Transportation & Logistics | AI Development Services

Customer success story

Day & Ross reclaimed millions automating document processing

Scaling AI solution across all North American terminals 

Implementing computer vision maintains 100% accuracy, regardless of the driver's document format.

Day & Ross is successfully industrializing Pythian's AI solution across their entire logistics network in North America. This scale-up is fundamentally shifting the company’s operational model from manual, reactive data entry to a proactive, automated workflow. The solution now handles the immense complexity of diverse document layouts, handwritten notes, and high-volume terminal traffic without skipping a beat. As a result, Day & Ross has unlocked a repeatable framework for innovation that continues to drive efficiency as their shipment volume grows.

With support from Pythian's seasoned AI experts, Day & Ross leveraged innovative AI technologies to automate a previously manual document process. We expect the Pythian AI experts will continue to serve a critical role as we continue to innovate at Day & Ross.
Jeff Schnarr CIO at Day & Ross
Jeff Schnarr

Chief Information Officer (CIO), Day & Ross

10x

Faster processing speed

99%

Data accuracy

120

Documents  per minute

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From manual data entry to AI-driven document precision at scale.

With real-time visibility, Day & Ross shortened driver wait times and replaced error-prone manual entry with high-accuracy data.

MANUAL DATA ENTRY

Manual entry caused delays in shipments—impacting customers

Manual paperwork processing posed a challenge for the real-time data features of the new TMS, creating significant freight backlogs and delays.

INCONSISTENT DOCUMENT FORMATS

Complex formats inhibited processing efficiency

Inconsistent document formatting, including handwritten and upside-down notes, made it nearly impossible to process data quickly without a sophisticated solution.

HIGH DRIVER DWELL TIMES

Terminal bottlenecks impacted driver productivity

Trailer loads were frequently held back at terminals because of the time required for manual data entry, resulting in dwell times of up to 2 hours.

SCALABILITY CONSTRAINTS

Operational growth was limited by staff

The dependency on manual data entry meant that scaling operations required a linear increase in headcount, creating a barrier to exponential growth.

Scalable multimodal extraction with Gemini 1.5 Pro

Pythian leveraged Google Cloud and Gemini 1.5 Pro to automate data extraction from scanned bills of lading (BoL). This solution accommodates a range of inputs, including video and images, rather than just predictable text. By validating data against labeled datasets, Pythian ensured freight processing continued with persistent, accurate information. This automation is directly integrated with the company's TMS to create real-time shipment data.

AI landing zone

After a successful proof of concept, Pythian created a new Google Cloud landing zone to transform the tool into a business-ready system. This scalable configuration of Google Cloud products allowed Day & Ross to process thousands of documents daily across their entire network. The architecture ensured high-volume terminals remained efficient without requiring additional manual intervention. The production-ready environment provided the foundation for all future AI innovations at Day & Ross.

Integrated extracted data directly into the TMS

Pythian facilitated the direct flow of extracted document data into the existing transportation management system (TMS) to eliminate manual entry. This seamless integration ensures shipment details are updated in real-time, providing immediate visibility for dispatchers and customers alike. By bridging the gap between raw document scans and core business software, the solution minimizes delays in the freight lifecycle. This connectivity transforms static paperwork into actionable digital assets that drive the entire logistics chain.

Future-proofing logistics with a robust AI framework

Pythian designed a flexible framework that scaled the AI solution to enable the rapid deployment of new document models, such as invoices or customs forms, without disrupting existing workflows. By utilizing Google Cloud’s elastic infrastructure, the system maintains high performance even during peak seasonal volumes. This foundation not only optimized current freight processing but also provided a repeatable blueprint for company-wide AI adoption.

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