Ambiguous data transformed into consumable insight.

We employ a robust process to turn your unstructured documents into data that is ready to use.

The Groundspeed Approach to AI

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.

Submission document data, auto-detected

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.

Parsed & organized

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.

Extracted and auto-classified

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.

Normalized, enriched, & transformed

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.

Client Success Stories

We deliver ROI from better underwriting, improved analytics, and faster speed to market




Revenue cycle automation expands time to review new business

Added capacity
Net Savings
  • Improved initial pricing turnaround by 95%
  • 10% increase in underwriter time to review target accounts




Groundspeed removes human error and enables advanced risk analysis

Annual Net Savings
  • Improved data accuracy from 88% to 98%
  • ML model-based claim data enrichments
  • Better risk categorization




Groundspeed eliminates data extraction and entry tasks

Annual Net Savings
  • Improved initial pricing turnaround by 95%
  • 10% increase in underwriter time to review target accounts




Groundspeed reduces time to quote by days

Premium Increase
  • Faster turnaround and increased access to data increases win rate.
  • For every $100m in potential premiums, there is an increase of $10m in premiums.

Unlock the value in your unstructured data

And understand the real risk, faster

Request a Demo