An externally-managed CRE firm ran its portfolio on a stack of disconnected systems: MRI for accounting, ARGUS for valuation, VTS for leasing, a CMMS for maintenance, with contracts and documents sitting in the managers' SharePoint and no unified view above any of it.
The data was scattered, and in places the firm had no real workflow around it. Data quality was extremely poor, the "Argus bug" later traced to unvalidated inputs was one symptom of many. Internal data couldn't be stitched together with external market data (comps, tenant credit, submarket trends). There was no way to take public signals like news, filings, market moves and see how they would hit the portfolio. There was no capability to turn any of it into real projections or predictions, let alone the human-in-the-loop workflows an operator needs to act on them. So every leasing, CapEx, and procurement decision was made in its own silo, on partial information, none tied to the year-end valuation that drives the NOI.
A three-layer decision platform for a CRE operating team: a unified Databricks lakehouse with automated extraction across leases, contracts and work orders; human-in-the-loop AI workflows that score each leasing, capex and procurement decision against NOI; and an evals-and-governance layer (built on Tensile) that traces every number and back-tests every model.
Across leasing, capex, and procurement
ROI prioritized spend improvements thanks to data validation, improved baselining, external source enhancements
The net-effective value of new leases (what actually reaches NOI after tenant improvements, free rent, and cash-flow timing).
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