Lease abstraction collects and organizes the information real estate stakeholders need to effectively manage the leases in their portfolio and make informed location decisions. It saves them days, weeks, or months of searching and deciphering hundreds of pages of lease documents—because someone else has already done it for them. Traditionally, that responsibility has fallen to lease administrators.
But what if lease administrators could get the same result, faster? What if they could use their expertise to simply review and approve lease abstracts, instead of manually abstracting each lease themselves?
That’s the vision behind AI lease abstraction. Using a specialized Large Language Model (LLM), lease administrators can significantly decrease the time required to abstract leases.
Tango’s AI can abstract a lease clause in five seconds.
Tango has been incorporating AI capabilities into our IWMS software solutions for more than a decade. Our existing predictive analytics and machine learning infrastructure enabled us to be among the first to train a proprietary LLM for AI lease abstraction, and we’ve been refining it ever since.
In this article, we’ll answer your biggest questions about AI lease abstraction, including:
- How does AI lease abstraction work?
- Is AI lease abstraction reliable?
- What’s the best AI lease abstraction software?
How does AI lease abstraction work?
Lease abstraction isn’t simply a scavenger hunt for critical dates, rent details, clauses, and options. It takes serious lease expertise to distill pages of legal details into precise, actionable information, and lease administrators develop that expertise through hundreds or even thousands of repetitions—so how does AI do it?
If you fed your entire lease portfolio into a standard LLM like ChatGPT, Claude, or Gemini, they could correctly identify some of the right data for a lease abstract. But they won’t have the training or context to consistently and reliably turn key paragraphs or pages into a comprehensive, yet concise lease abstraction.
Generally, AI-powered lease abstraction tools use specialized LLMs that have been trained on a wealth of lease documents. They use text embeddings to convert the text of hundreds or thousands of leases into numerical vectors, which it uses to learn the relationships between words and predict the most likely relationships between words in future documents that it reads—meaning your leases.
In other words, you feed your lease documents into the LLM, and it uses its familiarity with lease documents much like a veteran lease administrator, recognizing context to decipher ambiguous terms or connect when different passages are communicating the same concept. Then it summarizes all the key details in a standardized lease abstraction format.
For most AI use cases, experts recommend pairing AI solutions with human oversight. And considering the stakes, AI lease abstraction is no exception. You want to increase speed without compromising accuracy.
Importantly, some AI lease abstraction tools may use your lease portfolio to improve their training data. This will never happen with Tango Lease, but be sure to check with other vendors if they don’t tell you upfront. A larger (or more specialized) pool of training data can expose an LLM to more unique situations and help improve its accuracy, but it also puts your data at risk—and there are other ways to increase AI lease abstraction reliability.
Is AI lease abstraction reliable?
In many ways, lease abstraction is an ideal use case for AI. LLMs don’t simply search documents. They map the relationships between words in a massive dataset, then draw from that foundation to understand new data. Much like a lease administrator draws on their experience with hundreds of previous leases when abstracting a new lease.
AI just does this much faster, and it “learns” lease language differently than humans. But does that make it less reliable than human experience and expertise?
Despite the obvious value, there are valid concerns with using AI for lease abstraction: lease abstracts cannot have errors, and you don’t want your lease data to be exposed. And that’s why most AI lease abstraction tool vendors recommend that human lease administrators review any outputs that come from AI lease abstraction. It still saves hours of work, but there may also be nuances of lease language that a human is better at interpreting.
But the best AI lease abstraction tools are always being refined. At Tango, human experts continually test the limitations of our LLM and manually correct lease abstraction errors so they won’t be replicated with your data. We’re constantly improving accuracy without compromising your security.
While concerns about AI’s accuracy can be addressed with human oversight, some AI lease abstraction apps or services present another issue: they will store your data to enhance their training models. Depending on the LLM they use, this can expose you to various risks outside your tolerance. The bottom line: if a vendor stores your data in any capacity, they inherently introduce risk into your infrastructure.
So if you want AI lease abstraction to be reliable, you need a reliable partner.
What’s the best AI lease abstraction software?
Tango Lease is the best AI lease abstraction tool because it builds on more than a decade of experience with artificial intelligence, uses a proprietary LLM that’s been trained on a curated set of thousands of lease documents, and leverages human expertise to improve accuracy. Our LLM is constantly being refined, but we never store your data or use it without your consent. You can turn months worth of work into days without sacrificing the quality of your lease abstracts.
Want to see our AI lease abstraction software in action?
