Sienam Lulla
CEO Bryckel AI
Why Complex Leases Need AI and CRE Expert
Lease abstraction services and software: the winning combination for large portfolios
Lease abstraction services and software: the winning combination for large portfolios
Why Complex Leases In A Large Portfolio Need AI and CRE Expert
There's a lot of noise right now about AI transforming commercial real estate operations. And to be fair, some of it is warranted. AI has genuinely changed what's possible in document processing, data extraction, and workflow automation. But somewhere along the way, the conversation took a wrong turn — and now a growing number of real estate and legal teams are being sold a fantasy: that complex lease abstraction can be fully automated with generic AI tools or off-the-shelf real estate software.
It can't. And if you've ever managed a portfolio with leases that have gone through ten rounds of amendments, survived a corporate restructuring, and picked up a sublease rider along the way, you already know this in your bones.
This post is for the teams who are done being oversold. Let's talk about what actually makes complex lease abstraction hard, why generic solutions like Harvey and purpose-built but limited platforms like Orbital can't solve it at scale, and what a real answer looks like.
The Complexity Problem Nobody Wants to Talk About
When people imagine lease abstraction, they often picture a clean, single-document lease — a tenant, a landlord, a commencement date, a rent schedule, maybe an option or two. Run it through an AI model, out comes structured data. Simple enough.
But that's not the reality of most portfolios. The reality looks more like this: a master lease executed in 2009, followed by a first amendment in 2011 that added square footage, a second amendment in 2013 that relocated the tenant to a different floor in the same building, a third amendment in 2015 that assigned the lease to a successor entity after an acquisition, a sublease executed in 2017 for a portion of the premises, a fourth amendment in 2018 that modified the rent schedule in light of the sublease, and so on — through amendment ten, eleven, or twelve, each one layering new terms on top of, or sometimes in contradiction with, what came before.
This is not a document. It is an evolving legal narrative. And the operative terms of that lease — the ones that actually govern what you owe, what you're entitled to, and what your exposure is — are scattered across a decade of paperwork, some of which supersedes earlier provisions and some of which doesn't.
Generic AI tools like Harvey were not designed for this. Harvey is a powerful general-purpose legal AI, and it does impressive things in the right context. But it has no native understanding of how lease amendment chains work, no mechanism for reconciling conflicting provisions across documents, and no framework for determining which version of a rent schedule is actually operative after three amendments have touched it. Asking Harvey to abstract a complex commercial lease portfolio is a bit like asking the Harvey from Suits to handle your lease administration. Remember how that Harvey would waltz into a divorce hearing in the morning, close a billion-dollar M&A deal by lunch, and somehow never break a sweat or crack a file? Dazzling surface performance. Tremendous hair. But you wouldn't actually trust him with the granular, unglamorous, deeply technical work of reconciling amendment seven against amendment three on a relocated premises clause. The real Harvey AI operates on a similar energy — impressively confident across a vast range of legal content, but built for breadth, not for the specific, painstaking discipline of complex lease abstraction. It'll give you a breezy read of a document. It won't give you a trustworthy data set.
Real estate-specific platforms like Orbital have moved the needle further, but they run into a different set of walls. These platforms are built for the cleaner end of the lease abstraction market — straightforward leases, moderate complexity, reasonable document organization. When you start feeding them a lease with ten amendments that includes the twists and turns of expansion rights, relocation clauses, assignment and assumption agreements, sublease structures, and co-tenancy provisions, the orchestration breaks down. These tools are not architected to understand that amendment seven's rent abatement provision only applies to the portion of the premises that wasn't relocated in amendment three. That kind of reasoning requires something more than pattern matching on individual documents. It requires understanding the full legal and transactional history of the lease as a connected whole.
The technical orchestration challenge alone is enough to disqualify generic and lightweight solutions from complex lease abstraction work. But it's only the first problem.
The Human-in-the-Loop Problem: AI Is Never Perfect, and DIY Doesn't Scale
Here is a truth that every honest AI software company will acknowledge: AI makes mistakes. It misreads provisions. It misses nuance. It occasionally hallucinates data that isn't there or skips data that is. In a low-stakes context, that's manageable. In lease abstraction, where a missed rent escalation clause or an incorrectly abstracted option expiration date can translate directly into financial loss or legal exposure, it is not.
