How a US real estate asset manager began predicting revenue more accurately using AI.
- Marcelo Firpo
- 4 days ago
- 5 min read
Automated revenue forecasting with Machine Learning Accuracy and impartiality with generative AI for data access Faster and more informed decisions can be made with a margin of error of less than 5%.
AI-generated summary:
To make its financial planning more agile and accurate, a US real estate fund manager adopted a solution developed by BlueMetrics that combines generative AI and machine learning. Previously dependent on manual and subjective analyses, the company began forecasting revenues with a margin of error of less than 5%, directly through a conversational interface. This eliminated operational bottlenecks, accelerated strategic decisions, and incorporated artificial intelligence as an ally in asset management.
Overview
In a dynamic market like the American real estate market, the ability to accurately forecast revenue is essential for making strategic decisions quickly.
It was precisely this objective that led a US real estate fund manager, part of one of the country's largest business groups, to adopt artificial intelligence in its financial planning process, with surprising results.
BlueMetrics had already developed several data engineering and analytics projects for this client, building and ensuring a solid foundation for governance and information quality.
This history of partnership was fundamental in enabling the agile development of the new solution, allowing the predictive models to be trained with structured, reliable data aligned with the business context.
Market context:
High volatility and competitiveness in the US real estate market.
The need for more accurate and reliable financial forecasts.
Reliance on manual and subjective analyses in revenue planning.
Demand for solutions that combine data, automation, and agile decision-making.
Problem: manual and poorly standardized forecasts
Despite having business intelligence tools and a large volume of historical data, the company still relied on the individual experience of its managers to estimate the future revenue of each asset. The process was manual, time-consuming, and vulnerable to subjectivity.
With dozens of properties spread across different regions and multiple variables at play, predicting results in a practical and reliable way was a constant challenge, as well as a clear bottleneck for operational efficiency.
Main challenges:
Operational limitations:
Financial forecasts are made manually and without standardization.
Reliance on individual managers' experience for revenue estimates.
The simulation process is lengthy and requires support from technical teams.
Business limitations:
Subjectivity in the analyses compromises the accuracy of the planning.
Difficulty in quickly anticipating results in a dynamic market.
Limited autonomy for managers to simulate scenarios without technical support.
Technological limitations:
Lack of integration between BI tools and predictive models.
Lack of a simple interface for querying predictions in natural language.
The solution: Generative AI and Machine Learning for automated predictions.
BlueMetrics developed a complete solution that combines generative AI for interaction and machine learning for prediction. Through an intuitive conversational interface, managers were able to interact directly with the company's financial data using natural language, without relying on complex dashboards or specialized technical support.
Now, questions like “What will fund X’s revenue be in the next 6 months?” , “ What was the average revenue for the last few quarters by region?” or “What is the projected impact of a 10% vacancy rate on properties in portfolio Y? ” can be asked directly via chat. The AI agent understands the request, interprets the context, and automatically triggers predictive models based on time series to generate clear, accurate, and actionable answers.
This approach democratized access to data and advanced analytics, allowing professionals from different areas, even without technical knowledge in data science, to make faster and more informed decisions. As a result, the team gained more autonomy, financial planning became more agile and reliable, and the organization reduced its dependence on manual processes and spreadsheets.
The solution's architecture was built using scalable AWS technologies, such as Amazon Bedrock and Amazon SageMaker, ensuring performance, security, and native integration with the company's existing systems and data. This enabled rapid adoption and continuous use of the tool as part of the manager's strategic daily routine.
Main components:
Conversational interface based on natural language for data querying.
Predictive model trained with historical revenue time series.
AI agent that integrates the predictive model with user interaction.
Integration with the company's existing database.
Technological differentiators:
Combining Generative AI with Machine Learning for automated predictions.
Using scalable AWS technologies (Amazon Bedrock and SageMaker)
Integrated architecture with high availability and native scalability.
Simplified user experience, no technical knowledge required.
Immediate benefits:
Revenue forecasts with a margin of error of less than 5%.
Greater autonomy for managers in decision-making.
Reducing reliance on manual and subjective analyses.
Faster, more reliable, and more accessible financial planning.
Increasing the organization's analytical maturity.
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Results:
With the new solution, the asset manager began obtaining forecasts with a margin of error of less than 5%, eliminating the subjectivity of the analyses and allowing for faster and more informed decisions.
Revenue estimates, previously based primarily on the intuition and experience of managers, are now grounded in statistical models trained with the company's own historical data, which has brought more confidence to the planning processes.
The ability to simulate scenarios directly via chat, without spreadsheets, manual cross-referencing, or the involvement of technical teams, significantly increased managers' autonomy and accelerated financial planning. Through the conversational interface, objective questions about the real estate asset portfolio began to be answered instantly and in context, democratizing access to analytical intelligence.
The impact was directly on the organization's analytical maturity. AI ceased to be merely a support tool and began to occupy a strategic role in day-to-day management, guiding everything from short-term tactical decisions to scenario analyses for setting goals and allocating resources. With this solution, the company gained greater predictability, agility, and precision to operate in a highly competitive market, such as real estate assets.
Operational efficiency:
Automated revenue forecasting with a margin of error of less than 5%.
Reducing reliance on manual analysis and complex spreadsheets.
More agile financial planning, with immediate answers via a conversational interface.
Increased team autonomy in strategic decision-making.
Technological advancement and integration:
Combining generative AI and machine learning in a single integrated solution.
Use of predictive models based on time series, with on-demand execution.
Native integration with the manager's existing database.
Scalable architecture with AWS technologies
Technologies used
The solution was designed using several AWS technologies, including:
AWS Services
Sagemaker
Bedrock
S3
DynamoDB
MemoryDB
Languages, Libraries, and Frameworks
Python
Javascript
Node
React
Conclusion:
This case demonstrates how the combination of generative AI and machine learning can transform data into decisions with precision and speed. By automating revenue forecasting, the company not only increased its operational efficiency but also took an important step towards intelligent asset management in the real estate market.
A key factor in the solution's success was the asset manager's well-structured database, built with the support of BlueMetrics. This data maturity enabled seamless integration between predictive models and the generative AI interface, ensuring an agile and reliable experience from the very first tests.
As Gabriel Casara, CGO of BlueMetrics, points out: “Projects like this only gain scale and generate real value when there is a well-balanced data structure behind them. Our expertise in data engineering is a differentiator that guarantees not only speed in delivery, but also technical responsibility in building the foundations of any GenAI or Machine Learning application. Or, as in this case, precisely in the combination of both technologies.”
More than a technological solution, the project symbolizes a strategic evolution: by placing artificial intelligence at the center of the decision-making process, the company strengthened its competitiveness in one of the world's most dynamic markets, with faster, safer, and data-driven decisions.
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About BlueMetrics
BlueMetrics was founded in 2016 and has already delivered over 200 successful projects in the areas of Data & Analytics, GenAI, and Machine Learning for more than 90 companies in the United States, Brazil, Argentina, Colombia, and Mexico. It has its own methodology and a multidisciplinary team focused on delivering solutions to real-world business challenges.
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