Train a Custom AI for Lease Administration

Many CRE firms adopt tools like MRI's AI-powered abstraction or other third-party platforms. These systems work well for standard clauses like rent schedules and expiration dates found in simple office leases. They rely on models trained on generic data, failing to capture the specific nuances your 10-person team has developed for interpreting complex retail or industrial lease language.

Consider a lease administration team onboarding a new retail portfolio. A standard lease might have a co-tenancy clause that a generic tool can identify. But a custom-negotiated version might tie the clause to three specific anchor tenants, a 5-mile radius, and a tiered rent abatement remedy. The off-the-shelf tool extracts "Co-tenancy clause: Yes" but misses the critical business terms, forcing an analyst to manually re-read the 80-page document.

The core issue is architectural. These platforms are built as multi-tenant SaaS products, meaning their AI models are trained on a vast pool of documents from all their customers. This approach prevents them from fine-tuning the model on your team’s specific feedback and proprietary abstraction standards. You are forced to adapt your workflow to the tool's limitations, not the other way around.

The result is a system the team cannot fully trust. Senior administrators still have to review 100% of the AI's output, negating time savings. The cost per lease remains high, and the risk of a missed critical date or clause persists, despite the investment in new technology.

The engagement would begin with a 2-day data audit. We would analyze a sample of 20-30 of your past lease abstracts and the source PDFs. Syntora would map your team’s specific data fields, often over 100 per lease, and document the logic from your abstraction guide to create a target data schema.

The core of the system would be a data pipeline using the Claude API for its large context window, ideal for 100+ page lease documents. A Python script would pre-process each lease PDF, sending key sections to the model with prompts engineered from your guide. Extracted data would be validated against the schema using Pydantic and stored in a Supabase database, providing a full audit trail. This entire workflow would execute in under 60 seconds per document.

The final deliverable is a simple web interface, built with Vercel, where your team can upload leases and review the extracted abstracts. The system would highlight low-confidence extractions for human review, aiming for 95% accuracy on critical date and financial fields. You receive all the Python source code and a runbook for retraining the model, ensuring the system evolves with your portfolio within a typical 3-week build cycle.