This means that any responsible lease abstraction workflow — regardless of how good the AI component is — requires human review. There is no getting around it. The human in the loop is not optional overhead. It is the quality control layer that makes the output trustworthy.
The problem is what happens when you try to build that human review layer yourself.
If you're using a generic AI tool , the assumption baked into the product is that your team is the reviewer. Your paralegals, your lease administrators, your real estate counsel — they're the ones catching the AI's errors, reconciling conflicting data, flagging ambiguous provisions for interpretation, and making judgment calls on edge cases. Which sounds manageable until you think about what that actually means at scale.
A portfolio of five hundred leases, each with an average of four amendments, means two thousand documents that need to go through AI processing and human review. If your team is doing the reviewing, that's not a technology solution — that's your team doing lease abstraction with an AI assistant. You've automated part of the work, but you've kept all of the cognitive load and none of the scalability. Your team's bandwidth is still the bottleneck. Your team's expertise is still the limiting factor. And when your team is stretched thin — during a merger, an acquisition, a portfolio expansion — the whole thing falls apart.
Scalable AI lease abstraction isn't just about the AI. It's about who owns the human review.
If you have to do it yourself, it doesn't scale. That's not a criticism of your team — it's a structural reality. True scalability means that as your portfolio grows, the human review burden does not fall on your internal team. It means the expertise is embedded in the service, not the software.
What Expert Review Actually Looks Like — And Why It's an Ongoing Service, Not a One-Time Event
The right model for complex lease abstraction isn't a software subscription. It's a service that happens to be powered by AI — and the distinction matters enormously.
Here's what expert review actually involves when it's done right.
First, document organization and deduplication. Most portfolios have a document problem before they have a data problem. Leases filed in multiple versions. Amendments uploaded twice under different names. Exhibits separated from their parent leases. Executed copies and draft copies mixed together. Before any abstraction work can produce reliable output, someone has to get the document layer right. That means identifying every document in a lease file, understanding how they relate to each other, removing duplicates, and establishing a clean, authoritative document set.
This is not something AI does reliably on its own. It requires human judgment about which version of a document is the operative one, whether two documents with similar names are actually duplicates or distinct amendments, and how to handle gaps where documents appear to be missing.
Second, reconciliation and normalization across the amendment chain. Once the document layer is clean, the real abstraction work begins — and for complex leases, that means reading the full amendment history as an integrated whole, not as a series of independent documents. Expert reviewers understand which provisions have been superseded, which survive through all amendments, and which apply only to specific portions of the premises or specific periods of the term. They reconcile conflicting data points, flag provisions that are ambiguous or require legal interpretation, and ensure that the abstracted data reflects the actual operative terms of the lease as it stands today.
We normalize, reconcile, and clean the data layer underneath every lease — so every lease in your portfolio has the right documents, labeled correctly, connected properly, and ready to be searched and trusted. That's not a feature of a software product. That's a service delivered by people who know what they're doing.
Third — and this is the part that gets overlooked — it's ongoing. Lease abstraction is not a migration project that ends on a go-live date. Leases change. Amendments get executed. Options get exercised. Assignments happen. Every time a lease document changes, the abstracted data needs to change with it. Every time a new amendment comes in, someone needs to read it in the context of the full amendment history, determine what it supersedes and what it adds, and update the data accordingly.
If your abstraction solution is a one-time data conversion project, you will have accurate data on day one and increasingly stale data every day after. The portfolio you need to manage today is not the portfolio you'll need to manage in two years. The solution has to be able to grow with you — adding leases, processing new amendments, handling acquisitions that bring in new portfolios with their own document chaos.
That's what scalable AI lease abstraction providers actually provide: not just technology, but an ongoing service relationship that keeps your data current as your portfolio evolves.
The Real Cost of Getting This Wrong
It's worth being direct about what's at stake when lease abstraction is done poorly or left to tools that aren't up to the task.
In due diligence and legal work, the cost of bad lease data isn't an operational headache — it's a deal risk. A missed liability, a misread restriction, an amendment that wasn't reconciled before close. By the time anyone catches it, the damage is done.
