What problem does it solve?
Every commercial lease, amendment and letter of intent has to be read before its terms can be used: the dates that trigger a renewal or a break option, the rent and how it escalates, the clauses buried in a rider or an exhibit. Unframe's case study on Cushman & Wakefield reports that abstracting a single lease could take anywhere from six hours to three days, depending on the document's complexity, and that none of the vendors or internal approaches the firm evaluated combined the accuracy and the speed it needed at scale.
Leases are also rarely standardized and can run to 100 pages or more, Unframe's case study notes, which makes consistent, accurate extraction harder to achieve at scale.
How does it work?
- Ingest the document. The lease, amendment or letter of intent arrives as a PDF or scan of any length, in any of the organization's operating languages.
- Extract the standard fields. The AI reads the document and produces a first pass abstract against a defined schema: parties, term dates, rent, escalations, options, renewal notices and similar fields.
- Flag what does not fit the schema. Non standard clauses, unusual riders and anything the model is not confident about are flagged rather than guessed.
- A person validates the exceptions. Lease administration or legal staff review the flagged clauses and the model's confidence, not every field on every lease.
- The data flows downstream. Confirmed fields post into the lease administration or portfolio management system, so accounting, reporting and renewal tracking work from the same structured record.
- Answer questions from the corpus. The same extracted data and source documents let brokers and lease administrators ask questions about a specific lease or compare draft letters of intent to each other.
- Audience
- Employee facing
- Autonomy
- Supervised agent
- Adoption
- Early adopters
- Channels
- Internal tools, API and system to system
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Productivity gain | Too few to pool | 25% to 90% | 2 | 1 organization, 1 vendor |
| Cycle time reduction | Too few to pool | 78% | 1 | 1 vendor |
Value drivers: Lower cost to serve, Employee productivity, Speed and cycle time.
Indicative value
A commercial real estate services firm abstracting 10,000 leases and LOIs a year
USD 400,000 to USD 9 million
Lease abstraction labor cost avoided per year
How this is calculated
Formula: documentsPerYear * hoursSavedPerDocument * costPerHour. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Leases and LOIs abstracted per year documentsPerYear, documents per year | 5,000 | 20,000 | Editorial assumption, replace with your own volume. |
| Analyst hours saved per document hoursSavedPerDocument, hours saved per document | 2 | 5 | Conservative against the two deployments on this page: Unframe's case study reports that abstraction used to take six hours to three days per lease before AI, and Cadastral reports JLL's brokerage teams now generate lease and LOI abstracts within seconds. |
| Fully loaded cost of a lease administration or legal analyst hour costPerHour, USD per hour | 40 | 90 | Editorial assumption, replace with your own fully loaded cost. |
What it leaves out: Gross labor cost avoided only. It leaves out the software cost, the time still needed to review flagged exceptions, and any recovered revenue from clauses the AI surfaces that a manual review would have missed, which neither deployment on this page reports as a company wide figure.
Who already uses it?
10 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
American Dream
United States · Real estate · 2026
American Dream, the Triple Five Group owned retail and entertainment complex in East Rutherford, New Jersey, used Cadastral's AI platform to automate economic summaries, cotenancy monitoring and termination rights analysis across its retail tenant lease library, moving from manual document review to instant, structured lease data. The deployment indexed and analyzed more than 45,000 pages of lease documentation and resolved over 290 lease related workflows.
No outcome disclosed.
Bradken
Australia · Manufacturing · 2026
Bradken, a manufacturing company with more than 20 locations worldwide and a lease portfolio of over 100 property leases under IFRS 16, upgraded to Trullion's AI powered platform to extract key accounting details, such as terms, dates and payments, from its commercial property lease contracts, replacing a slower, manual process. In Trullion's case study, group financial accountant Morgan Hoffman is quoted saying the AI function saves time going through a 70 or 80 page property lease contract to pluck out the details needed for accounting, and that the team reduced time spent on reporting by 25% while cutting software cost by 30%.
- Productivity gain: 25%
"While we end up saving 25% on time, there was also a 30% cost reduction with Trullion."
Claimed by: organization
Continental Realty Corporation
United States · Real estate · 2026
Continental Realty Corporation, an $8 billion owner of shopping center and multifamily assets, used Cadastral's AI platform to accelerate lease review, lease term abstraction and broader document analysis for acquisition due diligence and asset management, such as cotenancy, radius restriction and exclusion clauses. The deployment cut reliance on outside counsel and analyzed more than 2,500 documents across the portfolio.
