Traditional, manual data mapping leaves the door open for large-scale human error and unreliable data quality. Intrinio combines powerful data quality infrastructure, advanced machine learning, and hyper-efficient human review to deliver financial data that you can trust.
Intrinio’s systematic approach to data quality relies on five main components:
Our data processing engine flags high-risk data and potential discrepancies and suggests fixes.
A data expert reviews flagged data and applies fixes before our users encounter the issue.
We won’t publish data with known, unresolved errors, so you can trust the data you retrieve.
Our system gets smarter with each filing, and we adapt to changes in the way companies file.
Users can quickly raise data quality concerns and speak directly to our US-based data experts.
“Working with Intrinio has been wonderful. When seeking a data partner, we sought a good fit, with open lines of communication. Intrinio has allowed us to be successful in developing our new data platform and has been a great partner in our joint product offering venture.”
-Business Valuation Resources
“We were able to establish an open and fruitful technical collaboration… Intrinio has a large variety of data, and quality is excellent. They are flexible and open to requests when we need customization and also very knowledgeable in fixing technical issues when they arise.”
“Intrinio’s team has great values in terms of work ethic and ambition… Customer support is really impressive, with a tech department that gets back to us within the day… You are going to get innovation, ambition, great values, great customer service.”
-Ziggma Portfolio Manager
“The documentation is clear, the data quality is good, and the support team is very reactive. The team is fast and is embracing innovation while keeping good quality data and reliable APIs, which is what we were looking for and failed to find in other data providers… We are looking forward to continuing using their services for the years to come!”
Our XBRL Standardizer leverages machine learning to standardize financial statements for easier consumption and comparison.
We built complex infrastructure for financial data, so you don't have to. Get a peek at the mechanics behind our data.