These aren't hypothetical risks. They are the predictable outcomes of treating complex lease abstraction as a software problem that can be solved with a subscription and self-service tooling.
The portfolios where these failures happen most often are exactly the portfolios where generic AI tools are most likely to be deployed — large, complex, heavily amended, with long document histories and significant financial exposure attached to every data point.
What to Actually Look For
If you're evaluating lease abstraction services and software for a complex portfolio, the questions to ask are not about the AI model or the interface. They are:
How does this solution handle a lease with ten or more amendments, including relocations, assignments, and subleases? Who owns the human review — your team or theirs? What happens when new amendments come in after the initial abstraction is complete? How do they handle document deduplication and normalization before abstraction begins? Can they show you examples of how conflicting provisions across an amendment chain are reconciled?
If the answers lean heavily on self-service, your team's review, or a one-time data migration model, that's not a scalable solution for complex lease abstraction. It's a tool that will work for simple leases and create problems for everything else.
The Bottom Line
The promise of AI in lease abstraction is real. AI can dramatically accelerate the extraction of standard provisions, reduce manual data entry, and flag issues faster than any human reviewer working alone. But AI is not a replacement for the expertise required to navigate complex leases, and it is not a replacement for the ongoing service relationship required to keep a real portfolio's data accurate over time.
Generic solutions like Harvey aren't built for lease complexity. Real estate platforms like Orbital handle the simpler end of the market better than most, but they hit walls when the amendment chains get long and the transactional history gets complicated. And any solution that puts the human review burden back on your team is not a scalable answer — it's just a more expensive version of doing it yourself.
Complex leases need expert review. They need ongoing service. They need a data layer that's been normalized, reconciled, and kept current. And they need a provider that understands the difference between abstracting a lease and managing a lease file — because for the portfolios that matter most, those are not the same thing.
Looking for scalable AI lease abstraction providers that can handle complex portfolios with expert human review? The right solution goes beyond software — it's a service built for the leases that generic tools can't handle.
Why Complex Leases In A Large Portfolio Need AI and CRE Expert
There's a lot of noise right now about AI transforming commercial real estate operations. And to be fair, some of it is warranted. AI has genuinely changed what's possible in document processing, data extraction, and workflow automation. But somewhere along the way, the conversation took a wrong turn — and now a growing number of real estate and legal teams are being sold a fantasy: that complex lease abstraction can be fully automated with generic AI tools or off-the-shelf real estate software.
It can't. And if you've ever managed a portfolio with leases that have gone through ten rounds of amendments, survived a corporate restructuring, and picked up a sublease rider along the way, you already know this in your bones.
This post is for the teams who are done being oversold. Let's talk about what actually makes complex lease abstraction hard, why generic solutions like Harvey and purpose-built but limited platforms like Orbital can't solve it at scale, and what a real answer looks like.
The Complexity Problem Nobody Wants to Talk About
When people imagine lease abstraction, they often picture a clean, single-document lease — a tenant, a landlord, a commencement date, a rent schedule, maybe an option or two. Run it through an AI model, out comes structured data. Simple enough.
But that's not the reality of most portfolios. The reality looks more like this: a master lease executed in 2009, followed by a first amendment in 2011 that added square footage, a second amendment in 2013 that relocated the tenant to a different floor in the same building, a third amendment in 2015 that assigned the lease to a successor entity after an acquisition, a sublease executed in 2017 for a portion of the premises, a fourth amendment in 2018 that modified the rent schedule in light of the sublease, and so on — through amendment ten, eleven, or twelve, each one layering new terms on top of, or sometimes in contradiction with, what came before.
This is not a document. It is an evolving legal narrative. And the operative terms of that lease — the ones that actually govern what you owe, what you're entitled to, and what your exposure is — are scattered across a decade of paperwork, some of which supersedes earlier provisions and some of which doesn't.
