Construction, Architecture, and Engineering: How Data and AI are Transforming Real Estate Developers and Expanding Results

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AI-generated summary:

This article presents how data, artificial intelligence, GenAI, and machine learning are transforming the construction, architecture, and engineering industries, with a special focus on real estate developers. In the first part, we explore in a didactic way how these technologies work—from data collection and integration to the use of predictive models, computer vision, and generative AI to reduce costs, increase productivity, and improve the customer experience. In the second part, we present real-world case studies of companies in the AEC sector that already use AI in security, construction management, and documentation, as well as two cases from BlueMetrics: a US real estate management company that began predicting revenues with a margin of error of less than 5%, and a Brazilian real estate developer that reduced delinquency by up to 46% by automating credit granting.

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Fundamentals and innovations: how data, AI, and GenAI work in the real estate development industry.

1. Data as a basis (collection, integration and quality)

Before any advanced application, it's essential to understand that data is the foundation of all digital transformation in the sector. Without consistent, integrated, and reliable data, no AI or Machine Learning solution can generate real value. In the context of real estate developers, we can have sources such as the following:

• Architectural and structural designs (BIM models)

• Data related to project scheduling, planning, and execution.

• Contract, supplier, and input management systems

• IoT sensors on the construction site (weather, humidity, temperature, vibration)

• Maintenance history and customer feedback.

• Building logs (water consumption, energy consumption, elevators)

  
  

Advanced machine learning models require that this data be clean, aligned, and interoperable. In many cases, ETL/ELT pipelines are used for data processing, normalization, and enrichment, including with external data (weather, real estate market, regional patterns).

Within the context of BIM (Building Information Modeling), the practice of coordinating different disciplines (architecture, structure, installations) in a digital model already generates a large volume of information. BIM is not only visual—it carries physical, chronological, and cost properties (3D, 4D, 5D) that can feed predictive models.

With quality data, the path to AI and GenAI becomes viable.

2. Machine Learning and Generative AI Models

Once the data is available and well-structured, it's possible to apply different artificial intelligence models. These models can predict future scenarios, automate complex processes, or even creatively generate new solutions with GenAI.

With structured data, developers can apply different techniques:

a) Predictive models

• Schedule forecasting and delays : identifying which stages are at risk of delay, based on historical productivity data, weather, and logistical bottlenecks.

• Cost estimation (dynamic budgeting) : predicting variations in inputs, adjustments to deadlines, and budgetary risk.

• Market demand/interest forecasting : within launches, use search behavior data, demographic profiles, and regional trends to project which product types will be in highest demand.

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b) Generative AI / GenAI

Generative AI goes further: it "creates" new artifacts or simulations based on what it has learned from the data. Some applications:

• Automatic generation of floor plan or layout variants : offering multiple optimized configurations for sunlight, ventilation, views, or cost, in minutes. This transforms the design process.

• 4D construction scenario simulations : combining schedule + BIM model to project construction progress, anticipate logistical conflicts, equipment relocation, and interferences.

• Automated document and report production : preparation of memos, specifications, status reports, cost statements, and explanatory justifications for non-technical users. Generative AI can draft or create an initial version of these documents – reducing hours of manual work.

  
  

c) Computer vision and image analysis

• On the construction site, cameras and drones can capture images of the work's progress. Computer vision models identify inconsistencies (execution deviations, non-conformities, safety issues) by comparing what has been built with the ideal design.

• It is also possible to recognize the presence of emerging structural flaws (cracks, deformations) and trigger immediate inspections.

d) Hybrid models / “human-in-the-loop”

Given the critical and regulated nature of construction projects, many systems use hybrid models: AI provides alerts or suggestions, but a technical team validates them before action is taken. This improves reliability and avoids "black boxes" that are viewed with suspicion in engineering.

3. Positive impacts: reduced costs, increased productivity, satisfaction.

By combining well-processed data with robust AI models, the practical effects begin to appear. It's not just about technology, but about real gains for developers and their clients.

