Boulder Equity Analytics started with two projects. In the first, for Overstock.com, Kevin was tasked with standardizing the financial data for a set of competitors and building interactive dashboards to analyze the financials. In the second, ai-one built a set of agents as a POC for Generation IM to use A.I. to measure sustainability criteria in financial and legal documents for portfolio companies. The combination was the seed for a new family of analytics.
Then while training agents and running the AI platform for a NASA project, Thomas began building the financial database for competitive intel and financial services. Excited about the prospects, he founded BEA and convinced Kevin and I to join. We sold our first client in October.
Our mission is to build an intelligent, enriched and fully interactive database from all the publicly available reports to improve the productivity and insight of the analyst covering an industry sector. Our service will be better because it will be:
Convenient - everything in one place and easy to use, including downloadable data for further analysis. This is the foundation of our value proposition.
Comparative - charts will compare results over time, between competitors, include rankings and side-by-side tables.
Contextual – filings, transcripts and financial notes will be contextually available within the financial dashboards with a simple click for fast interpretation.
Interactive – visualizations will be fully interactive so the analyst can develop the views and path through the data that’s most relevant and answer questions from management on demand.
Financial data on sector competitors comes from SEC filings (10q/10k) via companies such as Dow Jones FactSet, Edgar Online, ThomsonOne and Bloomberg who provide aggregated financial data through a service, via Excel download or an API “firehose” that requires programming resources to navigate. These services are too expensive.
Of course you can download financials from company investor relations or the SEC site for free. This is too time consuming. None of these options solve the mission.
But that’s just the numbers. There is an enormous amount of information in the filings and earnings transcripts that needs to be collected and analyzed.
To meet our goal, we had to 1) standardize the data, 2) develop the visuals, charts and scenarios, 3) load and analyze the latest data, 4) use A.I. to “read”, classify and link the relevant text to the financials and 5) put it all in an interactive solution. Our first client is happy and we’ve just begun to explore the next level of value we can provide.
We want to create the best kind of financial math: spend less than $18,000 to make your $100,000 analyst worth $200,000.
If you're reading this, thank you. Please register, comment and help us with our mission. Tell us what you'd really like to see that would make your work easier and your analysis even more brilliant.