Generic AI tools like Harvey were not designed for this. Harvey is a powerful general-purpose legal AI, and it does impressive things in the right context. But it has no native understanding of how lease amendment chains work, no mechanism for reconciling conflicting provisions across documents, and no framework for determining which version of a rent schedule is actually operative after three amendments have touched it. Asking Harvey to abstract a complex commercial lease portfolio is a bit like asking the Harvey from Suits to handle your lease administration. Remember how that Harvey would waltz into a divorce hearing in the morning, close a billion-dollar M&A deal by lunch, and somehow never break a sweat or crack a file? Dazzling surface performance. Tremendous hair. But you wouldn't actually trust him with the granular, unglamorous, deeply technical work of reconciling amendment seven against amendment three on a relocated premises clause. The real Harvey AI operates on a similar energy — impressively confident across a vast range of legal content, but built for breadth, not for the specific, painstaking discipline of complex lease abstraction. It'll give you a breezy read of a document. It won't give you a trustworthy data set.
Real estate-specific platforms like Orbital have moved the needle further, but they run into a different set of walls. These platforms are built for the cleaner end of the lease abstraction market — straightforward leases, moderate complexity, reasonable document organization. When you start feeding them a lease with ten amendments that includes the twists and turns of expansion rights, relocation clauses, assignment and assumption agreements, sublease structures, and co-tenancy provisions, the orchestration breaks down. These tools are not architected to understand that amendment seven's rent abatement provision only applies to the portion of the premises that wasn't relocated in amendment three. That kind of reasoning requires something more than pattern matching on individual documents. It requires understanding the full legal and transactional history of the lease as a connected whole.
The technical orchestration challenge alone is enough to disqualify generic and lightweight solutions from complex lease abstraction work. But it's only the first problem.
The Human-in-the-Loop Problem: AI Is Never Perfect, and DIY Doesn't Scale
Here is a truth that every honest AI software company will acknowledge: AI makes mistakes. It misreads provisions. It misses nuance. It occasionally hallucinates data that isn't there or skips data that is. In a low-stakes context, that's manageable. In lease abstraction, where a missed rent escalation clause or an incorrectly abstracted option expiration date can translate directly into financial loss or legal exposure, it is not.
This means that any responsible lease abstraction workflow — regardless of how good the AI component is — requires human review. There is no getting around it. The human in the loop is not optional overhead. It is the quality control layer that makes the output trustworthy.
The problem is what happens when you try to build that human review layer yourself.
If you're using a generic AI tool , the assumption baked into the product is that your team is the reviewer. Your paralegals, your lease administrators, your real estate counsel — they're the ones catching the AI's errors, reconciling conflicting data, flagging ambiguous provisions for interpretation, and making judgment calls on edge cases. Which sounds manageable until you think about what that actually means at scale.
A portfolio of five hundred leases, each with an average of four amendments, means two thousand documents that need to go through AI processing and human review. If your team is doing the reviewing, that's not a technology solution — that's your team doing lease abstraction with an AI assistant. You've automated part of the work, but you've kept all of the cognitive load and none of the scalability. Your team's bandwidth is still the bottleneck. Your team's expertise is still the limiting factor. And when your team is stretched thin — during a merger, an acquisition, a portfolio expansion — the whole thing falls apart.
Scalable AI lease abstraction isn't just about the AI. It's about who owns the human review.
If you have to do it yourself, it doesn't scale. That's not a criticism of your team — it's a structural reality. True scalability means that as your portfolio grows, the human review burden does not fall on your internal team. It means the expertise is embedded in the service, not the software.
What Expert Review Actually Looks Like — And Why It's an Ongoing Service, Not a One-Time Event
The right model for complex lease abstraction isn't a software subscription. It's a service that happens to be powered by AI — and the distinction matters enormously.
Here's what expert review actually involves when it's done right.
First, document organization and deduplication. Most portfolios have a document problem before they have a data problem. Leases filed in multiple versions. Amendments uploaded twice under different names. Exhibits separated from their parent leases. Executed copies and draft copies mixed together. Before any abstraction work can produce reliable output, someone has to get the document layer right. That means identifying every document in a lease file, understanding how they relate to each other, removing duplicates, and establishing a clean, authoritative document set.
This is not something AI does reliably on its own. It requires human judgment about which version of a document is the operative one, whether two documents with similar names are actually duplicates or distinct amendments, and how to handle gaps where documents appear to be missing.
Second, reconciliation and normalization across the amendment chain. Once the document layer is clean, the real abstraction work begins — and for complex leases, that means reading the full amendment history as an integrated whole, not as a series of independent documents. Expert reviewers understand which provisions have been superseded, which survive through all amendments, and which apply only to specific portions of the premises or specific periods of the term. They reconcile conflicting data points, flag provisions that are ambiguous or require legal interpretation, and ensure that the abstracted data reflects the actual operative terms of the lease as it stands today.
