Dili aims to use AI for automating due diligence

Stephanie Song, once of the corporate development and ventures squad at Coinbase, frequently found herself annoyed by the amount of inspection assignments she and her team had to conclude on a daily basis.

“Analysts work tirelessly day and night putting in countless hours performing the work that nobody wants to do,” Song relayed to TechCrunch in an email interview. “Meanwhile, funds are deploying less money and seeking ways to make their teams more effective while shrinking operating expenses.”

Inspired to locate a more suitable alternative, Song collaborated with Brian Fernandez and Anand Chaturvedi, two former Coinbase co-workers, to introduce Dili (not to be mistaken with the capital of East Timor), a platform that endeavors to mechanize key investment verification and portfolio management stages for private equity and VC firms using AI.
Dili, a Y Combinator graduate, has brought in $3.6 million in venture funding so far from supporters 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] influences all parts of an investment fund, from analysts to partners and back-office functions,” Song stated. “Investment professionals at funds are seeking a special edge on decision-making, and can now use their wealth of data to combine their comprehension of the deal with how it fits into the funds … Dili has a unique chance to emerge as a solution for funds in a harsh macro environment.”

Song is not incorrect 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 initial ventures.
Dili isn’t the first to use 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.
A number of startups and long-standing organizations are already turning to AI to pour through financial documents and ample 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 initially 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 receive 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.

In a survey from Bloomberg Law, 30% of deal lawyers said they wouldn’t consider using AI as it exists today at any stage of the due diligence process, citing concerns including violating confidentiality agreements associated with deals by entering third-party info into AI software.

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.
“While hedge funds and public markets have invested heavily in tech, private market data has a lot of untapped potential that Dili could unlock for firms,” Song said.

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.

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