Over the next few years, we expect that recent advances in data processing, cloud infrastructure, and artificial intelligence will allow for a fundamentally new class of vertical software products to be built that bake “intelligence” into all layers of the workflow. Large language models, with their ability to ingest and semantically reason over even the gnarliest unstructured data sets, have, in particular, unlocked new opportunities to go after markets that were not possible to address with legacy information processing and ML techniques. This pattern is exemplified by several companies we’ve partnered with today, like Alphasense, Weave, and Trunk Tools, all of which are applying foundation models to bring greater productivity and decision-making insight to their industries.
Our conviction in this thesis only deepened when we incubated and partnered with an exceptional duo, Olivier Babin and Naunidh Singh Bhalla, as they led a Research Driven Ideation (RDI) investigation into the changing needs of private market investors. Combining their backgrounds in financial services with a unique perspective on how LLMs might allow them to rethink some of the core needs in the industry, the team conducted hundreds of customer interviews to identify a massive pain point around performance data collection and analysis. Today, we are excited to announce that we are leading the seed round for the solution to this problem: Tetrix.
In the financial services world, institutional capital allocators like pension funds, sovereign wealth funds, university endowments, and family offices are responsible for investing in a diversified portfolio of assets, which can include traditional investments (public stocks and bonds), as well as “alternative investments” like private equity, venture capital, and hedge funds. The risk-return profile of these alternative investments has garnered increasing interest from investors, and consequently, investment in alternatives is expected to grow from around $13 trillion today to $23 trillion by 2026.
Despite this growth, many of the workflows around alternatives investing have remained extremely arcane. In particular, while there is robust data and reporting infrastructure that help an allocator dissect their holdings and positions in public securities in real-time, in the alternatives world, allocators typically receive documents (like financial statements, SOIs, and investor letters) from their fund managers as unstructured PDFs. To perform even basic analysis in a spreadsheet requires first parsing this data into a usable format. While computer vision techniques such as OCR can be helpful in domains where documents are standardized, in this case, each document has a unique format that can change quarter-to-quarter. Moreover, stylistic choices made by fund managers (such as using a portfolio company’s logo instead of its name) can limit the effectiveness of legacy text extraction techniques. So, most funds must employ a human team, most commonly via an outsourced service provider like MSCI, to manually key in data from these documents.
This manual dependency creates a fundamental three-way tradeoff between the speed of parsing, accuracy of the data, and amount of information that can be pulled. In our customer discovery interviews, we heard cases of numbers being reported inaccurately, investment teams waiting over a month for data to be processed, and back office teams trying to prepare Q2 2024 financial statements using Q4 2023 data. Most surprising to us were the implications of these challenges on the questions that allocators could ultimately answer over their holdings. For example, an allocator might struggle with the following simple queries:
Now, enter Tetrix, a company building a data ingestion and analytics platform that helps capital allocators manage their investments in alternative assets. Using large language models and other natural language processing techniques, the product retrieves and parses reports and one-pagers from fund managers and extracts key data points. The flexibility of transformer-based models enable Tetrix to scale to automatically parse reports in any format and handle the domain-specific semantic nuances of working with these documents.
Once the data is ingested, the Tetrix platform offers rich insights and benchmarking tools that help allocators answer questions about their underlying assets, benchmark managers within and across industries, plan and forecast future cash needs, generate reports for boards and investment committees, and more. The end result is that allocators get more accurate and more real-time data, a deeper understanding of performance and risk, and faster throughput on decision-making and analytical workflows, enabling them to make better investment decisions.
The team and story behind Tetrix is unique and follows a long history we have of incubating companies through Research Driven Ideation. I first got to know Olivier and Naunidh when they were students at the Stanford GSB. Olivier was formerly a banker and VC, spending time at Goldman Sachs before joining the Softbank Vision Fund. Naunidh graduated from the Singapore University of Technology and Design and was a software engineer and tech lead at JP Morgan before joining the GSB. The two had a deep shared interest in entrepreneurship, exploring various ideas for months before they graduated, and were even voted “most likely to build a unicorn” by their Stanford class.
After they graduated, the team decided to forgo corporate jobs and undertake a structured search process to find a startup idea to work on. Having used the same RDI process with my co-founders to start both of my companies, the whole Innovation Endeavors team and I were excited to support them in the incubation process over the last year. After conducting hundreds of interviews and exploring dozens of ideas across the financial services and supply chain, it became clear to Olivier, Naunidh, and us that the idea that ultimately became Tetrix had the right combination of market size, willingness to pay, technical difficulty, and “why now” to make it an exciting opportunity.
Although we agreed with the team about the various reasons why this is a good market to tackle — level of pain, low quality of incumbent solutions, the opportunity for technical disruption using LLMs, etc. — what got us most excited to invest was the team itself. We were consistently impressed by their customer-centric and fail-forward-fast approach to the incubation process — constantly experimenting, learning, and refining while asking the right questions. The team has the technical chops to solve the hardest problems and the GTM sophistication required for this vertical. These, we believe, will propel them more than anything to build an incredible company. We are excited to continue to support Tetrix in this next phase of the journey. Welcome to the Innovation Endeavors family!
