Ambiguous data transformed into consumable insight.
We employ a robust process to turn your unstructured documents into data that is ready to use.
We employ a robust process to turn your unstructured documents into data that is ready to use.
Deep domain experience enables us to expose the necessary building blocks of effective automations in order to serve customers with a scalable and repeatable process that can handle the full scope of commercial insurance submission data.
The Groundspeed approach provides an essential and systematic way to take a broad set of unstructured or semi-structured documents and transform it into actionable, enriched data sets that are ready to use.
Data presents itself in thousands of ways and traditional, robotic processes are not keeping up with the broad scope of this material in a meaningful way.
Data is initially extracted from the document using several methods like Optical Character Recognition (OCR) or directly reading it. Sources can include PDF and Excel files, but our advanced technology can accommodate data in nearly any structured or unstructured format.
Intelligent algorithms combine the full results from all of your documents to create an intelligent sum of information that boasts higher quality than any one individual source. This capture includes raw details, spatial characters, and other details important to processing.
After your data is initially input to our platform, a routing engine analyzes the documents based on what sort of information is contained and how it is represented.
At the same time, text-based natural language classifiers and image-based convolutional neural networks are hard at work bringing organization to the documents and adding detail to help understand both what the data contains and how it is being represented. This decides which automated workflow each set of documents is processed with.
Once your documents reach the right automated workflow, additional features of the data are exposed that can be leveraged in downstream processing.
To guide this process, we use classical machine learning, deep learning, and other information analysis and retrieval algorithms.
To ensure accurate and comprehensive annotation and structuring of the data, we put a human in the loop, where an operator can be alerted to a suspected error. Any corrections made are captured for future learning and iterations of our Automation AI or other data-driven aspects of the workflow.
By now, data has been extracted, annotated, structured, and is ready for the final stages– normalization and enrichment.
With diverse data sets like the ones we process, creating a standardized representation of data from patch-worked sources is a complex process. For that, we call on an extensive collection of normalization models we have developed by processing millions of records.
Data is ready to use immediately, but you can further enhance what you need from our suite of enrichment features like predictive analytics, natural language understanding tasks, or connecting it to data from a third party.
We deliver ROI from better underwriting, improved analytics, and faster speed to market
ROI
TOP LINE GROWTH
Revenue cycle automation expands time to review new business
ROI
BETTER UNDERWRITING
Groundspeed removes human error and enables advanced risk analysis
ROI
LOWER HEADCOUNT
Groundspeed eliminates data extraction and entry tasks
ROI
INCREASE WIN RATE
Groundspeed reduces time to quote by days