Leveraging Portfolio Monitoring Data with Agentic AI
Leverage clean, centralized data to drive smarter, faster insights across your private equity portfolio.
As artificial intelligence continues to influence how private equity firms operate, the value of structured, high-quality portfolio monitoring data is becoming increasingly clear. Alvarez & Marsal’s (A&M) Valuation Services team works with firms to prepare, centralize and activate data to support more efficient analysis, streamlined performance tracking and AI-enabled decision-making.
Our Proven Framework to Build an AI-Ready Data Stack
We utilize a three-step approach to help private equity firms make their data ready for agentic AI applications:
Step 1: Optimize Portfolio Data Intake
A&M ensures your portfolio monitoring data and operating model are agentic AI-ready across four key data dimensions:
- Accuracy
- Completeness
- Breadth
- Timeliness
We assist with system selection, configuration and ongoing platform support to improve data quality and consistency.
Step 2: Centralize, Structure and Contextualize
A&M's approach to creating a context aware data warehouse for AI integration includes:
- Setting up warehouse infrastructure
- Defining a data model
- Designing and implementing a semantic layer
- Establishing data marts for improving accuracy and latency of agentic AI application
- Integrating agentic AI
Step 3: Enable Agentic AI Insights
A&M's practical framework to build secure, purpose-built LLM solutions:
- Define use cases with deal, portfolio and finance teams
- Establish LLM pipeline for natural language business intelligence
- Fine tuning for domain specific understanding
- Deploy secure, role-based interface
- Continuous improvement and monitoring
Transform Data into a Competitive Advantage
Discover how A&M helps private equity firms turn portfolio monitoring data into a competitive advantage with agentic AI.
Click below to explore the full framework and learn what it takes to build an AI-ready data foundation.
