Chronograph's platform now holds over $20tn in gross assets, says CEO Charlie Tafoya.
Tafoya argues private markets are where public markets were around 1986, lacking infrastructure and standardization.
Tafoya says current AI models cannot generate alpha on their own, with useful applications limited to productivity tasks like summarising diligence documents.
Private equity runs on data it cannot yet handle
Private equity generates enormous amounts of data and still lacks the infrastructure to handle it. Tafoya puts private markets where public markets were in 1986, before standardization. In 2008, LPs won transparency at the underlying company level, and in March 2020 consumer-exposed investors had to revalue portfolios daily. Technology competency now shows up in fundraising DDQs, and Chronograph's bet is a flexible data model rather than templates.
Takeaways
Private markets are stuck in 1986. Tafoya says private markets lack the standardization public markets had around 1986, when a filing meant a trip to a repository and a paper spreadsheet. He places the industry at the 30-minute mark of a soccer match.
2008 and 2020 reset disclosure expectations. Before 2008 only the largest funds gave transparency at the underlying company level, and the crisis empowered LPs to demand it. In March 2020, investors with consumer exposure had to revalue portfolios almost daily. Since then, Tafoya has not heard an investor defend Excel.
Technology is now a fundraising prerequisite. After a difficult couple of years of fundraising, GPs have woken up to streamlined operations as an opportunity. LP expectations about GP data and technology competency now appear in DDQs, including for first-time and emerging managers.
Private equity data was valuable and untooled. Tafoya worked on secondaries at Pantheon, when the market ran $10-12bn a year against over $100bn today. He found more data in private equity than he expected, with tremendous value and no good tools to use it.
Templates cap the fidelity of the data. Existing tools were template based, a middle office team emailed a form to a portfolio company, which filled it in and sent it back. Chronograph rejects templates on both sides because predefined fields limit what a company can report.
AI's near-term value is productivity. Tafoya says current models cannot generate alpha on their own. The useful applications are productivity ones, like summarizing diligence documents and the thousands of PDFs LPs receive each quarter. He advises firms to fix data architecture and governance before adopting AI.
Chronograph grew on almost no marketing. Chronograph did no marketing for its first seven years and spent about $8,000 in total, yet reports 126% net revenue retention. Tafoya calls the win rate almost artificially high.
How Chronograph was built
2016. Tafoya and Michael Bridge, both developers who met in Brooklyn through their wives, build the first LP platform from a windowless closet above a beer hall.
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On this episode 2
Show 1
The website launch drew fewer visits than there are members of Tafoya's immediate family.
Pacific Life, Investran's first client, signs on first, followed by APG, Europe's largest public pension plan.
Late 2018 into 2019. The company extends from LPs into GP workflow and data collection, with the SoftBank Vision Fund signing on.
2019. Carlyle invests and becomes a client, alongside Nasdaq. Summit Partners invests at the end of 2022.