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.
We’ve spent years building an extensive network of data quality technology and infrastructure – but we’re not keeping it to ourselves. Our engineers can also build data quality infrastructure for enterprises that need higher quality for their own financial datasets.
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.
