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.
Originally published 18 June 2025
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