- Cycle time reduction: 78%
"78% faster lease abstractions"
Claimed by: vendor - Cost savings: about USD 300,000, per year
"$300K annual savings in external legal and operational costs"
Claimed by: vendor
Cushman & Wakefield
United States · Real estate · 2026
Cushman & Wakefield evaluated multiple vendors and internal approaches for lease abstraction before selecting Unframe in a competitive tender. Unframe's case study reports that abstracting a single lease used to take six hours to three days; after deployment, the platform processes leases of any length, across multiple languages, and brokers can access lease insights in real time. What started as one use case has grown into more than 15 active Unframe projects across the business.
No outcome disclosed.
Derwent London
United Kingdom · Real estate · 2026
Derwent London, the largest London focused real estate investment trust in the UK with 77 buildings valued at £5.7 billion, began using MRI Contract Intelligence, which MRI markets as lease abstraction software powered by AI, to create a central database of lease documents and extract key lease data, alongside the MRI Strategic Planning software it had used for more than ten years as its lease database.
MRI and Derwent London then linked the two systems so users can jump from lease data in Strategic Planning to the source document in Contract Intelligence with a single click. Jennifer Whybrow, Derwent London's Head of Financial Planning and Analysis, said the combination "gives all teams quick and easy access to critical information and increases our ability for deeper analysis and data accuracy."
No outcome disclosed.
JLL (Jones Lang LaSalle)
United States · Real estate · 2026
JLL's leasing brokerage business replaced manual lease and letter of intent abstraction with Cadastral, an AI platform later acquired by the legal AI company Legora. Brokers use it to generate lease and LOI abstracts within seconds, answer questions about complex leases through an integrated chat feature, and compare draft LOIs to each other, instead of relying on manual review. Cadastral reports the deployment now produces thousands of abstracts a year and saves JLL hundreds of thousands of dollars annually, without giving an exact figure for either.
No outcome disclosed.
Taboola
United States · Technology and software · 2026
Taboola, a publicly traded advertising technology company with 18 offices worldwide, manages a lease accounting portfolio of about 25 leases across real estate, data centers and other assets under ASC 842, and moved from Excel to Trullion's AI powered platform to read and scan lease contracts, including leases inherited through acquisitions such as Connexity. In Trullion's case study, Taboola's VP finance, Yaron Kaneti, is quoted describing the prior Excel process as a "nightmare" of manual review. According to Kaneti, automating lease accounting with Trullion has saved the company three full working days per person per quarter, and the case study puts that at 36 full days of work a year across the three team members using the platform.
No outcome disclosed.
Claroty
United States · Technology and software · 2024
Claroty, a cybersecurity company that secures cyber physical systems for industrial, healthcare and public sector clients, managed a lease portfolio spanning Israel, the United States, the United Kingdom and Singapore, and moved from Excel spreadsheets to Trullion's AI powered platform to comply with the ASC 842 lease accounting standard. In Trullion's case study, the AI scans lease contracts to extract terms, payments, start dates and end dates, and the company's controller, Ron Kariv, describes it replacing manual review of each contract with a system that shows the specific payment schedule and contract terms directly.
- Productivity gain: 90%
"Claroty achieves 90% time savings in lease management with Trullion's AI-powered solution"
Claimed by: vendor
Urban Renaissance Group
United States · Real estate · 2021
Seattle based Urban Renaissance Group (URG), a commercial real estate firm with a 13.5 million square foot portfolio, used Prophia to abstract roughly 100 tenant leases across 600 documents and 5,000 pages within a short due diligence window for its acquisition of Lloyd Center, a 1.4 million square foot, three storey retail mall in Portland, Oregon, in fall 2021.
URG's Director of Investments, Jeff Vorhies, said the team was "still blown away by the speed in which we got back all these abstracts," which were delivered in under 10 business days and linked every extracted data point back to the source lease document. URG successfully acquired Lloyd Center and handed the Prophia dataset to its Asset and Property Management teams for ongoing use.
- Cycle time: up to 10 days
"Less than 10 business days after uploading the Lloyd Center documents to the Prophia Cloud, the entire URG team had access to 100+ electronic lease summaries and notes along with the reporting tools that enabled easy analysis, with every data point linked back to the source document."
Claimed by: vendor
Spear Street Capital
United States · Real estate · 2019
Spear Street Capital, an owner and operator of office properties in the United States, Canada and Europe established in 2001, partnered with Prophia in 2019 to replace static spreadsheets and monthly property manager reports with a centralized, AI abstracted lease data platform. In Prophia's customer story, a Spear Street portfolio manager described the prior process, saying "with a static system to manage leases, there's always a trust issue," and the firm now uses Prophia's critical dates notifications, lease summaries and portfolio reports across every asset in its portfolio, with extracted lease data dynamically tethered to the source documents for verification.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- A defined extraction schema of the fields the organization actually uses downstream
- A library of past leases, amendments and letters of intent in digital form
- A golden set of correctly abstracted leases to measure accuracy against
Systems to integrate
- Document management or electronic signature system where leases are stored
- Lease administration or portfolio management system, the destination for extracted data
- Optical character recognition for scanned or image based leases
Complexity: Medium
Extracting standard fields such as dates and base rent is well understood; the hard part is the long tail of non standard clauses, exhibits, amendments and, for a global portfolio, multiple languages and currencies, plus wiring confirmed output into the lease administration system instead of leaving it in a spreadsheet.
