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Stephanie Song, formerly a member of the corporate development and venture capital team at Coinbase, was often frustrated by the volume of due diligence tasks she and her team had to complete on a daily basis.
“Analysts burn the midnight oil by working hundreds of hours 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 solution, Song teamed up with Brian Fernandez and Anand Chaturvedi, two former colleagues at Coinbase, to launch not (not to be confused with capital of East Timor), a platform that attempts to automate key investment due diligence and portfolio management steps for private equity and venture capital firms using AI.
Y Combinator graduate Dili has raised $3.6 million in venture capital 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-enterprises.
“[AI] affects all parties of an investment fund, from analysts to partners and back-office functions,” Song said. “Fund investment professionals are seeking a differentiated decision-making advantage 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 difficult macroeconomic environment.
Song isn’t wrong about funds seeking an edge — or any other promising way to mitigate investment risk, for that matter. VC would have have $311 billion in unspent cash and last year raised the lowest total – $67 billion – in seven years, as they became increasingly cautious about phase-in companies. starting.
Dili is not the first to apply AI to the due diligence process. Gartner predicted that by 2025, more than 75% of executive reviews of venture capitalists and early-stage investors will be informed by AI and data analytics.
Several startups and incumbents are already leveraging AI to sift through financial documents and large amounts of data to build market comparisons and reports, including Wokelo (whose clients are private equity and equity funds). risk, like that of Dili), Ansarada, AlphaSense and Thomson Reuters (through its Clear Adverse Media unit).
But Song insists Dili uses “first-of-its-kind” technology.
“[We can] provide very high accuracy on specific tasks such as extracting financial metrics from large unstructured documents,” she added. “We have created custom indexing and retrieval pipelines tailored to specific documents to provide [our AI] models with high quality context.
Dili leverages GenAI, specifically large language models similar to OpenAI’s ChatGPT, to streamline investor workflows.
The platform first catalogs a fund’s historical financial data and investment decisions into a knowledge base, then applies the aforementioned models to automate tasks such as analyzing private company data databases , managing due diligence request lists, and searching the web for little-known figures. .
Dili recently added support for automated comparable analysis and industry benchmarking on a company’s dealbook. Once funds upload their transaction 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 have a platform producing AI-generated red flags, competitive analysis, analysis “Industry benchmarking and a summary or preliminary memo leveraging your fund’s historical investment patterns,” Song said.
The question is: can Dili’s AI – or any AI in fact – be trusted when it comes to managing a portfolio?
Image credits: not
After all, AI isn’t necessarily known for sticking to facts. Fast business tested ChatGPT’s ability to summarize articles revealed that the model had a tendency to make mistakes, leave things out, and outright make up details not mentioned in the articles it summarized. 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-making process. In an experiment led by Harvard Business Review Several years ago, an algorithm trained to make investment recommendations to startups was found to choose white entrepreneurs over entrepreneurs of color and preferred to invest in startups with male founders. Indeed, 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 financing process – and ultimately raise less venture capital.
Then there’s the fact that some businesses might not feel comfortable managing their private and sensitive data through a third-party model.
To try to allay all these fears, Song said Dili continues to refine its models – many of which are open source – to reduce cases of hallucination and improve overall accuracy. She also emphasized that private client data is not used to train Dili’s models and that Dili plans to offer funds a way to create their own models trained on proprietary, offline fund data.
“While hedge funds and public markets have invested heavily in technology, private market data presents untapped potential that Dili could unlock for businesses,” Song said.
Dili conducted a first pilot project last year with 400 analysts and users from different types of funds and banks. But as the startup expands its team and adds new capabilities, it is looking to expand into new applications – ultimately becoming an “end-to-end” solution for investor due diligence and portfolio management, says Song .
“Ultimately, we believe that this core technology that we are developing can be applied to all parts of the asset allocation process,” she added.
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