Overview
Hirschbach is a temperature-controlled trucking and logistics company operating a large, multi-location network that supports time-sensitive, compliance-heavy refrigerated freight. To keep freight moving effectively, technology plays a central role across the organization, powering everything from dispatch and planning to driver workflows, billing, and settlements. As part of its modernization efforts, Hirschbach focused on reducing its reliance on legacy workflows and building an API-driven ecosystem to improve speed, reliability, and user experience.
The core focus of this digital transformation was document-heavy revenue and operations workflows, particularly billing and settlements. Hirschbach handles critical documents such as bills of lading (BOL), proof of delivery (POD), lumper receipts, rate confirmations, and accessorial documentation, all of which directly impact cash flow and invoice accuracy.
Prior to implementing a new solution, Hirschbach’s legacy process created operational inefficiencies, including manual effort, inconsistent document quality, limited exception visibility, and delays in billing readiness. Driven by internal operational pressure, growth needs, and customer demands for faster billing, Hirschbach sought a new foundation to support scalable, AI-driven workflows. As part of its modernization efforts, Hirschbach focused on reducing manual work, improving process visibility, creating a more scalable foundation for document automation, and supporting continued growth more efficiently. .
Challenges
Hirschbach manages a high volume of shipment-related documents across multiple operational and back-office teams. These documents include bills of lading, proofs of delivery, receipts, rate confirmations, and accessorial documentation. Prior to modernization, the process required significant manual review to classify documents, associate them to the correct shipment, and validate key billing information. This created delays, limited process visibility, and placed additional administrative burden on employees and drivers.
The most problematic inputs for the team included handwritten fields (such as signatures and quantities), multi-page packets with missing pages, and customer-specific formats where the same data appeared in varying layouts. Before modernizing the process, documents required heavy manual review to classify, index to the correct shipment, and key in billing fields. Because of this manual drag, the turnaround time from document receipt to billing readiness was frequently delayed, averaging 9 days to bill.
This legacy approach created significant operational and financial inefficiencies:
- High Costs and Cash Drag: Repetitive manual work drove up labor costs, while slower invoicing directly impacted cash flow.
- Compliance and Accuracy Risks: Manual workflows increased the risk of misfiled documents, inconsistent application of billing rules, and limited audit traceability.
- Employee, Driver, and Customer Friction: Back-office staff carried the burden of repetitive data entry and exception handling, leading to frustration. Drivers, meanwhile, faced administrative friction and repeated requests to resubmit documents. Downstream, billing delays created status confusion and friction with customers.
Solution
To solve these challenges, Hirschbach chose the Hyperscience Hypercell platform. Hyperscience was selected for its enterprise-grade Intelligent Document Processing (IDP) capabilities, strong accuracy across variable document formats, integration friendliness, and practical human-in-the-loop model for handling exceptions. Crucially, it allowed Hirschbach to take control of its own user and customer experiences as part of a broader billing automation strategy.
The technical implementation centered on building a reliable, observable document automation pipeline. The solution automatically ingests documents from existing channels, classifies and extracts data using Hyperscience, and normalizes the output into structured JSON payloads for downstream delivery via API. This pipeline integrated seamlessly with Hirschbach’s core billing workflows, legacy IBMi/AS400 components, and a new homegrown operating workflow system called “Connect.” Security was prioritized through role-based access, encryption, and strict logging to ensure the automation didn’t become an unmanageable “black box.”
With Hyperscience, the workflow fundamentally shifted from “humans do everything” to a managed exception model. Today, high-confidence fields and documents flow straight through the system automatically. Low-confidence or ambiguous data is routed to a validation queue, where human validators make corrections that subsequently feed continuous model improvement. Hirschbach leveraged the workflow and orchestration capabilities in the Hypercell to configure the system to meet their needs – including custom field mapping, document type definitions, and customer-specific routing logic.
Benefits & Results
Since implementing Hyperscience, Hirschbach has seen highly measurable improvements across processing speed, automation rates, and manual effort reduction. Hyperscience has helped Hirschbach eliminate the document processing backlog, and plays a vital role in enabling the company to continue to cost-effectively scale operations as the company continues to grow.
Accelerated Processing & Cash Flow
- Faster Billing: The most significant KPI improvement was the reduction in “days to bill,” which dropped by over 60%, from an average of 9 days to just 3 days.
- Rapid Turnarounds: Documents are now processed and returned from Hyperscience in as little as 10 to 15 minutes, drastically faster than prior legacy imaging processes, which took up to 4 hours. Hirschbach has also improved data visibility and process transparency. Through integration with the company’s Connect platform, teams now have clearer insight into document status (e.g., received, in review, ready to process), enabling faster identification of exceptions and reducing the risk of missed or delayed documents. This has been especially important in addressing prior gaps where missing documents were only discovered reactively.
Increased Automation & Accuracy
- Overall Classification: Overall document classification accuracy has reached approximately 98-99%.
- Signature Detection Enhancements: By applying an inference layering approach that combines specialized models and the ORCA, the vision language model from Hyperscience, signature detection accuracy jumped from the mid-60% range to over 77%. This reduced manual reviews of the documents by approximately 47 hours per week, and ensured only true exceptions were routed to human staff.
- Model Retraining Impact: Ongoing model retraining and PO / BOL matching enhancements increased overall automation by an additional 6.3%.
Operational Efficiency & ROI
- Massive Time Savings: In 2025 alone, Hirschbach realized over 288 hours of manual effort savings per week across billing workflows. The company estimates an additional 244 hours per week of savings in the first half of 2026.
- Eliminating Backlogs: The automation led to a massive drop in manual task queues. In one example, manual task hours dropped from 111 hours in January to just 34 hours in February—a ~70% month-over-month reduction.
- Elevating the Employee and Customer Experience: Staff hours are no longer viewed as pure reductions, but are instead being redeployed to higher-value tasks, such as exception quality, continuous improvement, and customer-specific tuning. Just as importantly, Hirschbach customers are benefitting from faster, cleaner, more transparent billing.
Conclusion
Strategically, implementing Hyperscience has provided Hirschbach with a scalable document automation foundation that dramatically improves speed, accuracy, and auditability. It acts as a major catalyst for the company’s long-term technology roadmap, accelerating the shift away from legacy workflows toward an AI-native, automation-first operation where documents are treated as structured data rather than “dead images.”
An unexpected but massive benefit of the solution has been process visibility. By moving work into structured queues, Hirschbach now has deep insight into where processes break and why exceptions occur, empowering the team to tackle upstream improvements proactively.
Looking ahead, Hirschbach plans to expand this automation foundation into other manual, document-centric workflows, including claims and Over, Short, & Damaged (OS&D) documentation, customer compliance packets, driver onboarding, and maintenance paperwork.For other logistics organizations considering a similar transformation, Hirschbach’s advice is clear:
“Start with a high-value, document-heavy workflow tied to cash flow. Design for exceptions early, involve operations deeply, measure everything, and don’t underestimate change management. The goal isn’t AI for AI’s sake; it’s cycle time, quality, and scale.”