• Reducing rework and construction corrections : errors detected early through simulation or remote viewing prevent high rework costs.

• Better budget predictability and more reliable timelines : predictive models help reduce the risk of budget overruns.

• Increased productivity on the construction site : less time lost due to poor coordination, improved logistics for machinery and supplies.

• Improved customer experience : transparency in project progress, predictable delivery dates, and proactive maintenance.

• Faster and more informed decision-making : managers and directors can simulate "what happens if this step is delayed?" scenarios and adjust plans accordingly.

• Optimization of technical workflows : designers generate variants with AI; operational teams receive more precise instructions; suppliers plan deliveries in advance.

  
  

Additionally, there is a strategic bias: while many real estate developers operate with tight margins and market risk, those who adopt AI and data will be better positioned to compete with lower operating costs and greater accuracy.

Next, we'll look at some case studies from this segment, including two from BlueMetrics.

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Real-world examples of AI in the AEC/construction market

Skanska Case: Security monitoring with AI

Skanska, a global real estate development company, adopted an intelligent AI-powered monitoring solution to reduce theft, vandalism, and unauthorized access at large construction sites. For example, in the I-405 highway improvement project in the US, AI tools for active surveillance, video analytics, and remote response were implemented to make operations more proactive.

Furthermore, Skanska developed Safety Sidekick in-house, a chatbot based on generative AI that assists workers and managers on the construction site: it answers safety queries, provides recommendations on site conditions, and helps with planning.

And in collaboration with the Smartvid.io platform , Skanska uses computer vision to identify safety risks: photos and videos captured on the construction site are automatically tagged with potential violations (SmartTags) to alert the team.

These mechanisms allow for anticipating incidents, reducing human risk, and accelerating responses, thus improving safety and operational efficiency.

Špansko Case: Automating Construction Documentation with AI

The European real estate developer Špansko adopted an AI tool to automate the daily production of construction reports from photos, images, and visual data captured on-site. The goal was to reduce the time inspectors spend drafting manual reports after returning to the office.

The application allows the inspector to record photos and observations directly on the construction site. It then automatically generates a formatted report, already including images, notes, and a professional layout. AI assists in the visual integration (photos) with text comments, aligning everything into a consistent document.

Observed results:

• Significant time reduction: the report was generated in minutes on-site, instead of hours in the office.

• Lower probability of human error and inconsistency in the document, because the layout and formatting are standardized.

• Greater efficiency in communication between construction teams and central offices, with rapid feedback and integrated visual data.

These cases show that recognized companies in the AEC sector are already applying AI, computer vision, predictive models, and BIM integration to improve safety, construction control, and coordination.

Next, we'll look at two BlueMetrics case studies in the real estate sector.

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Case Study: BlueMetrics 1: Revenue forecasts for a real estate management company in the US.

Context

A real estate asset management firm in the United States, part of a large corporate group, operated in a highly competitive and volatile market. To maintain a strategic advantage and predictability, it was crucial to accurately estimate future revenues. Prior to this project, the firm had already contracted BlueMetrics for data and analytics projects: building a structured database, information governance, and systems integration. This track record facilitated smoother collaboration in the subsequent phase.

Problem

Although the company had ample historical data and business intelligence tools, estimating future revenues for each property still relied heavily on the individual experience of managers. The process was manual, time-consuming, and subject to personal biases. With hundreds of properties distributed across different regions, multiple variables (occupancy, vacancy, seasonality, rent increases, operating expenses) needed to be considered. Simulating scenarios, comparing assets, and anticipating impacts was an operational and strategic bottleneck—especially in a rapidly changing economic market.

Solution

BlueMetrics has developed a hybrid solution that combines generative AI and machine learning models for automated revenue forecasting. The main interface is a conversational agent in natural language, where managers can type questions such as "What will the revenue of asset X be in the next 6 months?" or "If vacancy is 10%, what is the impact on portfolio Y?", without the need for complex dashboards or dependence on a technical team.

