Escaping the Busywork: UsingData to Automate Low-Value Tasks
"We mistakenly believe that reading the fine print on every single routine order is a human job. It isn't. Humans are beautifully equipped to handle nuance, but we are disastrously bad at spotting microscopic changes in repetitive text. The real human job is deciding what to do once the machine spots the change."
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In manufacturing, growth is a double-edged sword. When a business scales, every underlying process is amplified. As we like to say at Razor: the good gets great and profitable, while the bad gets worse. Consider the humble purchase order. In the early days, managing incoming orders is relatively easy. A human reads the document, translates the unique quirks of each client's formatting, and manually processes it.
But as a manufacturer grows, the volume of these unstructured documents skyrockets. Every client uses a different format. They refer to products or quantities in slightly different ways. To a human, deciphering these variations is intuitive-but at scale, this manual translation becomes a costly, sluggish anchor on operations.
It’s the perfect area where a marginal gain would make a massive bottom-line impact. Yet, because of the sheer complexity and the human intuition historically required to understand them, this process was viewed as far too difficult to automate.
Data isn’t just for populating beautiful dashboards or forecasting trends for decision-making. By approaching the problem laterally, we realised that purchase orders are essentially just images.
Razor engineered a solution to turn these static images into dynamic, automated workflows.
Phase 1: Visual Dissection
Using advanced computer vision, we bypassed the messy formatting by dissecting the images of the purchase orders into known, structured areas.
Phase 2: Meaningful Extraction
With additional processing and Natural Language Understanding (NLU), we extracted the complex, unstructured text and translated it into meaningful, actionable information.
Phase 3: Backend Automation
By training a bespoke model using the manufacturer's historical purchase orders, the system learned to independently understand, process, and seamlessly place orders directly into the backend systems.
By letting computers do what they are exceptionally good at-long, tedious, and repetitive tasks-we fundamentally shifted how human talent is deployed.
Frictionless Processing: The system now autonomously processes and places complex orders, removing a slow, costly administrative bottleneck from the manufacturing supply chain and unlocking people's time.
The Compliance Bonus: A powerful side-effect emerged. Each purchase order comes with heavy Terms & Conditions. People rarely read them every time, and if they did, they’d struggle to spot a subtle alteration. The data solution, however, never gets bored. It instantly highlights any changes in the terms and flags the order for review.
Strategic Human Intervention: The machine doesn't guess what action to take when T&Cs change. It leaves the critical thinking-reviewing, understanding, accepting, or negotiating-exactly where it belongs: with your people.
This wasn’t just an exercise in optical character recognition; it was about unlocking trapped potential. Razor’s ability to combine computer vision, NLU, and seamless backend engineering turns a sluggish administrative burden into an intelligent workflow. We don't just build software; we engineer time, freeing your team to add real human value where it matters most.
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