Case Study: AI Lease Abstraction for Real Estate Firms

The Challenge

Real estate firms manage thousands of leases, each with unique clauses and financial details. Normal lease abstraction methods are manual, time-consuming, and prone to errors, posing several challenges:

• Inconsistent Data Extraction: Leases in various formats lead to errors in capturing key information like dates, terms, and obligations.

• Time-Intensive Process: Manually abstracting lease data consumes hours, slowing decision-making and market responsiveness.

• Lack of Standardization: Without a consistent approach, data discrepancies arise across teams, complicating portfolio analysis.

• Difficulty tracking changes: Lease documents often change significantly through many iterations. Missing small provisions can be extremely costly. 

• Compliance and Risk Management: Ensuring compliance with regulations and managing risks across leases is difficult when relying on manual methods.

The Solution

Trellis offers an AI solution that streamlines the lease abstraction process. By automating the extraction and standardization of lease data, Trellis provides companies with accurate, timely, and actionable insights into their portfolios.

Before Trellis:

In a traditional workflow, lease abstraction involves multiple manual steps. Customers send a packet of documents, and manual reviews and checks are performed across various documents. This process is both time-consuming and prone to errors, leading to potential inconsistencies and missed information. Afterward, the system of record is updated manually—a slow and labor-intensive process.

After Trellis:

With Trellis, as soon as the packet of documents is received, the AI engine automatically flags any issues and extracts all relevant information into the system of record. This eliminates the need for manual reviews and speeds up the entire process, ensuring that data is accurate and consistent across the board. The system not only updates the records but also ensures that compliance-related clauses are flagged, helping the firm manage risk proactively.

How Trellis Works

Trellis utilizes advanced AI and machine learning algorithms to streamline the lease abstraction process. Here’s how it works:

1. Data Ingestion: Leases are uploaded to the Trellis platform automatically, where they are ingested and processed.

2. AI-Powered Extraction: The platform’s AI algorithms extract key data points from each lease, including critical dates, financial obligations, and specific clauses.

3. Data Standardization: Trellis standardizes the extracted data, ensuring consistency across all leases, regardless of their original format or language.

4. Custom Features: Companies can define custom extraction rules and data points specific to their needs, allowing for tailored insights and reporting.

5. Reporting and Analytics: The structured data is then used to generate real-time dashboards and reports, providing actionable insights into the lease portfolio.

With Trellis, Firms Have:

Faster Turnaround Times

• Accelerated Abstraction: What once took hours to complete can now be done in minutes, allowing firms to respond quickly to market opportunities and make informed decisions faster.

• Increased Productivity: With less time spent on manual data extraction, teams can focus on higher-value tasks, such as strategic planning and customer service, improving overall productivity.

Improved Accuracy

• Reduced Errors: By automating the extraction process, Trellis minimizes errors, ensuring that critical data is captured accurately. This reduction in human error leads to more reliable data for decision-making.

• Consistent Data: The standardized approach to lease abstraction ensures that all data is consistently categorized, making it easier to analyze and compare leases across a portfolio.

Enhanced Risk Mitigation

• Proactive Compliance: Trellis flags critical clauses and obligations, reducing the risk of non-compliance and legal issues. This helps firms avoid penalties and ensures they remain compliant with all regulatory requirements.

• Informed Risk Management: Firms can identify and address potential risks in their lease portfolios before they become problematic, enabling more proactive risk management.

Improved Decision-Making

• Data-Driven Insights: Trellis provides real-time access to lease data, enabling firms to make informed decisions about their portfolios. This leads to better resource allocation and strategic planning.

• Strategic Planning: With a clear view of lease terms, obligations, and financials, firms can better plan for renewals, expansions, and other strategic initiatives, ensuring they stay ahead of the competition.

Conclusion

Trellis has transformed the lease abstraction process for companies managing large real estate portfolios. By automating the extraction, standardization, and reporting of lease data, Trellis provides companies with the tools they need to manage their leases more effectively and efficiently. The result is increased accuracy, faster turnaround times, cost savings, and better risk management, making Trellis an essential component in modern lease management strategies.