We normalize, reconcile, and clean the data layer underneath every lease — so every lease in your portfolio has the right documents, labeled correctly, connected properly, and ready to be searched and trusted. That's not a feature of a software product. That's a service delivered by people who know what they're doing.
Third — and this is the part that gets overlooked — it's ongoing. Lease abstraction is not a migration project that ends on a go-live date. Leases change. Amendments get executed. Options get exercised. Assignments happen. Every time a lease document changes, the abstracted data needs to change with it. Every time a new amendment comes in, someone needs to read it in the context of the full amendment history, determine what it supersedes and what it adds, and update the data accordingly.
If your abstraction solution is a one-time data conversion project, you will have accurate data on day one and increasingly stale data every day after. The portfolio you need to manage today is not the portfolio you'll need to manage in two years. The solution has to be able to grow with you — adding leases, processing new amendments, handling acquisitions that bring in new portfolios with their own document chaos.
That's what scalable AI lease abstraction providers actually provide: not just technology, but an ongoing service relationship that keeps your data current as your portfolio evolves.
The Real Cost of Getting This Wrong
It's worth being direct about what's at stake when lease abstraction is done poorly or left to tools that aren't up to the task.
In due diligence and legal work, the cost of bad lease data isn't an operational headache — it's a deal risk. A missed liability, a misread restriction, an amendment that wasn't reconciled before close. By the time anyone catches it, the damage is done.
These aren't hypothetical risks. They are the predictable outcomes of treating complex lease abstraction as a software problem that can be solved with a subscription and self-service tooling.
The portfolios where these failures happen most often are exactly the portfolios where generic AI tools are most likely to be deployed — large, complex, heavily amended, with long document histories and significant financial exposure attached to every data point.
What to Actually Look For
If you're evaluating lease abstraction services and software for a complex portfolio, the questions to ask are not about the AI model or the interface. They are:
How does this solution handle a lease with ten or more amendments, including relocations, assignments, and subleases? Who owns the human review — your team or theirs? What happens when new amendments come in after the initial abstraction is complete? How do they handle document deduplication and normalization before abstraction begins? Can they show you examples of how conflicting provisions across an amendment chain are reconciled?
If the answers lean heavily on self-service, your team's review, or a one-time data migration model, that's not a scalable solution for complex lease abstraction. It's a tool that will work for simple leases and create problems for everything else.
The Bottom Line
The promise of AI in lease abstraction is real. AI can dramatically accelerate the extraction of standard provisions, reduce manual data entry, and flag issues faster than any human reviewer working alone. But AI is not a replacement for the expertise required to navigate complex leases, and it is not a replacement for the ongoing service relationship required to keep a real portfolio's data accurate over time.
Generic solutions like Harvey aren't built for lease complexity. Real estate platforms like Orbital handle the simpler end of the market better than most, but they hit walls when the amendment chains get long and the transactional history gets complicated. And any solution that puts the human review burden back on your team is not a scalable answer — it's just a more expensive version of doing it yourself.
Complex leases need expert review. They need ongoing service. They need a data layer that's been normalized, reconciled, and kept current. And they need a provider that understands the difference between abstracting a lease and managing a lease file — because for the portfolios that matter most, those are not the same thing.
Looking for scalable AI lease abstraction providers that can handle complex portfolios with expert human review? The right solution goes beyond software — it's a service built for the leases that generic tools can't handle.
Why Complex Leases In A Large Portfolio Need AI and CRE Expert
There's a lot of noise right now about AI transforming commercial real estate operations. And to be fair, some of it is warranted. AI has genuinely changed what's possible in document processing, data extraction, and workflow automation. But somewhere along the way, the conversation took a wrong turn — and now a growing number of real estate and legal teams are being sold a fantasy: that complex lease abstraction can be fully automated with generic AI tools or off-the-shelf real estate software.
It can't. And if you've ever managed a portfolio with leases that have gone through ten rounds of amendments, survived a corporate restructuring, and picked up a sublease rider along the way, you already know this in your bones.
