Breaking New Ground in Real Estate Analytics

Leveraging LLMs to simplify code and derive accurate insights, faster.

Meet Our Client

A Provider of Cutting-Edge Real Estate Analytics

A leading appraiser in the U.S. real estate analytics sector, our client leverages a national database of 23 million property records to enhance business efficiency and decision-making. By addressing edge cases that traditional AI services often miss, they empower clients to confidently navigate complex market dynamics. Their diverse clientele includes financial institutions, institutional investors, REITs, insurance companies, family offices, law firms, accounting firms, and property owners.

Challenges: Moving Mountains of Data

The client’s product generates comprehensive property reports including the final value of the property by integrating data from disparate sources.

Manual efforts and costly data

First, data extraction from such diverse sources manually posed a huge challenge, as many of these sources needed to be purchased at a hefty cost, and consolidating such vast amounts of data was nearly impossible.

Wasted time and new errors

Second, the cognitive complexity defects identified by the SonarCube quality inspection tool created challenges for new developers, making it difficult for them to grasp the business logic and leading to additional time delays. This situation contributed to the introduction of new errors as well.

Our team leveraged AI models to sieve through massive amounts of information and produce accurate results in real time. The use of LLMs also enabled us to simplify code and eliminate errors.

Rajkumar Purushothaman,
Head – Activate Analytics

Solution We Offered

Leveraging LLMs for real-time answers

The Zuci team evaluated the current property report and discovered that using LLMs like OpenAI’s models and Gemini enables them to extract precise information about specific properties. By leveraging the Generative AI API, the system can produce accurate market figures and values in real time. This dynamic prompting capability allows for the creation of up-to-date summaries of market trends, ensuring that users have access to relevant insights for decision-making.

Simplifying code to improve quality

Our solution leverages code-focused Large Language Models (LLMs) to enhance code development in areas of cognitive complexity. When a method or function exhibits significant complexity, it is analyzed by the LLM, which refactors the code to generate a substantial portion of the required functionality. Developers can then review and modify this output, tailoring the solution to their specific needs.

Additionally, the simplified structure of the generated code—often with fewer nested loops—facilitates quicker onboarding for new developers, allowing them to grasp the logic and functionality more easily. By integrating code LLMs into the development workflow, it empowers developers to work more efficiently and create higher-quality code.