How Groundspeed Works

From information overload to data-driven decisions

Groundspeed’s AI accurately extracts and structures information locked inside your commercial insurance submission documents.

Our process improves quoting speed and accuracy to help you deliver more revenue.

And with reviews from trained specialists in the insurance domain, you can trust the data, not just your gut.

step 1

Document Ingestion

Insurance carriers send documents like insurance applications, broker emails, loss runs, and exposure schedules to Groundspeed via API, SFTP, or email.

Our pipeline can take in unstructured formats including:

jpg, .bmp, .gif, .pdf, .tif, .tiff, .doc, .docx, .xlsx, .xls, .csv, .xlsb, .xlsm, .xps, .oxps, .eml, .msg, .zip
Click here for a complete and updated list.

step 2

AI Data Labeling

Our AI classifies, annotates, cleans, and structures data from insurance documents, freeing your team from time-consuming data entry.

In today’s manual processes, the quality of that data is unreliable. Groundspeed uses automated systems and AI insights to supply a clear and trustworthy result.

step 3

Automated QA

Groundspeed monitors quality at the data point level through automated in-production quality checks and controls, which validate both extracted as well as predicted and calculated fields. Along with regular post-production audits, our QA environment ensures critical data point accuracy at a very high level for all underwriting processes.

step 4

Human Review

Our AI flags lower-confidence data and loops in a human to confirm accuracy. This way, you can trust that your data will come back to you in near-perfect form.

It also gives us unmatched flexibility—we can handle (nearly) anything that’s thrown at us. Submission documents come in many shapes and sizes. Systems that only rely on AI are limited to standardized documents they can confidently automate.

step 5

Enrichments

After the human review process, we normalize the data. Then, we use our proprietary predictive models as well as third-party data to enrich the data set. That means the output you receive is better and more complete than any individual source.