AI in Real Estate
Leases, diligence and portfolio obligations, searchable in one pass.
Real estate runs on documents whose terms determine cash flow, and most owners cannot answer a portfolio-level question about those terms without re-reading them. That gap is worth more than any of the consumer-facing AI features the industry has spent its attention on.
Book a free consultationWhat makes this hard
- Portfolio obligation questions require re-reading the documents, so they mostly go unasked
- Missed option and notice dates cost more than the entire administrative budget that was cut to save money
- Diligence timelines are compressed and document volumes are not
- Enquiry response speed decides which broker gets the deal, and most responses are slow
Where AI earns its place
Lease and Contract Abstraction
Extract operative terms across a portfolio with the amendment chain reconciled, so the reported term is the current one rather than the original.
Portfolio terms visible in one passDiligence Document Review
First-pass review of the data room against your own diligence checklist, flagging gaps and non-standard terms for the deal team.
Diligence findings in days, not weeksCritical Date Management
Options, renewals, notice windows and escalations extracted into a structure that drives alerting rather than a spreadsheet somebody maintains.
No missed notice windowsEnquiry and Tenant Response
Answer property, availability and tenant questions immediately from your own current data.
Instant response at any hourFigures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.
The rules that shape the build
These are the constraints that decide the architecture, usually before anyone has picked a model. This is general information about the regulatory landscape, not legal advice on your obligations.
Fair Housing Act
Any model touching tenant screening, marketing targeting or application handling carries disparate impact exposure, and proxy variables such as geography reproduce protected characteristics without naming them.
State agency and disclosure duties
A broker's duties to a client do not transfer to a tool. Anything an automated system tells a prospect is a representation by the brokerage.
Lender and covenant reporting
Where extracted terms feed covenant compliance reporting, the extraction needs an audit trail that survives a lender review.
Amendments are where the money is lost
The most common failure in real estate document AI is reading the base lease and ignoring the amendment chain, which produces confident answers about terms that were superseded years ago. Senteras builds amendment reconciliation before anything else, and reports the operative term with a citation to the document that set it. It is unglamorous and it is the difference between a useful system and a liability.
Where this applies
The same core systems, with the differences that matter in each setting.
- Commercial real estate owners
- Lease abstraction and covenant tracking across a portfolio; the payback is measured in missed dates avoided.
- Brokerages
- Enquiry response speed and pitch material assembly from comparable transactions.
- Property management
- Tenant enquiry handling and work order triage at volume.
- Real estate investment and funds
- Diligence document review under compressed timelines.
- Title and escrow
- Document extraction and exception clearing against the commitment.
Common questions
Will it read the amendments or just the base lease?
The amendment chain is reconciled and the operative term is what gets reported, with a citation to the document that set it. Reading only the base lease is the most common failure in lease AI and it produces confident answers about superseded terms.
Can we use AI in tenant screening?
Only with real care. Fair Housing disparate impact applies, and geography and behavioral variables proxy for protected characteristics without naming them. We would want a fair housing specializt involved before building anything in that path.
How accurate is abstraction on poor scans?
Low-confidence fields are flagged rather than filled. On a genuinely illegible document the honest output is a flag, and a system that returns a plausible value there is worse than one that returns nothing.
Can it answer portfolio-level questions?
That is the main point. Once terms are extracted with citations, obligation and exposure questions can be asked across the whole portfolio instead of per document, which is the thing most owners currently cannot do at all.
How we build it
Local & On-Prem LLM Deployment
The most powerful AI models, running entirely on your hardware.
Custom AI Agents & Automation
AI that doesn't just answer questions. It gets things done.
Model Fine-Tuning & Integration
Models that speak your industry's language, trained on your data.
Lease Abstraction
Every extracted term linked back to the page it came from.
Document Processing
Extraction, classification and validation across formats you do not control.
Contract Review
Flags deviations from your playbook, not from a generic standard.
Start with a conversation, not a proposal
Thirty minutes. We will tell you what we would change first, and whether you need us at all.
Book a callThe firm behind the firm