Partnership combines Basking’s AI extraction and review platform with Propstack’s abstraction team to deliver verified lease data faster and at lower cost
HOLLADAY, UT, UNITED STATES, September 29, 2026 /EINPresswire.com/ — Basking.io and Propstack today announced a strategic partnership to launch AI-powered lease abstraction services worldwide. The service combines Basking’s AI extraction and human-in-the-loop review platform with Propstack’s expert abstraction team, built on more than a million structured real estate transactions. Enterprises get verified lease data in a fraction of the time and cost of manual review.
Traditional lease abstraction is accurate but slow and expensive. AI can process documents much faster, but enterprises still need someone to verify the output before relying on it. In the new service, AI reads each lease and Propstack experts check the results.
“A single missed break notice or renewal option can cost a company millions of dollars in rent on space it meant to exit, yet roughly 80% of lease data still sits unstructured in PDFs no one reads twice,” said Dr. Eldar Gizzatov, CEO of Basking. “Basking’s AI reads every lease, Propstack’s experts verify the clauses that matter, and we stand behind the result.”
“Propstack has structured more than a million real estate transactions and over 1.4 billion square feet of space in India,” said Raja Seetharaman, Co-founder of Propstack. “Combining that expertise with Basking’s AI platform lets us offer enterprises worldwide something that didn’t exist before: abstraction at AI speed with the accuracy of a trained analyst on every lease.”
How the service works
AI extraction. Basking uses multi-model extraction to pull hundreds of fields per document, including rent, renewals, escalations, options, covenants, penalties and key dates, in a single pass. Every extracted value carries a confidence score and a link back to its exact location in the source document. Basking has processed thousands of documents to date.
Expert verification. Low-confidence fields go automatically to a review workspace, where Propstack’s experts confirm, edit or reject each value against the source. Every change is tracked, so finance and legal teams get a full audit trail, and hours of manual work per lease become minutes.
Structured lease data. Verified leases become structured records covering details, events, options, cost schedules and legal conditions. Teams can search and filter the data across their portfolio and export it to finance, legal or BI tools. The service extends Basking’s LeaseOps platform for managing lease obligations, risks and renewals.
There is no software to install and no integration project. Customers upload leases, and verified data is delivered in the LeaseOps platform or exported to their systems of record. The service is available now to enterprise customers globally.
Get started free: new customers can abstract up to 10 leases at no cost. Sign up at https://app.basking.io/signup.
About Basking.io
Basking.io provides AI-driven occupancy and lease data intelligence for corporate real estate teams. Its LeaseOps platform and human-in-the-loop AI abstraction turn lease documents into structured, verified data, helping organizations manage multi-million-dollar obligations and make faster portfolio decisions. Basking is headquartered in Holladay, Utah and Berlin, Germany. Its investors include Ray Wirta (former Chairman and CEO of CBRE), Zain Jaffer (founder of Vungle, acquired by Blackstone for $780 million) and Future Energy Ventures, the corporate venture capital arm of E.ON. Visit basking.io.
About Propstack
Propstack is India’s leading real estate data, analytics and workflow platform. Founded in 2013 and headquartered in Mumbai, Propstack maintains India’s largest private repository of registered sale, lease and mortgage records, covering more than 1.4 billion square feet, over 1 million transactions and more than $100 billion in real estate loans. It is backed by DMGT (dmg information) and Real Capital Analytics (now part of MSCI). Visit propstack.com.
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