This post is for the teams who are done being oversold. Let's talk about what actually makes complex lease abstraction hard, why generic solutions like Harvey and purpose-built but limited platforms like Orbital can't solve it at scale, and what a real answer looks like.
The Complexity Problem Nobody Wants to Talk About
When people imagine lease abstraction, they often picture a clean, single-document lease — a tenant, a landlord, a commencement date, a rent schedule, maybe an option or two. Run it through an AI model, out comes structured data. Simple enough.
But that's not the reality of most portfolios. The reality looks more like this: a master lease executed in 2009, followed by a first amendment in 2011 that added square footage, a second amendment in 2013 that relocated the tenant to a different floor in the same building, a third amendment in 2015 that assigned the lease to a successor entity after an acquisition, a sublease executed in 2017 for a portion of the premises, a fourth amendment in 2018 that modified the rent schedule in light of the sublease, and so on — through amendment ten, eleven, or twelve, each one layering new terms on top of, or sometimes in contradiction with, what came before.
This is not a document. It is an evolving legal narrative. And the operative terms of that lease — the ones that actually govern what you owe, what you're entitled to, and what your exposure is — are scattered across a decade of paperwork, some of which supersedes earlier provisions and some of which doesn't.
Generic AI tools like Harvey were not designed for this. Harvey is a powerful general-purpose legal AI, and it does impressive things in the right context. But it has no native understanding of how lease amendment chains work, no mechanism for reconciling conflicting provisions across documents, and no framework for determining which version of a rent schedule is actually operative after three amendments have touched it. Asking Harvey to abstract a complex commercial lease portfolio is a bit like asking the Harvey from Suits to handle your lease administration. Remember how that Harvey would waltz into a divorce hearing in the morning, close a billion-dollar M&A deal by lunch, and somehow never break a sweat or crack a file? Dazzling surface performance. Tremendous hair. But you wouldn't actually trust him with the granular, unglamorous, deeply technical work of reconciling amendment seven against amendment three on a relocated premises clause. The real Harvey AI operates on a similar energy — impressively confident across a vast range of legal content, but built for breadth, not for the specific, painstaking discipline of complex lease abstraction. It'll give you a breezy read of a document. It won't give you a trustworthy data set.
Real estate-specific platforms like Orbital have moved the needle further, but they run into a different set of walls. These platforms are built for the cleaner end of the lease abstraction market — straightforward leases, moderate complexity, reasonable document organization. When you start feeding them a lease with ten amendments that includes the twists and turns of expansion rights, relocation clauses, assignment and assumption agreements, sublease structures, and co-tenancy provisions, the orchestration breaks down. These tools are not architected to understand that amendment seven's rent abatement provision only applies to the portion of the premises that wasn't relocated in amendment three. That kind of reasoning requires something more than pattern matching on individual documents. It requires understanding the full legal and transactional history of the lease as a connected whole.
The technical orchestration challenge alone is enough to disqualify generic and lightweight solutions from complex lease abstraction work. But it's only the first problem.
The Human-in-the-Loop Problem: AI Is Never Perfect, and DIY Doesn't Scale
Here is a truth that every honest AI software company will acknowledge: AI makes mistakes. It misreads provisions. It misses nuance. It occasionally hallucinates data that isn't there or skips data that is. In a low-stakes context, that's manageable. In lease abstraction, where a missed rent escalation clause or an incorrectly abstracted option expiration date can translate directly into financial loss or legal exposure, it is not.
This means that any responsible lease abstraction workflow — regardless of how good the AI component is — requires human review. There is no getting around it. The human in the loop is not optional overhead. It is the quality control layer that makes the output trustworthy.
The problem is what happens when you try to build that human review layer yourself.
If you're using a generic AI tool , the assumption baked into the product is that your team is the reviewer. Your paralegals, your lease administrators, your real estate counsel — they're the ones catching the AI's errors, reconciling conflicting data, flagging ambiguous provisions for interpretation, and making judgment calls on edge cases. Which sounds manageable until you think about what that actually means at scale.
