Leasing inquiry qualification
Capture inquiries from portals, forms, email, and messages. Classify fit, urgency, property, budget, and next step. Draft the response and route the lead to the right person with source context attached.
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Industries / 02
We deploy AI into the workflows that run real estate, property management, and owner reporting.
Forward-deployed engineers sit inside the leasing, tenant, maintenance, and owner-reporting loops that keep property businesses moving. The goal is not a chatbot. The goal is less delay between inquiry, decision, work order, document, and report.
When this industry needs the engine treatment
- Leasing inquiries arrive from portals, signs, email, WhatsApp, brokers, and referrals, then sit until someone manually qualifies them.
- Maintenance tickets lose context between tenant, manager, vendor, owner, and accounting.
- Owner reports are rebuilt from screenshots, PDFs, notes, and spreadsheets every month.
- Lease files, inspection reports, agreements, and correspondence are searchable only by the person who remembers where they were saved.
How we deploy it, by engine
Tenant and maintenance routing
Turn tenant messages into structured cases, detect emergency vs routine work, assign vendors, update the property system, and keep the audit trail visible.
Owner reporting and variance review
Pull rent, expenses, work orders, deposits, and notes into a review queue. Draft owner explanations and flag anomalies before reports go out.
Lease and property document memory
Index leases, inspection notes, management agreements, correspondence, and SOPs so staff can answer property questions with citations instead of hunting files.
On the ground
- Faster leasing response with the property, applicant context, and next step attached.
- Maintenance cases tracked from tenant message to vendor closeout with owner-visible history.
- Owner reports drafted from source records instead of monthly spreadsheet reconstruction.
Industry diagnostic
Diagnose your property workflow
We map the property loop that is bleeding time, prove whether AI should touch it, and price the first deployment only if the economics clear.
Workflow map / eval harness / go-no-go