management of the asset, through marketing, operation, and long-term maintenance.
Use cases in real estate and construction
The use cases of artificial intelligence in this sector cover a wide variety of operational and strategic challenges:
• Dynamic valuation of real estate: integrating multiple variables (property type, neighborhood dynamics, demographic flows, market pressure) to obtain an evolving probabilistic estimate of an asset's value.
• AI-assisted land prospecting: automated detection of high-potential parcels, analysis of urban planning constraints, cross-referencing cadastral data, local planning documents, and socio-economic databases.
• Optimization of construction schedules: predictive modeling of delays, dynamic redeployment of human and material resources, integration of weather, logistical, and regulatory data.
• Automatic detection of anomalies on construction sites: use of computer vision to identify in real time non-compliance, security incidents, or regulatory compliance deviations.
• Forecasting of rental vacancies or tenant turnover: anticipating the behavior of tenants or users based on weak signals (payments, intervention requests, interactions) and segmenting at-risk profiles.
• Automation of technical management of buildings (GTB): real-time adjustment of temperature, ventilation, lighting, or security based on actual user behaviors.
• Predictive analysis of maintenance or renovation costs: projecting future needs based on maintenance history, modeled wear of equipment, and evolving technical standards.
Solutions developed by NeuriaLabs for the real estate and construction sector
To meet the requirements for management, profitability, and sustainability of the sector, NeuriaLabs designs advanced and modular artificial intelligence solutions, integrable into existing business systems or deployed as specialized vertical platforms.
Among these solutions are:
• Tools for automated valuation of real estate assets: dynamic estimation platforms based on non-linear regression models and databases enriched by supervised learning, offering real-time projections of the market or rental value of a property.
• Intelligent land monitoring systems: opportunity detection engines based on the correlation of cadastral, demographic, regulatory, and socio-economic data, integrating personalized strategic filters.
• Modules for optimizing the construction cycle: intelligent dashboards coupled with predictive models of time or budget drift, integrating supplier schedules, technical incidents, and regulatory constraints.
• Artificial vision solutions applied to construction sites: systems mounted on drones or fixed cameras, capable of continuously identifying compliance gaps, security risks, or defects during execution.
• Tools for predicting vacancies and rental turnover: behavioral scoring algorithms correlated with users' history of consumption, communication, and intervention, with possible integration into real estate CRM systems.
• Predictive technical management systems for buildings: intelligence embedded in GTB networks, autonomously managing the use of equipment (HVAC, lighting, security) based on observed habits and energy efficiency goals.
• Long-term maintenance simulators: models based on maintenance history, equipment life cycles, sectorial feedback, and regulatory obligations, allowing for anticipating maintenance or renovation budgets over several years.
All these solutions are developed in compliance with current standards (RT2012, RE2020, BIM standards, ESG references) and can interface with property management systems (ERP, CMMS, BIM, GIS), ensuring their compatibility with the technical environments of contracting authorities.