A portfolio of five hundred leases, each with an average of four amendments, means two thousand documents that need to go through AI processing and human review. If your team is doing the reviewing, that's not a technology solution — that's your team doing lease abstraction with an AI assistant. You've automated part of the work, but you've kept all of the cognitive load and none of the scalability. Your team's bandwidth is still the bottleneck. Your team's expertise is still the limiting factor. And when your team is stretched thin — during a merger, an acquisition, a portfolio expansion — the whole thing falls apart.
Scalable AI lease abstraction isn't just about the AI. It's about who owns the human review.
If you have to do it yourself, it doesn't scale. That's not a criticism of your team — it's a structural reality. True scalability means that as your portfolio grows, the human review burden does not fall on your internal team. It means the expertise is embedded in the service, not the software.
What Expert Review Actually Looks Like — And Why It's an Ongoing Service, Not a One-Time Event
The right model for complex lease abstraction isn't a software subscription. It's a service that happens to be powered by AI — and the distinction matters enormously.
Here's what expert review actually involves when it's done right.
First, document organization and deduplication. Most portfolios have a document problem before they have a data problem. Leases filed in multiple versions. Amendments uploaded twice under different names. Exhibits separated from their parent leases. Executed copies and draft copies mixed together. Before any abstraction work can produce reliable output, someone has to get the document layer right. That means identifying every document in a lease file, understanding how they relate to each other, removing duplicates, and establishing a clean, authoritative document set.
This is not something AI does reliably on its own. It requires human judgment about which version of a document is the operative one, whether two documents with similar names are actually duplicates or distinct amendments, and how to handle gaps where documents appear to be missing.
Second, reconciliation and normalization across the amendment chain. Once the document layer is clean, the real abstraction work begins — and for complex leases, that means reading the full amendment history as an integrated whole, not as a series of independent documents. Expert reviewers understand which provisions have been superseded, which survive through all amendments, and which apply only to specific portions of the premises or specific periods of the term. They reconcile conflicting data points, flag provisions that are ambiguous or require legal interpretation, and ensure that the abstracted data reflects the actual operative terms of the lease as it stands today.
We normalize, reconcile, and clean the data layer underneath every lease — so every lease in your portfolio has the right documents, labeled correctly, connected properly, and ready to be searched and trusted. That's not a feature of a software product. That's a service delivered by people who know what they're doing.
Third — and this is the part that gets overlooked — it's ongoing. Lease abstraction is not a migration project that ends on a go-live date. Leases change. Amendments get executed. Options get exercised. Assignments happen. Every time a lease document changes, the abstracted data needs to change with it. Every time a new amendment comes in, someone needs to read it in the context of the full amendment history, determine what it supersedes and what it adds, and update the data accordingly.
If your abstraction solution is a one-time data conversion project, you will have accurate data on day one and increasingly stale data every day after. The portfolio you need to manage today is not the portfolio you'll need to manage in two years. The solution has to be able to grow with you — adding leases, processing new amendments, handling acquisitions that bring in new portfolios with their own document chaos.
That's what scalable AI lease abstraction providers actually provide: not just technology, but an ongoing service relationship that keeps your data current as your portfolio evolves.
The Real Cost of Getting This Wrong
It's worth being direct about what's at stake when lease abstraction is done poorly or left to tools that aren't up to the task.
In due diligence and legal work, the cost of bad lease data isn't an operational headache — it's a deal risk. A missed liability, a misread restriction, an amendment that wasn't reconciled before close. By the time anyone catches it, the damage is done.
These aren't hypothetical risks. They are the predictable outcomes of treating complex lease abstraction as a software problem that can be solved with a subscription and self-service tooling.
The portfolios where these failures happen most often are exactly the portfolios where generic AI tools are most likely to be deployed — large, complex, heavily amended, with long document histories and significant financial exposure attached to every data point.
What to Actually Look For
If you're evaluating lease abstraction services and software for a complex portfolio, the questions to ask are not about the AI model or the interface. They are:
How does this solution handle a lease with ten or more amendments, including relocations, assignments, and subleases? Who owns the human review — your team or theirs? What happens when new amendments come in after the initial abstraction is complete? How do they handle document deduplication and normalization before abstraction begins? Can they show you examples of how conflicting provisions across an amendment chain are reconciled?
If the answers lean heavily on self-service, your team's review, or a one-time data migration model, that's not a scalable solution for complex lease abstraction. It's a tool that will work for simple leases and create problems for everything else.
