Today I’d like to summarize our research in application of AI technologies in VC industry.
The main conclusion is that the market is still in the very beginning of its journey, and we are not seeing a large adoption and most likely will not see it since the entire industry still lives on warm inbound connections. However, there are several tasks or core cases when data-driven approach may also be appropriate:

1. Intelligence or identification - when the investor wants to conduct a macro screening of markets, founders or companies and find general information about how a particular segment or company is developing.
2. Research work - when the investor monitors already identified opportunities and conducts a deeper analysis, scoring or additional information that will help make the final investment decision.
3. Current portfolio interactions - when the investor has already made some investments and does not have enough data or time to track how these investments are developing in accordance with his expectations.

Each of these cases can be closed with current solutions, for example, points 1 and 2 through Pitchbook.com, Harmonic.ai, CBInsights, etc., and point 3 through edda.co or
peachscore.com, but all of them are either very expensive - $30k+ per year, or do not close all the functionality, so what is the next big thing of the product that can work through all 3 cases on the nascent data-driven trend? In our opinion, it is the ability to provide market signals and sentiment analysis that can show the activity of a particular company in the market, compare it with successful patterns and build various predictive models.
To our minds, sentiment analysis is systematized information about the connection and context the company is mentioned in, what its digital footprint is, including user comments, publications of the founders, custom PR articles to raise the round, etc. All this can be analyzed and compared with the most successful benchmarks, and this can greatly help all current players, including small funds and business angels, when making certain investment decisions.
And finally, the search for PMF and a market niche for Wale.ai
led us to think about where and how web3 and AI technologies will fit together and how they will affect the investment decision-making process in general. The topic is vast and interesting, and I hope to write a little more about it in the following parts, but it is obvious that there are not so many intersection points yet, for example, the monetization of digital assets, i.e., company data will represent a special type of assets, and they will have to be stored in distributed and verified networks, since the world around will be full of fake data generated by the same AI networks, and only distributed registries and trained AI algorithms will be able to understand where the truth is and where the fake is, so investors will look for sources of verified information and data