Streamlining Lease Abstraction with AI

Photo by Mari Helin on Unsplash

In the fast-paced realm of real estate and commercial property management, staying ahead of the curve is essential. One of the most critical yet time-consuming tasks in this domain is lease abstraction — the process of distilling complex lease agreements into concise and structured data points. Traditional lease abstraction methods involve manual labor, often consuming countless hours and leading to potential errors.

In this tutorial, we’ll delve into how to automate lease abstraction using a custom-trained AI model in conjunction with a Large Language Model (LLM). We’ll unravel the intricacies of transforming lease abstraction from a labor-intensive chore into an efficient, accurate, and streamlined process. Whether you’re a real estate professional, property manager, or tech enthusiast, this tutorial will empower you with insights into harnessing AI’s potential to reshape the landscape of lease management.

Throughout this journey, we will cover the following areas:

1. Custom AI Training: Discover the nuances of training a custom AI model tailored specifically for lease abstraction. Learn how to effectively label a training dataset and train a custom AI model.
2. Leveraging Large Language Models (LLMs): Delve into the capabilities of LLMs and comprehend how they complement custom AI models, elevating the accuracy and depth of lease abstraction.
3. Creating Custom Workflows: Combine our AI models and LLMs into one unified workflow using Kudra. Experience the transformation of arduous manual abstraction into a seamless automated process.

Whether you’re aiming to optimize your property management practices or keen to explore the intersections of technology and real estate, this tutorial serves as a valuable starting point to learn how to automate lease abstraction through advanced AI integration. Let’s get started!

Custom Model Training

To train our model on lease comprehension, we need to provide it with exmaples of labeled leases to learn from. Fortunately, there is a publicy annotated dataset that we can use to train our model. The entities that we are interested to extract from the leases are:

LEASED_SPACE
LESSOR
LESSEE
CLAUSE_NUMBER
CLAUSE_TITLE…