Intrinio was founded by innovators, for innovators. Continuing our history of powering startups, Intrinio has launched a new essentials program which makes it easier and more affordable than ever to use our data to power your next big idea.
Get real answers to semantic questions across the universe of US public companies. Using cutting-edge advancements in machine learning and natural language processing, our Answers API can read thousands of SEC filings just like a human would, but with exponentially greater efficiency.
Intrinio just launched its very first episode of Fintech, What the Heck?, a podcast dedicated to all things fintech. Host Andrew Carpenter recaps fintech news, and chats with two fellow Intrinians, senior content creator Chelsea Caltuna and co-founder and CEO Rachel Carpenter.
We identified trending beliefs and values from S&P 500 companies using our propriety alternative data engine, Thea, which processes data sourced from recent SEC filings.
We used Intrinio’s alternative data, powered by our proprietary Answers API, to discover what human rights challenges companies are facing. Here are the top 10 human rights challenges among S&P 500 companies, as reported in their SEC filings.
Intrinio has developed Thea, an AI search and question answering engine capable of answering any question about companies using all SEC filings. In this article, we will walk through the process of using our new Company Answers API endpoint to answer questions about S&P 500 companies.
Run your business like an S&P 500 company. No, really. We leveraged our proprietary alternative data engine to generate this list of why S&P 500 companies consistently lead their industries on all fronts, with data sourced from SEC filings.
We’ve leveraged Intrinio’s alternative data, powered by our proprietary Answers API, to determine the top sustainability challenges that S&P 500 companies are facing. Here are the 10 biggest sustainability challenges that companies have reported in recent years.
How has Intrinio automated the data supply chain to provide higher-quality financial data, faster, at a more competitive price? The secret is machine learning. In this blog series, we will be offering a high-level overview of what machine learning is and how it can be used to analyze and interpret financial information.
Deciding what data your company needs often starts with the decision between real-time or delayed market data. Here’s a guide to access methods, exchange fees, integration, and other factors that influence whether you need real-time or delayed equity and option prices.