Real Capital Solutions, a private equity commercial real estate investment manager with $2.8 billion in assets under management across the US, Mexico and Canada, faced a familiar but painful problem: manual lease review was slow, inconsistent, and expensive. Outsourcing lease abstraction cost the firm between $375 and $450 per lease, and outputs varied wildly in quality. After evaluating more than a dozen products, this customer implemented a focused lease abstraction workflow powered by Kolena AI and transformed how acquisitions, legal, finance, and asset management teams access lease data.
The problem: high volume, high cost, inconsistent results
A recent acquisition—a mall with 150 tenants—made the problem impossible to ignore. The acquisitions team needed fast, accurate economic data from leases and amendments to underwrite the deal. Outsourced abstracts were expensive and often unusable: some reports were overly verbose, others returned unreadable spreadsheets. Internal reviews could take two hours or more per lease.
This scenario is common where lease abstraction is treated as a one-off task instead of an integrated process. Document complexity (multiple amendments, embedded rent tables, varying formats) makes many tools stumble. Real Capital Solutions had already trialed a dozen-plus solutions without consistent accuracy, particularly when handling multiple amendments and complex rent tables.
Kolena AI allows us to be more in a decision making stance instead of spending half our life finding data. “
Why a purpose-built approach matters for lease abstraction
Two technical hurdles consistently tripped up previous tools:
Amendments and versioning: leases often include many amendments across years. The critical requirement is to return the current, effective term across all documents rather than isolated answers from individual files.
Rent tables and economics: rent tables are notoriously tough to parse. Tools that collapse text and attempt to recreate tables produce errors in headers, missed amounts, or merged columns. Accurate lease abstraction requires reading tables as tables and clearly delineating multiple rent schedules.
Kolena succeeded where others struggled by reliably consolidating multiple documents and reading rent tables natively. That meant accurate economics for underwriting and a consistent output format for downstream workflows.
Deployment and onboarding: fast, lightweight, and self-service
Speed of adoption was a key factor. From contract to a working environment took roughly three days. The team could create agents—templates that run specific lease abstraction tasks—in minutes, duplicate them for loan documents or fee agreements, and refine prompts using built-in suggestion helpers.
Integration into existing systems was intentionally simple. Using the SharePoint and Microsoft Teams connector, the firm set up an output folder where processed documents land automatically. That allowed paralegals and property accountants to self-service new projects without engineering support.
I can create an operating agreement proof of concept in a 10 minute intro meeting and show them the output."
Real results: accuracy, speed, and ROI
The most immediate and measurable win came in time savings. Internal review time dropped from an average of two hours to roughly 17 minutes—a reduction of about 85%. That dramatic improvement turned lease abstraction into a scalable activity rather than a bottleneck.
Practical ROI calculation used by the firm:
Compare outsourced cost per lease (for example, $450) against internal time saved (17 minutes instead of two hours).
Multiply time savings across acquisition volume and recurring portfolio activities (renewals, extensions, notices).
Include soft benefits: faster deal screening, fewer errors, and reduced dependency on external vendors.
Because Kolena handled multiple amendments correctly and parsed rent tables reliably, the output could be fed into acquisitions models and forecasting systems without extensive manual correction. That amplified throughput for underwriting and portfolio management.
How the lease abstraction workflow expanded across the organization
What began as an acquisitions use case quickly proved useful across departments. Examples include:
Fee agreements for asset management and billing.
Commercial loan documents for DSCR testing and debt forecasting.
Tax and notice processing where a handful of data fields from varied notices are required by the tax team.
Rent roll reconciliation across multiple property managers.
The common thread: any document-heavy, repetitive task that required extracting specific data fields benefited from a robust lease abstraction agent.
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Operational lessons for firms adopting lease abstraction
The customer emphasized several practical lessons for teams planning to adopt lease abstraction and document AI:
Prioritize data quality and governance. AI cannot fix bad or fragmented data. Clean, consistent source documents and well-defined access controls are necessary.
Map the process before buying technology. Define how leases are used across teams and identify the exact tasks AI should perform. Focus on high-frequency, high-impact steps first.
Quantify time and cost. Track how long tasks currently take and compute ROI on a per-event basis (monthly or quarterly cadence works well).
Choose tools that handle domain nuance. For lease abstraction, prioritize vendors that correctly interpret amendments and treat tables as structured data.
Enable non-technical users. Integrations and self-service capabilities reduce burden on IT and speed adoption.
Vision: AI as a productivity multiplier, not a replacement
The strategic view positions AI as a way to increase throughput and decision-making capacity. With better connectivity between accounting systems and APIs from platforms like MRI or Yardi, AI can sit on top of clean data to accelerate underwriting, renewals, and portfolio analytics.
The goal is explicit: use lease abstraction to let people spend more time on judgment and less time on data gathering. By automating redundant extraction tasks, teams can underwrite more deals, respond faster to opportunities, and use human expertise where it matters most.
We’ve cut processing time with other tools down from 2 hours... down to 17 minutes per internal review.”
Quick checklist before piloting lease abstraction
Identify high-frequency document flows (acquisitions, renewals, tax notices).
Run a timing study to capture current effort per task.
Clean sample data and collect multi-document amendment chains for testing.
Test table parsing and amendment consolidation as top acceptance criteria.
Set success metrics (time reduction, accuracy threshold, cost per document).
With those pieces in place, lease abstraction moves from a one-off cost center into a repeatable capability that supports scaling acquisition pipelines and portfolio operations.
Conclusion
Real Capital Solutions demonstrates how targeted lease abstraction, when combined with fast onboarding, thoughtful integration, and a focus on data quality, delivers measurable savings and operational leverage. The right approach treats lease abstraction as a process to design first and a technology to implement second. When both are aligned, AI becomes a true productivity multiplier across acquisitions, accounting, tax, and asset management.
