A custom agentic AI platform that eliminated manual financial re-entry, surfaced anomalies in real time, and gave leadership new visibility into maintenance and vendor activity across the portfolio.
A commercial real estate firm managing a growing multifamily portfolio was dealing with a problem that's more common than most property owners admit. Their accounting team was spending significant time doing work that shouldn't have required a human at all.
Because the firm's properties were managed by a third-party operator, financial data (deposits, invoices, bills, GL entries) was controlled by the operator's team and delivered through exports. Before any of that data could be entered into the firm's own accounting records, the team had to review it for errors: duplicate invoices, wrong account codes, missing unit attribution, revenue misclassifications. Then they had to re-enter the corrected data manually, line by line.
On the operational side, maintenance work orders were inconsistently recorded. The firm had no reliable way to monitor how long tasks were taking, whether outsourced work was being used appropriately, or how maintenance productivity compared across properties.
Leadership knew the situation wasn't sustainable. But the answer wasn't obvious. The data complexity, the volume of exceptions, and the judgment required to catch coding errors made this feel like a human problem, not a technology problem.
Summit designed and deployed a custom agentic AI platform addressing three distinct use cases, beginning with a controlled Proof of Value at a single property.
The platform was trained on the firm's specific coding standards: account classifications, unit attribution rules, vendor categorization, prepaid treatment logic, and exception handling. Rather than delivering data as a static export for humans to review and re-enter, the platform reads incoming data, applies the firm's logic, validates each entry, and structures the output for direct entry into the accounting system. The manual re-entry step is eliminated. Entries that fall outside defined parameters get flagged for human review before they reach the records, not after.
The platform monitors financial data in real time against the firm's defined standards. Duplicate invoices, miscoded transactions, missing unit attribution, and revenue misclassifications get flagged immediately and routed to the appropriate team member for review. Issues that previously surfaced only at monthly close, after they had already affected reporting, are now identified and resolved before they create downstream problems.
The platform monitors work order completion data against established benchmarks for common maintenance tasks, tracks internal versus vendor labor usage, and surfaces patterns that would otherwise require manual analysis. The firm now has visibility into maintenance productivity across the portfolio: which tasks are being completed on time, where outsourcing may be excessive, and where operational gaps exist.
The Proof of Value ran for 30 days at a single property, with the platform operating in parallel with the existing process to validate outputs. By the end of the period, automated data flow was operating as designed, real-time anomaly detection had identified multiple actionable issues that would have been caught only at month end under the previous process, and maintenance oversight was live with benchmark comparisons active.
A Phase 2 expansion roadmap was defined at the conclusion of the Proof of Value, covering additional properties and new use cases including cross-property benchmarking, utility expense monitoring, and vendor performance scoring.
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