Skip to main content
Real Estate

Real Estate Lease Abstraction Agent

Extract 200+ lease variables from commercial leases in minutes.

Start a ConversationFree 30-min scoping call
Real Estate Lease Abstraction Agent
The Scenario

The problem
being solved

A CRE firm managing 50 properties re-abstracts leases annually and for every acquisition. Each lease: 40-100 pages with base rent escalations, CAM formulas, percentage rent, co-tenancy, exclusive use, options, and expense caps. 4-8 hours per lease for a trained abstractor.

Prophia and LeaseLens parse 200+ variables in minutes with 85-95% time reduction. MRI/Leverton handles institutional scale. The technology works — the challenge is complex provisions: "proportionate share of increases over the base year" has different implications depending on the base year definition 40 pages later.

For acquisitions, abstraction accuracy directly impacts underwriting. A missed co-tenancy clause or incorrect escalation schedule changes projected NOI.

The Solution

How this
agent works

The agent processes commercial leases extracting 200+ variables: rent (base, escalations, percentage, CPI), operating expenses (CAM structure, base year, caps, exclusions), options (renewal, expansion, termination), use restrictions (permitted, exclusive, co-tenancy), and dates (commencement, expiration, option exercise deadlines).

Handles cross-reference resolution: a rent escalation referencing "the CPI adjustment in Section 4.2(b)" is resolved by reading that section and extracting the actual mechanism. Defined terms resolved to their definitions throughout.

Output feeds into MRI, Yardi, or VTS. Financial provisions modeled into projected cash flows: base rent + escalations + percentage rent - abatements = projected gross rent by period. Confidence scores per field — high auto-populates, low flagged for verification.

How It's Built

Built on Anthropic Claude for clause-level reasoning, with LayoutLM handling document structure across scanned PDFs and native digital leases. FastAPI serves the extraction pipeline; Celery queues batch abstractions asynchronously so large portfolios don't block. We train extraction models on your existing abstracts during a 3-4 week setup, configuring field schemas per lease type before go-live.

Stack
PythonAnthropic ClaudeLayoutLMFastAPIPostgreSQLRedisCelery
Capabilities
  1. 01

    Deep Lease Parsing

    Extracts 200+ variables across rent, operating expenses, options, use restrictions, and critical dates. Resolves cross-references, defined terms, and amendment stacking that trip up simpler extractors.

  2. 02

    Cash Flow Modeling

    Models base rent, CPI and fixed escalations, percentage rent clauses, and free rent abatements into period-level projected cash flows. Outputs match the rent schedule format your analysts already use.

  3. 03

    Asset Management Integration

    Structured abstracts push directly into MRI, Yardi, and VTS via their APIs, auto-populating lease records without manual re-entry. Field mappings are configured per platform during setup.

  4. 04

    Confidence-Based Verification

    Every extracted field carries a confidence score. High-confidence fields auto-populate; ambiguous or conflicting provisions are flagged with the source text highlighted for attorney or analyst review.

Build this agent
for your workflow.

We custom-build each agent to fit your data, your rules, and your existing systems.

Start a Conversation

Free 30-min scoping call