Data entry ops · Production

SafeRide Health automates driver and vehicle background verification with Nanonets OCR, reducing manual workload by 80%

The problem

SafeRide Health manually processed up to 16 different document types per driver and vehicle and manually entered the relevant data into Salesforce. As demand for their non-emergency medical transportation service grew, the process became cumbersome and unscalable.

Workflow diagram · grounded in source
1
Document import via ShareFile
trigger
“All vendors securely send their documents through ShareFile, which are automatically processed by Nanonets”
2
Document type classification
ai_action
“Nanonets' classification model intelligently identifies each of the 16 possible types of documents shared and directs it to the relevant OCR model for the data to be extracted”
3
Data extraction via OCR
ai_action
“Nanonets' trained custom OCR models identify specified data points from different types of documents and extract them with high accuracy”
4
Validation and discrepancy flagging
validation
“The files then pass through validation checks put in place to weed out discrepancies, based on logics defined by SafeRide Health. In case of an error, the file is flagged and a member of the SafeRide team is notified and the files with n…”
5
Human review of flagged files
human_review
“The files with errors are redirected to a dedicated "pending" folder in Salesforce, to be manually vetted by the SafeRide team”
6
Export clean files to Salesforce
integration
“the files with no errors are mapped to their respective Salesforce folder”
Reported outcome

Nanonets automated the end-to-end credentialing workflow, reducing manual workload by 80% and increasing team efficiency by up to 500%, with 80% of files approved automatically without human intervention.

Reported metrics
Manual workload reduction80%
Team efficiency increaseup to 500%
Files auto-approved without errors80%
Reported stack
NanonetsShareFileSalesforce
Source
https://nanonets.com/customer-success-story/saferide-health-automates-background-verification-process-to-manage-partner-riders
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Nanonets automated the end-to-end credentialing workflow, reducing manual workload by 80% and increasing team efficiency by up to 500%, with 80% of files approved automatically without human intervention.

What tools did this team use?

Nanonets, ShareFile, Salesforce.

What results were reported?

Manual workload reduction: 80%; Team efficiency increase: up to 500%; Files auto-approved without errors: 80% (source-reported, not independently verified).

How is this data entry ops AI workflow structured?

Document import via ShareFile → Document type classification → Data extraction via OCR → Validation and discrepancy flagging → Human review of flagged files → Export clean files to Salesforce.

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