“Analysts burn the midnight oil working hundreds of hours doing the work that nobody wants to do,” Song told TechCrunch in an email interview. “At the same time, funds are deploying less capital and looking for ways to make their teams more efficient while reducing operating costs.”

Inspired to find a better way, Song teamed up with Brian Fernandez and Anand Chaturvedi, two ex-Coinbase colleagues, to launch Dili (not to be confused by the capital of East Timor), a platform that attempts to automate key investment due diligence and portfolio management steps for private equity and VC firms using AI.

Dili, a Y Combinator graduate, has raised $3.6 million in venture funding to date from backers including Allianz Strategic Investments, Rebel Fund, Singularity Capital, Corenest, Decacorn, Pioneer Fund, NVO Capital, Amino Capital, Rocketship VC, Hi2 Ventures, Gaingels and Hyper Ventures.

“[AI] affects all parts of an investment fund, from analysts to partners and back-office functions,” Song said. “Investment professionals at funds are looking for a differentiated edge on decision-making, and can now use their wealth of data to combine their understanding of the deal with how it fits into the funds … Dili has a unique opportunity to emerge as a solution for funds in a harsh macro environment.”

Song’s not wrong about funds looking for an edge — or any new promising ways to mitigate investing risk, for that matter. VCs reportedly have $311 billion in unspent cash, and last year raised the lowest total — $67 billion — in seven years as they grew increasingly cautious about early-stage ventures.

Dili isn’t the first to apply AI to the due diligence process. Gartner predicts that by 2025, more than 75% of VC and early-stage investor executive reviews will be informed using AI and data analytics.

Several startups and incumbents are already tapping AI to pour through financial documents and copious amounts of data to craft market comparisons and reports — including Wokelo (whose customers are private equity and VC funds, like Dili’s), Ansarada, AlphaSense and Thomson Reuters (through its Clear Adverse Media unit).

But Song insists that Dili uses “first-of-its-kind” technology.

“[We can] deliver very high accuracy on specific tasks like pulling financial metrics from large unstructured documents,” she added. “We’ve built custom indexing and retrieval pipelines tuned for specific documents to provide [our AI] models with high quality context.”

Dili leverages GenAI, specifically large languages models along the lines of OpenAI’s ChatGPT, to streamline investor workflows.

The platform first catalogs a fund’s historical financial data and investment decisions in a knowledge base, and then applies the aforementioned models to automate tasks such as parsing databases of private company data, handling due diligence request lists and digging for little-known figures across the web.

Dili recently added support for automated comparable analysis and industry benchmarking on a firm’s backlog of deals. Once funds upload their deal data, they can compare historical and current investment opportunities in one place.

“Imagine being able to get an email with a new investment opportunity or portfolio company update and instantly having a platform produce AI-generated deal red flags, competitive analysis, industry benchmarking and a preliminary summary or memo leveraging your fund’s historical investing patterns,” Song said.

The question is, can Dili’s AI — or any AI really — be trusted when it comes to managing a portfolio?

Image Credits: Dili

AI isn’t necessarily known for sticking to facts, after all. Fast Company tested ChatGPT’s ability to sum up articles and found that the model had a tendency to get stuff wrong, leave pieces out and outright invent details not mentioned in the articles it summarized. It’s not tough to imagine how this might become a real problem in due diligence work, where accuracy is paramount.

AI can also bring prejudices into the decisioning process. In an experiment conducted by Harvard Business Review several years ago, an algorithm trained to make startup investment recommendations was found to pick white entrepreneurs rather than entrepreneurs of color and preferred investing in startups with male founders. That’s because the public data the algorithm was trained on reflected the fact that fewer women and founders from underrepresented groups tend to be disadvantaged in the funding process — and ultimately raise less venture capital.

Then there’s the fact that some firms might not be comfortable running their private, sensitive data through a third-party model.

To attempt to allay all those fears, Song said that Dili is continuing to fine-tune its models — many of which are open source — to reduce instances of hallucination and improve overall accuracy. She also stressed that private customer data isn’t used to train Dili’s models and that Dili plans to offer a way for funds to create their own models trained on proprietary, offline fund data.

Dili ran an initial pilot last year with 400 analysts and users across different types of funds and banks. But as the startup expands its team and adds new capabilities, it’s angling to expand into new applications — ultimately toward becoming an “end-to-end” solution for investor due diligence and portfolio management, Song says.

“Eventually we believe this core technology we’re building can be applied to all parts of the asset allocation process,” she added.