Stephanie Song, a former member of the corporate development and operations team at Coinbase, was often frustrated by the amount of due diligence she and her team had to complete on a daily basis.
“Analysts are burning the midnight oil working hundreds of hours doing the work no one 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 former colleagues at Coinbase, to start Dilly (not to be confused by the capital of East Timor), a platform that attempts to automate the due diligence and portfolio management steps of key investments for private equity firms and VCs using artificial intelligence.
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] it affects all parts of an investment fund, from analysts to partners and back-office operations,” Song said. “Investment professionals in funds are looking for a differentiated decision-making edge and can now use their wealth of data to combine their understanding of the deal with how it fits the funds… Dili has a unique opportunity to emerge as a solution for funds in a tough macroeconomic environment”.
Song isn’t wrong about funds looking for an edge — or any promising new ways to mitigate investment risk, for that matter. VCs According to reports they have $311 billion in unallocated cash and last year raised their lowest total — $67 billion — in seven years as they grew increasingly cautious about early-stage businesses.
Dili is not the first to apply artificial intelligence to the due diligence process. Gartner predict that by 2025, more than 75% of executive and early-stage investor reviews will be informed using artificial intelligence and data analytics.
Several startups and incumbents are already using AI to sift through financial documents and copious amounts of data to create market comparisons and reports — including Wokelo (whose clients are private equity and VC funds like Dili’s); Ansarada, AlphaSense and Thomson Reuters (via the Spam Clearance Unit);
But Song insists Dili uses “first-of-its-kind” technology.
“[We can] it provides very high accuracy for specific tasks, such as extracting financial metrics from large unstructured documents,” he added. “We’ve built custom indexing and retrieval pipelines tailored to deliver specific documents [our AI] models with a high-quality frame.”
Dili leverages GenAI, specifically large language models according to OpenAI’s ChatGPT, to streamline investor workflows.
The platform first captures a fund’s historical financial data and investment decisions into a knowledge base and then applies the aforementioned models to automate tasks such as analyzing private corporate data databases, handling due diligence request lists, and searching little known elements on the web.
Dili recently added support for automated comparable analysis and industry benchmarking on a company’s pending deals. Once funds upload their trade 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 immediately have a platform that generates AI-generated deal red flags, competitive analysis, industry benchmarking and a preliminary summary or note that leverages historical investment patterns of your capital,” Song said.
The question is, can Dili’s AI – or any AI really – be trusted when it comes to managing a portfolio?
Image Credits: Dilly
AI isn’t necessarily known for obsessing over facts, after all. Fast Company tested ChatGPT’s ability to summarize articles and found that the model had a tendency to get things wrong, leaving bits out and inventing details not mentioned in the articles it was summarizing. It is not difficult to imagine how this can become a real problem in due diligence work, where accuracy is paramount.
AI can also bring biases into the decision-making process. In an experiment be conducted by the Harvard Business Review several years ago, an algorithm trained to make startup investment recommendations was found to choose white entrepreneurs over entrepreneurs of color and preferred to invest in startups with male founders. This is because the public data on which the algorithm was trained reflected the fact that fewer women and founders from underrepresented groups tend to be at a disadvantage in the financing process — and ultimately raise less venture capital.
Then there’s the fact that some companies may not feel comfortable running their private, sensitive data through a third-party model.
To try to assuage all these fears, Song said Dili continues to improve its models – many of which are open source – to reduce the incidence of hallucinations and improve overall accuracy. He also stressed that private client data is not used to train Dili’s models, and that Dili plans to offer a way for funds to build their own models trained on proprietary, offline fund data.
“While hedge funds and public markets have invested heavily in the technology, private market data has a lot of untapped potential that Dili could unlock for companies,” Song said.
Dili ran an initial pilot program last year with 400 analysts and users across different types of mutual funds and banks. But as the startup expands its team and adds new features, it wants to expand into new applications — eventually toward becoming an “end-to-end” solution for investor due diligence and portfolio management, Song says.
“Ultimately we believe that this core technology that we are building can be applied to all parts of the asset allocation process,” he added.
