[shum1]

As someone who has looked into alternative data business models for the finance industry, this is really awesome to see someone doing this as a company. I was interested to understand how you think about your revenue model? I feel that if your data provides alpha (i.e. selling before other people are aware of the problematic disclosures), as your models become validated within the industry, someone/some firm is going to use it to generate alpha. But then you have a problem where, that one firm that captures most of the value, and takes it from other participants who now lose the value-add of your product.

How do you balance those two sides? I mean it as a potential customer who would love to pay for your product, but want to understand how you prevent this becoming a alpha-generating NLP strategy for one hedge firm who pays the most for it.

[HeatherJudd]

It's definitely something we've discussed as a team. We would like to help make fraud less profitable.

The current iteration of the product requires users with some level of financial expertise - hence why we are starting with fundamentals investors. We believe that the longer a fraud goes on, the more people get hurt - so we want to bring these issue to the forefront. Perhaps each trade can be considered a zero-sum game, but long-term there is a benefit to all market participants. We love the idea of every investor considering how aggressive accounting/reporting informs management integrity. Unfortunately, I think we have some time before this becomes the norm.