we got started by solving our own problem


As first-time founders, learning to raise was an uphill battle

When we were getting started, there was no seamless or interactive platform that we could use for self-learning. We ultimately learned by reading articles and running experiments via trial-and-error.

In hindsight, we found this to be a particularly inefficient and ineffective way to learn.

Our unique experience left us with unique insights on this problem

From 2019-22, our team raised $120m in venture financing across five rounds in a frontier market that lacked precedence for venture-backed startups.

In our experience, due to specific factors that are common to most startups, we felt that every round was an uphill battle. Today, we are working on building an "operating system" for fundraising that can make the process a lot easier for other founders.

investor discovery

We had no way to identify the most relevant investors

In raising capital, we realised the need for a data-driven approach to identifying investment firms that would be most relevant to our business.

We learned that the right software tools were critical in enabling founders to study the investment behaviours of 2,000+ VC firms to identify ones that were most likely to invest at our stage, sector and geography.

With our last startup, over 70% of the firms we engaged were not a good fit for our sector and/or geography. Upon research, we found that our experience was representative of that of most startups.


Despite vast networks, our team struggled to identify intro paths

Despite having a cap table with deep networks, we had no way to determine who could connect us with a given investment firm. This led us to be a lot less intentional in our choice of firms that we approached.