Mapline.ai

Accelerating Due Diligence for Real Estate with Generative AI

From Weeks to Minutes with GenAI-Powered MapLine.ai

The Partnership

BlueLabel worked with Mapline.ai to revolutionize the land development analysis process. BlueLabel created a product that automated a traditionally manual and complex task by combining cutting-edge generative AI technology with geospatial data and municipality regulations. This partnership enabled Mapline.ai to deliver efficient, precise, and actionable insights, significantly enhancing the decision-making process for civil engineers, urban planners, real estate developers, and urban planners.

Services

GenAI Opportunity Analysis

AI Product Strategy & Discovery

GenAI Application Development

UX Design & User Testing

Data Engineering and Pipeline Automation

Web App Development (FE/BE)

Outcomes
& Value Created

$2000

Can save an average of $2,000 per project by streamlining due diligence analysis and reducing consultant and labor costs.

60^%

Can reduce the number of site visits by 60%, focusing efforts on high-priority areas through aggregation of local geospatial data and GenAI analysis.

25^%

GenAI-driven analysis and geospatial data aggregation uncover 25% more potential risks compared to traditional manual methods.

Reduced due diligence time for land use development from days to minutes, enabling civil engineers to complete more projects in less time.

Allowed a single engineer to perform the work of multiple engineers, significantly lowering labor costs.

GenAI-driven insights uncovered potential risks and opportunities often missed in manual analysis of municipal regulations.

Real-time, AI-generated reports equip engineers with deeper, data-driven insights for better decision-making.

Microservices based architecture on AWS used in conjunction with a cloud hosted vector database ensures the system is able to work with large datasets of geospatial and municipal data.

Objective & Challenges

BlueLabel was tasked to develop a solution that simplifies the due diligence and planning phases in real estate development. Through extensive research and user interviews, we uncovered critical challenges such as the overwhelming amount of geospatial data, the complexity of navigating diverse municipality regulations, and the time-intensive nature of traditional analysis methods. Our objective was to create Mapline.ai, a tool that integrates these disparate data sources to provide comprehensive, actionable insights without the need for extensive on-site assessments, thereby reducing both time and costs for developers.

Key Challenges

Aggregating disparate geospatial data, including zoning maps, transportation plans, and environmental data, into a unified vector representation that can be processed by both LLMs and traditional algorithms.

Parsing local municipal zoning and development bylaws and mapping unified geospatial data units them to understand regulatory and compliance limits.

Developing a solution capable of functioning nationwide despite the fragmentation and lack of standardization in how development laws are represented across different municipalities.

VIsual Design

Built to Scale

To ensure MapLine continues to succeed, we have created a powerful set of AI-driven analytical tools. This adaptable and scalable system enables the Mapline.ai internal team to efficiently analyze new datasets and update zoning regulations. It also allows for seamless integration of new features and enhancements, providing actionable insights that significantly enhance the decision-making process for civil engineers, urban planners, real estate developers, and urban planners.

Tech Stack & Partners

How We Delivered Value

Our
Approach

GenAI Vision &
Opportunity Analysis

Identified areas of highest impact for generative AI within the real estate due diligence process.

Analyzed due diligence user journeys and proposed AI-driven solutions to improve property information accessibility, mitigate development risks, and accelerate and reduce the costs of the due diligence process.

Developed hypotheses on how AI could address identified pain points and opportunities.

Developed a comprehensive AI vision for Mapline.ai, including prioritized opportunities and use cases.

GenAI Product Strategy & Discovery

Conducted detailed interviews with real estate developers, engineers, and municipality workers to identify key pain points and validate initial hypotheses.

Designed key user flows in a Figma prototype to visualize and refine the user experience.

Conducted technical feasibility studies to evaluate the implementation challenges of proposed AI solutions, ensuring that they are practical and align with business objectives.

Created a strategic roadmap for the integration of AI capabilities into Mapline,ai, prioritizing features that address the most pressing user needs and market demands.

GenAI Application Development

Architected a Retrieval-Augmented Generation (RAG) based solution that leveraged OpenAI alongside a Milvus vector database for dynamic referencing of property data and related governing documents.

Created and deployed custom prompt chains with dynamic, property-specific data to deliver relevant and accurate property insights.

Trained a LLM for dynamic analysis of governing documents pertinent to the selected property.

Developed custom functionalities within ArcGIS to retrieve, process, and generate new geospatial vectors, along with location-specific reports and insights.

Conducted user testing to enhance product accuracy and uncover additional insights valuable to Mapline.ai’s target audiences.

“We are conducting due diligence for a 95-acre site, including a detailed sketch plan and numerous consultant meetings. With MapLine’s insights, we can streamline this process to reduce cost between $1,000 and $3,000, significantly enhancing efficiency and reducing costs.”

Civil Engineer user of Mapline.ai

Key Activities
& Takeaways

BlueLabel addressed the developers’ challenge of analyzing inconsistent and varied data sources by integrating AI into their workflow. We created Mapline.ai, a tool that utilizes Large Language Models (LLMs) to process ArcGIS geospatial data and Unified Development Ordinance (UDO) documents. Here’s how we achieved this:

AI Driven Analysis

We applied LLMs as a decision support system, automating the analysis of geospatial data and UDO documents. This enabled the generation of comprehensive insights and recommendations in minutes, a task that previously took weeks.

Expert Integration

Our team ensured seamless integration with ARCGIS by developing APIs using Express, facilitating smooth communication between the backend and frontend. This allowed for efficient handling of complex queries and quick delivery of AI-generated reports.

Real-Time Processing

We implemented a Retrieval-Augmented Generation (RAG) process with Langchain and OpenAI models to provide real-time AI responses for large datasets. This ensured accurate and timely insights, enhancing the decision-making process.

Scalability and Performance

By dockerizing services and optimizing indexing in Milvus, we ensured that the system could scale without compromising performance. This allowed for high concurrency and efficient handling of large datasets.