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Stephanie Song, who was previously in the corporate development and venture team at Coinbase, was often frustrated by the amount of due diligence tasks she and her team had to complete on a daily basis.
“Analysts spend hundreds of hours working in the middle of the night doing work that 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 ex-Coinbase colleagues, to launch the coin. No (not to be confused with capital of east timor), a platform that seeks to automate key investment due diligence and portfolio management stages for private equity and VC firms using AI.
Y Combinator graduate Dili has raised $3.6 million in venture funding so far from Allianz Strategic Investments, Rebel Fund, Singularity Capital, CoreNest, Dekacorn, Pioneer Fund, NVO Capital, Amino Capital, Rocketship VC, High2 Ventures, Gangels and backers. Hyper Ventures.
,[AI] “Investment impacts all parts of the fund, from analysts to partners to back-office functions,” Song said. “Investment professionals in funds are looking for a different edge in decision making, and now they can use their wealth of data to connect their understanding of the deal with how it fits into the fund… Dili “Funds have a unique opportunity to emerge as a solution in a tough macro environment.”
Song isn’t wrong about funds looking for an edge or a promising new way to reduce investment risk. VC’s Allegedly They have $311 billion of unspent cash, and last year they raised the lowest total in seven years – $67 billion – as they became more cautious about early-stage ventures.
Dili is not the first to apply AI to the due diligence process. Gartner Prediction By 2025, more than 75% of VC and early-stage investor executive reviews will be informed using AI and data analytics.
Many startups and incumbents are already using AI to generate market comparisons and reports by sifting through financial documents and abundant data – including Vocello (whose clients are private equity and VC funds, like Dilly) . Ansarada, alphasense and Thomson Reuters (through its Clear Adverse Media unit).
But Song emphasizes that Dilly uses “the first of its kind” technology.
,[We can] “Delivering very high accuracy on specific tasks like extracting financial metrics from large unstructured documents,” she said. “We have built custom indexing and retrieval pipelines to provide specific document [our AI] “High quality reference models.”
Dilly leverages models from GenAI, specifically large languages along the lines of OpenAI’s ChatGPT, to streamline investors’ workflows.
The platform first catalogs a fund’s historical financial data and investment decisions into a knowledge base, and then automates tasks such as parsing databases of private company data, handling due diligence request lists, and searching for little-known data on the web. Applies the above model to. ,
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 to capture AI-generated deal red flags, competitive analysis, industry benchmarking and a preliminary summary or memo of your fund’s historical investment patterns. “Takes advantage of,” Song said.
The question is, can Dilli’s AI – or any AI – really be trusted when it comes to managing portfolios?
Image Credit: No
After all, AI isn’t necessarily known for sticking to facts. fast company tests ChatGPT’s ability to summarize articles and found that the model had a tendency to get things wrong, leave out pieces, and invent details not mentioned in the summarized articles. It’s not hard to imagine how this could become a real problem in due diligence work, where accuracy is paramount.
AI can also introduce bias into the decision process. in an experiment operated Several years ago by Harvard Business Review, an algorithm trained to make startup investment recommendations was found to choose white entrepreneurs over entrepreneurs of color and to prefer investing in startups with male founders. This is because the public data on which the algorithm was trained reflects the fact that there are fewer women and founders from underrepresented groups. Deprived in the funding process – and ultimately raise less venture capital.
Then there’s also the fact that some companies may not be comfortable with running their private, sensitive data through a third-party model.
To try to allay those fears, Song said Dilli is continuing to improve its models — many of which are open source — to reduce such incidents. Maya 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. Have planned.
“While hedge funds and public markets have invested heavily in the technology, there is a lot of untapped potential in private market data that Dili can unlock for companies,” Song said.
Dilly ran an initial pilot last year with 400 analysts and users from a variety of funds and banks. But as the startup expands its team and adds new capabilities, it is poised to expand into new applications – ultimately becoming an “end-to-end” solution for investor due diligence and portfolio management. , Song says.
“Ultimately we believe the core technology we are building can be applied to all parts of the asset allocation process,” he added.
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