The Bottom Line
The promise of AI in lease abstraction is real. AI can dramatically accelerate the extraction of standard provisions, reduce manual data entry, and flag issues faster than any human reviewer working alone. But AI is not a replacement for the expertise required to navigate complex leases, and it is not a replacement for the ongoing service relationship required to keep a real portfolio's data accurate over time.
Generic solutions like Harvey aren't built for lease complexity. Real estate platforms like Orbital handle the simpler end of the market better than most, but they hit walls when the amendment chains get long and the transactional history gets complicated. And any solution that puts the human review burden back on your team is not a scalable answer — it's just a more expensive version of doing it yourself.
Complex leases need expert review. They need ongoing service. They need a data layer that's been normalized, reconciled, and kept current. And they need a provider that understands the difference between abstracting a lease and managing a lease file — because for the portfolios that matter most, those are not the same thing.
Looking for scalable AI lease abstraction providers that can handle complex portfolios with expert human review? The right solution goes beyond software — it's a service built for the leases that generic tools can't handle.
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In-house Legal
Move at the pace your business requires while ensuring every decision is informed and defensible. Handle more work with less resources. Reduce your external counsel spend, invest in codifying expertise across deals for future efficiency.
Real Estate Development Team
Fast growing tenants in industries such as restaurant, retail, fitness, banking, grocery, logistics and coworking. Never sign an unfavorable lease. Speed up lease approvals, streamline negotiations, and manage multiple locations with confidence.
Real Estate Investors & Asset Managers
Never miss an acquisition opportunity. Maximize NOI & monetization opportunities. Respond to investors, leasing team, brokers, outside counsel and leadership in fraction of time.
Real Estate Advisors
For anyone who loves deals, not documents. Get your head around complex leases and portfolios, and advise clients about issues from day one. Deliver actionable insights and strategic advice that accelerates deals and strengthens client relationships.
Law Firms
Spot issues before they become problems, watch your clients’ back and protect their business. Meet tight client deadlines. Handle work at scale and stay competitive.
Learn more about Bryckel AI.
Trusted by hundreds of leading real estate businesses.
Book a Demo
In-house Legal
Move at the pace your business requires while ensuring every decision is informed and defensible. Handle more work with less resources. Reduce your external counsel spend, invest in codifying expertise across deals for future efficiency.
Real Estate Development Team
Fast growing tenants in industries such as restaurant, retail, fitness, banking, grocery, logistics and coworking. Never sign an unfavorable lease. Speed up lease approvals, streamline negotiations, and manage multiple locations with confidence.
Real Estate Investors & Asset Managers
Never miss an acquisition opportunity. Maximize NOI & monetization opportunities. Respond to investors, leasing team, brokers, outside counsel and leadership in fraction of time.
Real Estate Advisors
For anyone who loves deals, not documents. Get your head around complex leases and portfolios, and advise clients about issues from day one. Deliver actionable insights and strategic advice that accelerates deals and strengthens client relationships.
Law Firms
Spot issues before they become problems, watch your clients’ back and protect their business. Meet tight client deadlines. Handle work at scale and stay competitive.
Learn more about Bryckel AI.
Trusted by hundreds of leading real estate businesses.
Book a Demo
In-house Legal
Move at the pace your business requires while ensuring every decision is informed and defensible. Handle more work with less resources. Reduce your external counsel spend, invest in codifying expertise across deals for future efficiency.
Real Estate Development Team
Fast growing tenants in industries such as restaurant, retail, fitness, banking, grocery, logistics and coworking. Never sign an unfavorable lease. Speed up lease approvals, streamline negotiations, and manage multiple locations with confidence.
Real Estate Investors & Asset Managers
Never miss an acquisition opportunity. Maximize NOI & monetization opportunities. Respond to investors, leasing team, brokers, outside counsel and leadership in fraction of time.
Real Estate Advisors
For anyone who loves deals, not documents. Get your head around complex leases and portfolios, and advise clients about issues from day one. Deliver actionable insights and strategic advice that accelerates deals and strengthens client relationships.
Law Firms
Spot issues before they become problems, watch your clients’ back and protect their business. Meet tight client deadlines. Handle work at scale and stay competitive.