Driver-submitted documents extracted and loaded for verification without manual intervention
Turvo, Boomi, Snowflake, and Retool integrated into a single event-driven workflow
Streaming ingestion and scheduled processing ready for higher document volume
.png)
A growing third-party logistics company relied on a manual process to review documents submitted by truck drivers, the kind of operational drag that compounds with every new lane, customer, and load. As volume grew, the review work consumed more analyst time and slowed the broader operation.
The company needed an automated solution that could extract and verify document data at scale without adding headcount, and that would hold up as the business continued to expand.
OneSix designed and built an event-driven document processing workflow spanning the company's transportation management, integration, data, and application layers. Boomi subscribes to a Turvo webhook that uploads driver-submitted files into a Snowflake stage. A stream tracks changes on the stage, and a scheduled task periodically invokes a procedure that runs the Snowflake Document AI prediction model and loads extracted data into a structured table.
A Retool application surfaces the extracted data for client review and verification, giving operators a single interface to confirm or correct results before the data flows downstream. The full pipeline has been end-to-end tested, with each component working together without manual intervention.
The result is an automated review process that replaces ad hoc manual work, a clear path to verifying data at scale, and an architecture positioned to absorb higher document volume as the company continues to grow.
More to explore

OneSix built an AI/ML cardiovascular risk prediction calculator that supports early patient intervention across care, research, and population health.

OneSix designed and implemented a computer vision-based product identification pipeline capable of rapidly identifying products on grocery store shelves.

OneSix built an AI product recommender that maximizes conversion and lets non-engineering teams tune it through low-code tooling.