Investors are utilizing the sea of public data to make better financial decisions; however, with millions of data sets available, it becomes very difficult for investors to make sense of it all, at scale. Apteo helps investors cut through the noise by sourcing, centralizing, and analyzing alternative data. Apteo’s platform allows financial firms to integrate their own data sets, while automatically organizing it alongside a built-in catalog of 2M+ public datasets. The software also allows investors to create A.I.-supported future forecasts without requiring any knowledge of data science.
AlleyWatch interviewed Shanif Dhanani to learn how Apteo allows investors to utilize the best of data science tools, the effect that data and analytics are having on investing, and the company’s recent funding round.
Who were your investors and how much did you raise?
We raised a $1M Seed. The lead investor is a VC firm named Ripple, with follow-on investment from ERA and various angels from the finance ecosystem.
Tell us about the product or service that Apteo offers.
We’re creating a data management and analytics platform that helps financial firms source, centralize, and analyze alternative data. Sell-side banks and buy-side funds use us to automatically identify relevant data for the assets and investments they research, share and categorize their internal data, and create A.I.-supported future forecasts without knowing a thing about data science.
Before we started Apteo, my cofounder and I both independently started thinking about how investors could better use data. My background is in software and AI, and I had traded options using my own money (this actually helped pay my way through business school), but always based on gut instinct, and I knew there had to be a more systematic way to make decisions. My cofounder, who came from the world of institutional finance, began to see the power of data and analytics for investing.
We met each other through a common friend and started organically working together to apply some of the latest techniques in machine learning to stock analysis, and ultimately, we decided to go at it full time. Our first product was a B2C site that used A.I. to pick stocks, but we pivoted to where we are now – a B2B data science platform for finance firms.
How is Apteo different?
Everyone right now is trying to figure out how data can be used to improve decision-making, this is especially true in finance. Some startups are coming at the problem from the perspective of selling custom datasets to investors, others are approaching the perspective of sorting and aggregating public data or private data, other firms are trying to make it easier for investors to get access to traditional financial data in an easier platform, and others still are trying to help financial data scientists build better models.
We’re doubling down on the problems of dataset categorization, discovery, relevance, and analysis. Our platform is designed to help firms solve the problem of managing data and using it to answer their questions. We bring a catalog of more than 2 million public datasets into a platform where they can also plug in their own data and we organize it all for them automatically, and then we give them the tools to use it within their workflow.
What market does Apteo target and how big is it?
We’re targeting financial service firms that are using data to improve their decision-making. This market was around $28B in the previous year, and we’re excited to be focusing on the fastest-growing segment: the use of alternative data and data science.
What’s your business model?
We provide a SaaS platform that charges users on a monthly basis for access to the product and (coming soon) sells premium data to all users. We also charge enterprises an implementation fee for enterprise-grade access to the product.
Who do you consider to be your main competitors?
We frequently hear that our platform has several components that users haven’t seen before. With that said, the names that we hear come up during our conversations with customers tend to fall into two buckets:
- Data providers or aggregator platforms like Enigma, Crux, and Collibra
- Tools to help data scientists build A.I. models more efficiently
We’re different from the data aggregators in (1) because we not only help companies catalog data, but we go one step further by providing a large public catalog that we then organize into an ontology, and we help companies organize their internal data alongside the public data, allowing them to consolidate all of their data sources in a single place.
We’re different from the tools in (2) because we’re making predictive analytics easy for everyone to use, not just for data scientists.
What was the funding process like?
We built a relationship with our lead investor several months before we started actually fundraising. We kept them in the loop with our progress and they started to learn more about us, so after a few months of seeing progress, joining up with them was a natural fit. After that, it took a few weeks to fill up the round. Overall, we focused on building a great product, experimenting quickly, and focusing on customers, and the round came together as a byproduct of that.
We built a relationship with our lead investor several months before we started actually fundraising. We kept them in the loop with our progress and they started to learn more about us, so after a few months of seeing progress, joining up with them was a natural fit.
What are the biggest challenges that you faced while raising capital?
We wanted to make sure we found the right partners – people that believed in the team and the potential for what we’re doing. We ultimately decided that we’d focus on the product first, and then figure out how to raise funding after we had traction and we found the right people. Once we found the right partner, the rest fell into place.
What factors about your business led your investors to write the check?
They really liked our founding team and the space that we were focusing on (data and analytics). They saw the potential of all the applications of the technology that we had built, and we could build going forward.
What are the milestones you plan to achieve in the next six months?
We plan on closing several additional paid enterprise PoCs, hiring additional key personnel (head of marketing, head of sales, engineering), growing our user base for the cloud product, and getting to an ARR that can support a Series A fundraising process
What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?
This is something we dealt with ourselves for a long time. We started the company well before we received any outside capital. The main thing for us was to keep our expenses low and focus on what we knew we’d have to do to gain the traction we’d need to raise funds.
What’s your favorite restaurant in the city?
I love Tacombi for their great tacos and drinks.