- 1
Define the schema before the pilot
Agree the exact fields lease administration, accounting and portfolio teams need, not every field a model can technically extract, and use that as the acceptance test.
- 2
Pilot on one lease type
Start with the most common, most standardized lease template in the portfolio before adding ground leases, sale and leaseback structures or multi tenant riders.
- 3
Route exceptions to a person by default
Treat every non standard clause and every low confidence extraction as a review item, not an accepted answer, until the exception rate on that clause type is proven low.
- 4
Validate against a golden set on every change
Keep a fixed set of leases with a confirmed correct abstract and rerun it whenever the model, prompt or schema changes, so accuracy regressions are caught before they reach production.
- 5
Wire the output into the system that uses it
Post confirmed fields into the lease administration or portfolio system automatically; extraction that stays in a standalone tool does not save the downstream re entry it is meant to remove.
Guardrails
- Every non standard or low confidence clause is routed to a person before the data is relied on
- Extracted values are shown next to the source clause so a reviewer can check them in seconds
- Version control tracks which document version, and which amendment, an abstract came from
KPIs to instrument
- Processing time per document, split by lease type and by whether it needed a review
- Share of extracted fields confirmed correct on a review sample
- Count of clauses, such as escalations or options, recovered that a prior manual process missed
Human in the loop
Lease administration or legal staff confirm every flagged exception before it reaches the lease administration system, and a sample of fields the model marked confident are spot checked on a schedule so silent accuracy drift is caught early.
Common failure modes
- Non standard clauses misread as standard
- A clause with unusual wording gets mapped to the wrong field or missed entirely because it does not match the schema; a human review pass and a growing library of confirmed exceptions catch this over time.
- Abstracted data nobody uses
- Extraction is treated as a one off clean up project rather than wired into the lease administration and portfolio systems, so the structured data goes stale as new leases and amendments arrive.
What are the risks and rules?
EU AI Act
Depends on design
Extraction alone is minimal risk: reading and structuring the terms of a commercial contract does not decide credit, employment, insurance, biometric identification or another use listed in Annex III, so it carries only the Article 4 AI literacy duty. The conversational assistant that lets employees ask questions about a lease adds Article 50(1): people who interact directly with it must be told they are dealing with an AI system, unless that is obvious from the context, as it usually is for an internal tool.
Controls to put in place
- A person confirms every non standard or low confidence clause before it is used in financial reporting or a renewal decision
- An audit trail records which user confirmed the abstract that downstream numbers rely on
Frequently asked questions
- How accurate is AI lease abstraction?
No deployment on this page discloses a checked accuracy percentage. Cadastral reports that JLL's brokerage teams now generate lease and LOI abstracts within seconds rather than manually; treat every extraction as a first pass that a person confirms before it feeds financial reporting or a renewal decision.
- What time does lease abstraction actually save?
Unframe's case study reports that abstracting a single lease used to take six hours to three days before deployment; afterward, it reports that Cushman & Wakefield's brokers can access lease insights in real time, without a stated company wide time or cost figure. Cadastral's case study on Continental Realty Corporation reports "78% faster lease abstractions" and "$300K annual savings in external legal and operational costs".
- Can it replace a lease administrator?
No. None of the case studies on this page describes the review step, so plan for one: route non standard clauses and low confidence extractions to a lease administrator or legal reviewer before the data feeds financial reporting or a renewal decision. Treat the AI as a copilot that produces a first pass, not a replacement for that review.
- Does it also handle letters of intent, not just signed leases?
Yes in the JLL deployment. Cadastral reports that JLL's brokers use the platform to generate LOI abstracts and to compare draft letters of intent to each other, alongside signed lease abstraction.
How to cite this page
Blits.ai AI Use Case Library, "AI lease abstraction for commercial real estate", last verified 28 September 2026, https://www.blits.ai/ai-use-cases/lease-abstraction. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 29 September 2026: Added deployments: Derwent London, Spear Street Capital, Urban Renaissance Group.
- 29 September 2026: Added deployments: Continental Realty Corporation, American Dream.
- 28 September 2026: First published