Portfolio-wide lease answers without re-reading every PDF.

Portfolio questions require re-reading every lease by hand.

Lease terms, renewal windows, and escalation clauses usually live inside scanned PDFs attached to a PROPERTY. A question like "which LEASE records expire in the next 90 days with a fixed 3% escalation" normally means opening every lease again by hand, because nothing durable was extracted and stored as structured, queryable fields the first time it was read.

Graph intelligence on ingest

Incoming data becomes queryable graph context.

Lease abstraction produces a PROPERTY nesting LEASE records, each carrying tenant, expiration, and escalation fields. RushDB links each LEASE to its PROPERTY and TENANT the moment that nested structure is imported, and suggested-relationship analysis can connect LEASE records imported flat from a document-processing pipeline back to the right property and tenant.

Suggested relationship analysis requires an LLM configured for the project. Suggestions stay in draft form until you approve them, so inferred domain meaning never mutates the graph silently. You can also add explicit relationships through the SDK or API.

Data model

One flexible graph for the workflow.

Start with the payload shape your product already produces. RushDB stores it as Records, infers typed properties, and keeps nested or approved domain relationships queryable.

{
  "propertyId": "riverside-plaza",
  "LEASE": [{
    "leaseId": "lease-3391",
    "expiresAt": "2026-09-30",
    "escalationType": "fixed",
    "escalationRate": 3.0,
    "renewalOptionMonths": 6,
    "TENANT": [{ "name": "Northwind Logistics" }]
  }]
}

Working example

Find every lease that matches a portfolio-wide condition.

Once lease terms are extracted once and stored as Records, a portfolio question becomes a single filtered query instead of a document review.

PROPERTY riverside-plaza
  LEASE lease-3391
    tenant: "Northwind Logistics"
    expiresAt: "2026-09-30"
    escalationType: "fixed"
    escalationRate: 3.0
    renewalOptionMonths: 6

{
  "labels": ["LEASE"],
  "where": {
    "expiresAt": { "$lte": "2026-09-30" },
    "escalationType": "fixed",
    "escalationRate": { "$gte": 3.0 }
  },
  "limit": 50
}

{
  "leaseId": "lease-3391",
  "property": "riverside-plaza",
  "tenant": "Northwind Logistics",
  "expiresAt": "2026-09-30",
  "escalationRate": 3.0
}

TypeScript SDK

Query the whole portfolio in one call.

Lease abstraction happens once, on import. Every portfolio-wide question after that is a filtered query, not a document re-read.

from rushdb import RushDB

db = RushDB('RUSHDB_API_KEY')

schema = db.ai.get_schema_markdown({'labels': ['PROPERTY', 'LEASE', 'TENANT']}).data

expiring_leases = db.records.find({
    'labels': ['LEASE'],
    'where': {
        'expiresAt': {'$lte': '2026-09-30'},
        'escalationType': 'fixed',
        'escalationRate': {'$gte': 3.0},
    },
    'limit': 50,
})

Implementation blueprint

Build the lease-abstraction portfolio path.

Use this sequence to turn extracted lease terms into a queryable portfolio memory instead of a per-building manual process.

Relevant docs

Read the exact primitives behind this pattern.

These links point to the RushDB docs pages that map directly to this blueprint: ingestion, labels, properties, values, SearchQuery, relationships, semantic search, MCP, or deployment.

How it works

Build the smallest useful workflow first.

Know where it fits.

Questions developers ask.