AI Agentic Workflow for Feeding Abstracted Data into Business Intelligence (BI) Platforms

Portfolio analysts typically spend 2-3 weeks per quarter manually extracting, cleaning, and mapping lease data from PDFs and spreadsheets into valuation models. A custom agentic workflow automates this ETL process, ingesting normalized abstraction outputs and feeding them directly into DCF templates in Power BI or Tableau. This shifts analyst effort from data wrangling to interpreting results and modeling scenarios.

Disparate data from property managers (Yardi, MRI), legacy spreadsheets, and abstracted leases creates reconciliation overhead and reporting risk. A custom workflow unifies these streams through automated mapping, conflict resolution, and currency normalization. The output is a governed, real-time dataset in the BI platform's semantic layer, enabling consistent dashboards for occupancy, NOI, and valuation across the entire portfolio.

Static quarterly reports delay responses to market shifts. A workflow that continuously ingests live data—like lease expirations, CPI indices, and cap rate feeds—enables dynamic what-if analysis directly in the BI tool. Asset managers can instantly model the impact of renewal assumptions, rent resets, or economic changes on portfolio NAV, turning valuation from a backward-looking report into a forward-looking planning instrument.

Legacy systems like Argus Enterprise create data silos, requiring manual exports and reformatting for broader analysis. A custom workflow uses browser automation and API connectors to extract structured data from these closed systems, harmonize it with abstracted lease terms, and push it to a modern cloud data warehouse. This breaks the dependency, reduces licensing costs for casual users, and makes portfolio data accessible for custom analytics.

Compiling board and investor packages is a manual, error-prone process of copying data from multiple systems into slide decks. An automated workflow triggers the generation of standardized report packages directly from the BI platform, pulling the latest visualizations, rent rolls, and valuation summaries. This ensures consistency, embeds live data, and cuts report production time from days to hours, enhancing stakeholder confidence.

Leasing, asset management, and acquisitions teams often wait for analyst-run reports. By building a well-modeled semantic layer with clear dimensions (Property, Tenant, Lease Type) and measures (Effective Rent, Renewal Probability), the workflow empowers these teams to explore data safely in the BI tool. This reduces the analyst bottleneck and allows business users to answer their own ad-hoc questions about lease performance and portfolio concentration.

This agent ingests raw, abstracted lease terms from documents or systems like Yardi and applies business logic to unify disparate data. It handles critical tasks like converting gross/net rents to a standard metric, normalizing square footage units (e.g., SF to SM), applying live FX rates for global portfolios, and mapping inconsistent clause labels (e.g., 'CPI Adjustment' vs. 'Indexed Escalation') to a controlled vocabulary. The output is a clean, query-ready fact table that serves as the semantic layer for the BI tool.

This component processes time-series lease data to create aggregated views for trend analysis. It rolls up monthly rent rolls to quarterly/annual forecasts, cohorts leases by expiration year or renewal probability for risk dashboards, and calculates portfolio-wide weighted average lease term (WALT) and occupancy rates. It enables analysts to slice performance by vintage, tenant credit tier, or lease structure without writing complex SQL.

A rules-based agent continuously monitors the normalized data stream against historical benchmarks and portfolio averages. It flags outliers such as rents significantly above/below market, unusual CAM recoveries, or leases missing critical terms. These anomalies are routed to a human review queue in the BI dashboard or via Slack/MS Teams, ensuring data quality before it drives strategic decisions.

This system component manages the integration between the processed data warehouse and the BI platform (e.g., Power BI Datasets, Tableau Extracts). It orchestrates incremental or full refreshes on a scheduled or event-driven basis (e.g., after new lease abstractions). It handles authentication, applies row-level security rules based on user roles (analyst vs. asset manager), and manages versioning to prevent report corruption during updates.

A critical control layer that logs every data transformation, aggregation, and user access event. It creates an immutable record of how a specific KPI on a dashboard was derived from source lease documents. This service supports compliance (SOX, GDPR) and provides defensible audit trails for valuation conclusions presented to investors or auditors, linking dashboard insights directly back to original lease terms.

This is the user-facing component where portfolio analysts can define custom calculated metrics without engineering support. Using a low-code interface, they can create formulas like 'Rent PSF Growth' or 'Projected Vacancy Cost' that are computed by the aggregation engine and instantly available in the BI tool. This shifts report development from IT backlog to business operations, dramatically accelerating insight velocity.