Private Equity's Cold Storage Problem

We hear it all the time from private equity deal teams: “We’ve got a SharePoint folder with tens of thousands of documents from the past 20 years.” This trove of memos, market reports, board decks, and more is meticulously labeled—“Project Alpha,” “CIMs,” you name it—yet most of that knowledge remains untapped.

At Metal, we see the real challenge as not just finding these documents, but processing them so deal teams can actually leverage what’s inside.

Why “Chat With Your Data” Falls Short

Back in 2023, we built a simple Retrieval-Augmented Generation (RAG) system on OpenAI's GPT-3 series, wrapped in a chatbot interface. It allowed queries like, “Give me an overview of Project Alpha’s CIM,” and would retrieve relevant snippets. But these documents are often too dense and nuanced for standard semantic search, leaving users with lengthy legal text or marketing language that’s not always actionable.

We quickly realized that the core issue wasn’t retrieval alone—it was processing. By classifying, summarizing, and organizing this data upfront, we could provide far more precise insights. That realization led us to build robust data pipelines that dig into every nook and cranny of your SharePoint or shared drive, automatically tagging and organizing files to deliver the right information to the right analyst at the right time.

The Agentic Librarian

Over time these pipelines matured into an agentic organizer for firms—constantly detecting and classifying new files, growing smarter and more efficient with each upload or crawl. We call these classification tools our “Detectors,” and they’re the heart of our platform:

1. Company Detection: We identify the specific company each file references, ensuring it goes into the correct workflow. By doing so, we can quickly extract the most crucial metrics without forcing users to sift through irrelevant data.
2. Document Date Detection: In private equity, timing is everything. By tagging files with contextual dates, you can spot changes over time—an essential factor in discovering alpha.
3. Document Type Detection: A CIM might gloss over risks with promotional language, whereas an expert call can reveal more candid insights. We tag each document by type so our system can account for these nuances.
4. Topic Detection: Whether you’re dealing with “pricing power,” “customer dynamics,” or “market headwinds,” we label each file with the relevant topics. This classification lets you see the bigger picture of a company’s performance and challenges.

Screenshot of Metal's File System

Leading the Way in PE AI

Through these pipelines, we’re doing more than opening up old files—we’re ushering in a new era of data-driven analysis that goes way beyond a simple chatbot. Our clients view it as a competitive edge: vital insights are accessible in seconds, letting them move at the rapid pace the market demands.

By focusing on intelligent data processing, Metal helps private equity firms transform decades of archived material into real-time intelligence. The result? Teams become more proactive, more informed, and better equipped for whatever comes next.