Webinar · 5 November 2026 · 47 min
The AI-First Approach to Portfolio Monitoring for PE Operating Teams
For private equity operating teams: monitoring portfolio companies with AI.
Speakers
- Dhiraj SharmaProduct, Fundrev
- Akash KhairnarOperations, Fundrev
About this session
Portfolio monitoring has run on Excel templates, Power BI dashboards and PDF board packs that each work on their own, so deal teams spend their time collecting and reconciling numbers rather than acting on them.
This session shows how AI assistants turn an uploaded board pack or template into a finished output, how projects, skills and artifacts make that work repeatable, and why the numbers have to be consistent, traceable and quick to check before a team can rely on them.
It then covers the data foundation behind it: each company's data inventoried, its key metrics defined once, mapped into a data warehouse and served to AI assistants through an MCP server, shown live in Fundrev and Claude. It closes with how AI maturity moves from assistants to agents to systems, and two ways to work with Fundrev: a hands-on workshop, or a project in which Fundrev builds the system.
What we covered
Introduction
Portfolio monitoring before AI
Excel templates, Power BI dashboards and PDF board packs each work on their own, so deal teams spend their time collecting and reconciling numbers rather than acting on them.
AI assistants turn an uploaded file into a finished output
Upload a board pack or template, describe what is needed, and review a working file: a variance analysis workbook or an interactive dashboard.
Projects, skills and artifacts make the work repeatable
Projects hold the context, skills hold the method and artifacts hold the output, so every analysis follows the same method and format.
Demo: an AI assistant on a company's reporting files
Faster insight only matters if the numbers can be trusted
The same question gets the same answer, every figure traces back to its source, and only flagged items need a human check.
A common data foundation makes every use case faster
Right after close, inventory the data each company holds and agree definitions for its key metrics.
How the data foundation works in practice
Company data is mapped once into a data warehouse, then served to AI assistants through an MCP server.
Demo: portfolio data in Fundrev and Claude
From assistants to agents to systems
AI maturity builds in stages: train people on assistants, build agents one workflow at a time, then redesign processes and data end to end.
Two ways to work with Fundrev
A hands-on workshop for portfolio teams, worked on their own reporting files, or a project in which Fundrev builds the system: data integration, benchmarks and live dashboards.
Next steps
Every registered guest receives a library of portfolio skills to import into their projects, and the session materials.