Hirschbach Motor Lines implemented AI-powered intelligent document processing to automate freight documentation workflows, reducing its days-to-bill cycle by over 60% from nine days to three days and saving 288 hours of manual work per week.
- Hirschbach achieved a 70% reduction in manual task hours between January and February 2026 by implementing AI document automation
- Document processing speed improved dramatically, transitioning from driver submission to review in as little as 5 minutes instead of upward of 4 hours
- The automation achieved 98-99% overall document classification accuracy using Hyperscience's Hypercell platform
- Hirschbach plans to expand automation to additional workflows including claims, driver onboarding, maintenance paperwork, customer compliance packets and more
Refrigerated freight and temperature-controlled trucking company Hirschbach Motor Lines (CCJ Top 250, No. 44) has been implementing automation within its operations over the past year and some change to the tune of 288 hours of manual work saved per week during 2025.
The company saw a 70% reduction in manual task hours between January and February this year, with expectations to nearly double its weekly time savings in the first half of 2026 compared to 2025.
The initial proving ground was billing, said Hirschbach Chief Technology Officer Ivan Ramirez.
Hirschbach developed an operating workflow platform called Connect to give its business teams a centralized operational workspace to manage document-driven workflows, review exceptions, track processing status, and seamlessly bridge automated document processing with downstream business operations such as billing and settlements. Then it integrated with Hyperscience, an AI infrastructure software that focuses on Intelligent Document Processing (IDP).
“Before implementing Hyperscience (Hypercell), Hirschbach relied on legacy document intake, imaging and operational workflow tools. The process was heavily dependent on manual review, manual validation and fragmented handoffs between systems,” Ramirez said. “Staff frequently had to manually classify documents, index or associate them with the correct shipment, and review them line by line for key billing information.”
Hirschbach has since shifted to a managed exception model using the Hyperscience Hypercell platform to automate processing of documents like bills of lading (BOL), proof of delivery (POD), lumper receipts, rate confirmations and accessorial documentation.
Hypercell leverages Hyperscience’s intelligent inference layering approach, which combines specialized models with its proprietary Vision Language Model ORCA to ingest, classify and extract data from highly variable formats. This normalizes the output of unstructured documents and turns them into structured JSON payloads for downstream delivery via API.
In this integrated workflow, Ramirez said Hypercell handles the underlying processing and validation of documents, while Connect provides Hirschbach’s teams with clear visibility into where documents sit in real time: received, in review or ready to process.
“The impact of this combined setup where Hyperscience handles classification, extraction and validation, while Connect manages routing and workflow, is most evident in processing speed,” he said. “Before implementation, it could take upward of four hours for a document to move from driver submission to operations review. Today, documents that clear automated business logic checks transition from driver submission to operations in as little as five minutes.
“While loads with exceptions are still routed for manual review, the total volume requiring human touch has dropped significantly,” he added.
The financial outcome
While billing was the initial proving ground, Ramirez noted that Hyperscience and Connect provide a scalable automation foundation that enables Hirschbach to extend into other document-heavy workflows over time, further increasing time savings.
It already has.
Since initial implementation, Hirschbach's calculated time savings represented the impacts achieved by deploying these capabilities across the broader billing and settlements process, not just billing alone.
Expanding automation to additional freight documentation has not only created additional time savings but has also improved the company’s bottom line. Hirschbach has reduced its "days-to-bill" cycle by over 60%, dropping from an average of nine days to three.
Ramirez said the biggest benefit has been improved cash-flow velocity — a meaningful advantage in freight operations, where margins are tight and working capital matters.
“Faster billing improves liquidity and gives the business more financial flexibility to support growth, equipment needs, driver programs and customer service without adding unnecessary back-office burden,” he said. “The value is not only that the team is doing less manual work. It is that revenue can move through the business faster, exceptions can be identified earlier, and billing can operate with more consistency and transparency.”
He added that this enables Hirschbach to build a scalable operating model capable of managing higher volumes without increasing headcount.
A win, win, win situation
Hirschbach’s back-office staff, drivers and customers have all gained from the benefits of this document automation pipeline.
For customers, the gain is faster, cleaner and more transparent billing, Ramirez said. Accelerating overall invoice readiness and maintaining more consistent documentation minimizes billing confusion and eliminates unnecessary payment delays caused by missing or mismatched paperwork, he added.
For drivers, the core expectation remains the same: submit complete and accurate documents as soon as possible through the approved process, Ramirez said, adding that automation functions best when documents are submitted through the correct channels.
Using Hypercell, Hirschbach has achieved 98-99% overall document classification accuracy.
Ramirez noted that issues with missing, incomplete or unclear paperwork are now identified much earlier, preventing constant resubmission requests to drivers and repeated back-and-forth follow-ups.
Additionally, with the foundational work on back-office workflows largely complete, Ramirez said Hirschbach is now positioned to apply this same approach to driver-facing workflows to directly reduce the overall administrative burden required of drivers to complete a load.
Hirschbach also plans to expand this automation into other manual, document-centric workflows, including claims, Over, Short, & Damaged (OS&D) documentation, customer compliance packets, driver onboarding and maintenance paperwork, further improving back-office operations.
For back-office staff, employees are no longer wasting valuable hours searching for paperwork, trying to locate bottlenecked work, or manually reviewing every single line item, he said.
“Instead, staff time is being meaningfully redeployed toward higher-value work that truly requires human judgment, such as exception quality, customer-specific requirements and continuous process improvement,” Ramirez said.
The inference inflection point
Hyperscience CEO Andrew Joiner said Hirschbach’s success is a textbook example of the inference inflection point: the shift in AI from training models to actively running them in real time to reason, take actions and perform productive work.
Ramirez noted that Hirschbach’s transformation was not a case of implementing AI for AI’s sake.
The company pinpointed an AI use case — improving cycle time, quality, visibility and scale in document-heavy workflows — tied directly to cash flow.
He said Hirschbach’s ongoing efficiency gains comes from continuous model retraining, operational tuning and improved exception handling. That process has also benefitted Hypercell, said Xabier Ormazabal, vice president of product marketing at Hyperscience.
“Hirschbach has been an invaluable innovation partner because of both the complexity and scale of its operations,” Ormazabal said. “Working together has helped us further refine Hypercell for transportation-specific workflows, including processing document packets that contain multiple freight document types, validating information across documents, and supporting the exception-based workflows required in real-world logistics operations.”