Behind this agent, predictive models based on time series use the company's historical data (revenue, vacancy, costs) to generate estimates. The solution integrates with the manager's legacy systems, queries internal databases, and runs the models automatically. The architecture was built with scalable AWS services (such as Amazon Bedrock and Amazon SageMaker), ensuring security, performance, and easy scalability.

Key differentiators:

• The combination of generative AI for interaction and ML for integrated prediction.

• A conversational interface that democratizes its use by non-technical managers.

• Native integration with existing systems and architecture using tools from the AWS ecosystem.

  
  

Results

• The forecasts now have a margin of error of less than 5%, increasing the reliability of the plans.

• The subjectivity of estimates based on intuition has been eliminated, bringing more consistency to the process.

• Managers gained autonomy: simulations and consultations can be done directly through chat, without technical support.

• Financial planning has become more agile, with immediate and well-founded answers.

• The organization's analytical maturity has increased, and AI has ceased to be a one-off element, becoming an ongoing part of operations.

With this solution, a Brazilian developer/manager in this segment could apply the same adapted model for projecting unit sales, condominium revenue, or return on investment for residential and commercial projects.

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Case Study BlueMetrics 2: Brazilian real estate developer optimizes credit granting with AI and reduces delinquency by 46%.

Context

One of the largest real estate developers in Brazil, in addition to selling residential units, also operates as a financing company, offering direct credit to buyers. This practice increases margins and competitiveness, but also significantly increases financial risk. With the growth in sales and the number of applications, the need to revise the credit granting model, which until then was manual and poorly standardized, became evident.

Problem

The credit analysis process relied on criteria that varied among analysts, often influenced by subjective factors. This led to inconsistent decisions, conflicts between sales and finance departments, delays in service, and difficulty scaling operations. In a real estate market with high default rates, the company needed an objective, agile, and reliable process to assess risks.

Solution

BlueMetrics developed a machine learning model trained on the developer's own historical data. The model considers variables such as income, marital status, and number of children to estimate the likelihood of default among new applicants.

Integrated with an artificial intelligence agent, the system automatically generates a real-time risk score for each request, enabling fast, standardized, and data-driven decisions. The process remains under human supervision, but now with robust support from reliable predictions.

The solution architecture was built using scalable AWS technologies, such as Amazon SageMaker, ensuring performance, flexibility, and security to support operational growth.

Results

• A potential 46% reduction in default rates, according to simulations using historical data.

• 92% accuracy in classifying good payers.

• Standardization of credit analysis, eliminating subjectivity and internal conflicts.

• Greater agility in service, with automated decisions in real time.

• Additional insights for marketing to target campaigns to customers with a higher propensity to pay.

  
  

With this solution, the developer achieved greater efficiency, scalability, and financial security, demonstrating how the combination of data, machine learning, and AWS technologies can transform critical processes in the real estate sector.

Conclusion: AI and data are the foundation of the new era for real estate developers.

The incorporation of data, artificial intelligence, and machine learning is no longer a distant trend in the construction, architecture, and engineering sector: it is a reality that redefines how developers plan, build, finance, and interact with their clients. From the use of generative models in design to revenue forecasting and credit analysis based on predictive algorithms, companies that adopt these technologies are gaining clear competitive advantages: lower costs, greater efficiency, faster decisions, and more satisfied customers.

With over 200 AI and data projects delivered to more than 90 clients in Brazil, the United States, and Latin America, BlueMetrics has accumulated the experience and expertise necessary to transform initiatives into concrete results. Our strong partnership with AWS ensures that each solution is built on scalable, secure, and adaptable foundations.

More than just implementing technology, our mission is to understand your company's specific context and develop tailored solutions, whether it's to reduce delinquency, increase revenue predictability, improve customer experience, or optimize operations on the construction site.

The future of the construction industry is already underway, and those who get ahead on this journey will have a competitive edge. Let's talk about it?

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