Understanding AI for PE & VC

For fund managers, generative AI offers the potential to completely overhaul how their investment process works. Whether you’re trying to accelerate your diligence process or mine insights from years of portfolio data, the opportunities are enormous. But if you’re like many enterprises, actually implementing today’s tooling is easier said than done.

If your fund is evaluating how AI can be leveraged for efficiency gains or better investment decisions, then it’s important to understand the technology in the context of your own use cases.

In this post, we’ll simplify AI capabilities for private equity and venture teams. We’ll ground the technology in actual use cases from our customers. And using Metal's capabilities as an example, you'll have a clearer understanding of how you can enhance your deal and portfolio team’s workflows today.

An Analogy for the AI Stack

To understand today's AI capabilities, you can think of it like a library. So what makes up a library?

Readers: People who are looking for knowledge

Librarians: A person who is generally knowledgeable, skilled in language, and has an understanding of the library’s organization and contents

Books: The knowledge itself! Good libraries organize their books well, making information easier to find when needed

For example, a typical interaction at a library might go something like this:

library flow

In this case, the librarian understands the reader's question and retrieves the necessary information. Simple enough!

This is a bit how foundational models like Chat GPT, Claude, or Gemini work today. Where instead of readers we have users, instead of librarians we have models, and instead of books we have infrastructure and data.

library x llms

Foundational models are a big deal because they can understand a user's question and quickly provide a response on a wide range of subjects. It’s part of what makes these tools so powerful, and why they have fundamentally changed the capabilities of software.

That said, they are not without their limitations.

What About Private Data?

By definition, private company data is not accessible to the public. For LLMs, this means they are not trained on or informed about these data sets.

For example, let’s say you ask a model what the last 12 months of ARR was for a private company.

private co data flow

In order to answer this question, the LLM would need to have knowledge of the company, their financials, and understand concepts like time series data as it relates to financial metrics. But how do you get this information into the hands of an LLM?

Metal is Building the Missing Pieces

Using our library analogy, Metal can be understood as a system that puts the right books on the shelves and ensures the librarian (i.e. LLM) understands their contents for easy access.

There are three components in Metal that make this possible:

Infrastructure: We extract, structure, and store both qualitative and quantitative data on files uploaded to the system. Metal’s processing capabilities are explicitly focused on company documents – handling financials, call transcripts, board decks, and more.

Application: This data is then integrated into deal and portfolio team workflows, accelerating processes from early diligence to LP reporting.

Platform: And any data uploaded to Metal is fully extensible. You can push data in and out, customizing it to your proprietary workflows. For example, you can push extracted data from Metal directly into excel or BI tools for further analysis.

Real World Examples

So how can you use Metal today? Below are a few use cases that deal and portfolio teams are using Metal for right now:

For Deal teams:

Automated deal screening: Process data on an inbound deal (eg CIM, data room, 3rd party research) and compare it to similar deals in the past. This helps teams get bad deals out of the pipeline sooner, and jump on good deals faster.

Information requests: All data in Metal is highly searchable, making it easy for deal teams to understand what information they have on a company – and what information they are missing. Teams no longer need to spend hours digging through a data room looking for something that isn’t there!

Contact list extraction: Parse unstructured documents, like PDFs or documents, and convert their contents into excel tables. You can use this on many files at once, repeating the workflow at scale.

For Portfolio teams:

Automated portfolio updates: Process monthly or quarterly updates from companies, extracting and storing key financials and events.

Company trend detection: Metal extracts KPIs and metrics while also understanding concepts like time series data, or forecasted numbers vs actual numbers. This allows funds to analyze how a company is performing over time with ease.

Accelerated LP reports: Using the aggregated data Metal collects from company documents, you can generate drafts of reports for your LPs. Portfolio teams get hours of time back with every report!

Wrapping Up

While the hype around AI is warranted, for funds – the technology is only as useful as the data it can understand. Metal is bridging the gap between general purpose AI and specific fund workflows and needs. We’re purpose built for fund data and workflows, helping you efficiently find insights with ease.

If your fund is currently evaluating AI tooling, or is just getting started, please get in touch! We’ll help you build fully functional test environment with your own data, today.