Asset and property teams do not need a chat summary of a lease. They need structured fields in the system of record: commencement, expiration, options, escalations, and who pays for what.
Commercial leases run dozens or hundreds of pages. Critical terms sit in definitions, exhibits, and amendments that amend earlier amendments.
Abstraction is slow and uneven. Two analysts can land different option dates from the same package. Portfolio tools then run on garbage-in data.
When a renewal or audit hits, teams re-read the PDF because nobody trusts the last extract—or the extract never made it into the system of record.
An agent pipeline that classifies lease packages, extracts a fixed field set into your schema, flags conflicts across base lease and amendments, and routes low-confidence fields to human abstractors.
Deterministic checks run after extraction: date order, currency consistency, square footage vs rent math where you define it, required clauses present for the asset type.
Abstractors keep judgment on ambiguous legal language. The system owns the re-keying, the checklist, and the link from each field back to page evidence.
Sense: ingest PDFs from DMS or deal rooms; split base lease, amendments, exhibits; order by effective date when detectable.
Reason: extract under schema; reconcile amendments against base terms; score confidence per field; block complete status when hard rules fail.
Rock: write to lease admin or Yardi/MRI-class systems via API or controlled load; open review tasks; retain page-level provenance.
Amendments that silently change rent without restating the full schedule. The pipeline must treat the package as a stack, not a single PDF.
Jurisdiction-specific clauses the model mislabels. Critical fields require human confirm or dual extract before portfolio reporting uses them.
No provenance. If you cannot click from "option exercise date" to the page, the extract will not survive the next audit.
It is a system that turns multi-page lease packages into structured fields your property systems can use, with checks and human review on uncertain terms. It is not a chatbot that paraphrases the lease.
It replaces most re-keying and first-pass checklist work. Ambiguous legal language, unusual deal structures, and portfolio-critical dates still need abstractor or counsel review. The goal is throughput and consistency, not unsupervised legal advice.
Documents are ordered and applied as a stack where dates allow. Conflicts surface as findings (e.g. two different option windows) rather than silently picking one.
Lease administration platforms, data warehouses, or custom ops tools—whatever is the system of record. Integration shape depends on your APIs and data model.
One asset class, a fixed field dictionary, real lease packages, page-level provenance, and a human queue for low-confidence fields. Expand field coverage after the first dictionary is trusted.
Engineering services
Lease abstraction and data extraction is the kind of workflow we build as an AI agent system: deterministic checks, human checkpoints, and an audit trail operators can trust. We start from your real process boundaries and ship software that holds up in production.
We build production-ready, highly observable agentic systems engineered for enterprise scale. No black boxes, no magic—just systematized workflows with systemic safeguards.
We don't build fragile wrappers. Complex decisions and exceptions are automatically routed to your team for approval, ensuring zero unverified actions in production.
Every AI-generated output is validated against deterministic, programmatic rules before execution, guaranteeing structural integrity and compliance.
Our architecture records every state change, agent reasoning step, and user interaction, providing complete observability into your automated workflows.
Built for enterprise scale. We optimize for high-throughput, low-latency execution using edge infrastructure and efficient state management.
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