- Use Cases
Ours is not a 'one-size-fits-all' kind of solution. A little customization to address your ground-level problems could take your team's efficiency from good to mind-blowing
This enables the humans in your team to do intelligent work by enabling them to do more than just entering data.
Legacy OCR’s are not built for handling documents that have different variations and multiple languages. They slow down the processing and the accuracy also goes for a toss. At Infrrd, we build machine learning models that learn from different variations and can process multiple languages. In fact, we built a model for one of our clients that could handle over 2.1 million potential variations saving them enormous amounts of time and money.
The technology used by legacy OCR vendors can make it difficult to process complex documents. Infrrd’s intelligent proprietary platform allows customers to classify, extract from, and also correct the extracted documents. Our correction models make for flexible, trainable and configurable solutions with absolutely no limits.
Around 80 percent of corporate enterprise data is semi-structured or unstructured (Gartner) and processing these documents accurately can be challenging. Extracting data from documents by different vendors from different sources is made easy with our prediction model. We are the fastest platform for semi-structured documents. Ask anyone (even our competitors)
We configure solutions that work best for your organization and these solutions keep getting better with time. Machine learning retraining and Human-in-the-loop corrections ensure that our platform improves continuously. Accuracy and processing speed improve continuously over time helping you reap better ROI.
Unlike data extraction technologies that are template-based, our proprietary platform doesn't guess. It learns from the millions of variations we train it with and predicts where extractable data is present. The result? faster and more accurate extraction, every. single. time.
We get it, document processing is complex because documents are complex. You need way more than OCR to handle them and guess what, we already have a lot of successful examples of how we've done it with our proprietary platform.