Case Studies / PIPE AG – AI Ticket Scanner
Agriculture
AI
Business Optimization
Switchbox built a machine-learning-powered ticket scanning system for PIPE AG that uses computer vision and OCR to automatically extract, validate, and sync crop ticket data—eliminating manual entry, protecting farmer payouts, and integrating directly with PIPE AG's existing IoT ecosystem.
0%
Reduction in ticket entry admin time
~0
Data errors in early field testing
The Challenge
Manual entry cost farmers time and profit
Manual data entry has long been a tedious but unavoidable part of doing business in agriculture. Farmers who transfer and sell their crops rely on tickets that capture detailed product data—many of which still exist in paper form.
As the industry becomes more data-driven, these outdated processes create serious challenges. Precision is critical to ensure farmers are paid accurately for their yields. Yet with hundreds of tickets generated during harvest, each data point must be manually entered into online systems or spreadsheets. A single typo or misplaced number can mean the difference between being paid hundreds—or tens of thousands of dollars—less than earned.
In addition to costly errors, manual entry consumes hours of valuable time. PIPE AG set out to create a smarter, automated system to handle ticket data entry, saving farmers both time and profit.
Our Approach
Designing an AI-driven solution for agriculture
The Switchbox team evaluated PIPE AG’s existing IoT ecosystem and identified computer vision as the key technology to eliminate manual ticket entry at scale. We collaborated with PIPE AG to define the training dataset requirements, architect integration points with their existing field device infrastructure, and design a validation pipeline capable of running in real time from the field.
A custom AI model was trained on thousands of historical ticket entries to validate and map each field to the correct database record—building in accuracy at scale before the system was deployed.
What We Built
AI leveraged for automation
Switchbox developed a machine-learning-powered system that reads, interprets, and validates ticket data automatically—no manual input required.
The application uses computer vision and OCR (Optical Character Recognition) to extract data from scanned or photographed tickets. A custom AI model, trained on thousands of historical entries, validates and maps each field to the correct database record, ensuring accuracy at scale.
Built within PIPE AG’s existing IoT ecosystem, the integration allows real-time syncing between field devices and the cloud, ensuring that every load, weight, and delivery record is tracked and verified instantly.
As the technology continues to evolve, the team is expanding the AI’s capabilities to handle additional ticket formats, detect anomalies, and continuously improve its accuracy through real-world usage.
The Results
85% time reduction with near-zero errors
While this AI-driven solution’s full impact is still being understood, early results are incredibly promising. In early field testing, farms are already seeing the amount of admin time spent in the ticket-entering process reduced by over 85%.
Beyond time savings from entry itself, the tool has reduced data errors to nearly zero, helped streamline financial processes, maximized profit, and offered farmers peace of mind during the most demanding time of the year.
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