W.R. Long ships hydraulic valve kits that connect and power tractors, moving orders through a four-station process from pick to pack. On the operations side, one of the organization's leaders with a software background built the company's order-fulfillment tracking app himself, with AI helping close the gap between the time he had and the software he needed. It shipped, and it worked. What he didn't have was the time to keep building and maintaining it while running the business.
He brought the app to RoleModel for a codebase assessment: an outside, expert read on what he'd built and whether it would hold up.
The assessment surfaced a real risk. The application worked, but it had zero automated tests. Every change was a bet that nothing else would quietly break somewhere. RoleModel proposed several options, and recommended the smaller, cheaper path: patch what existed and hand back a punch list.
W.R. Long chose the more extensive option instead. Not because the smaller fix wouldn't have worked, but because he didn't want to keep managing an application on top of running his business. He wanted a partner who could take the software off his plate entirely, with enough confidence in it that he could stop thinking about it.
The scope: build a new section of W.R. Long's application, one he hadn't built yet, as a standalone application tested from the ground up, in two weeks.
Because W.R. Long had already built and used his own version of the surrounding application, RoleModel wasn't starting from a blank requirements document. The team could read his repo directly: which Salesforce field mapped to which data point, what the business rules already were. That existing codebase didn't carry over into the new section directly, but the concepts did, and that removed weeks of the back-and-forth extraction conversations a written spec would have required. It's the same thing RoleModel looks for on every project: a partner's own domain knowledge, folded into the software, just lifted from working code this time instead of a conversation.
The new application follows hydraulic valve kits through W.R. Long's four physical stations. At station one, staff pull up the order and print a label on a dedicated Zebra printer; the label rides inside the box rather than on it. Stations two through four can pick up the box in any order and scan it on arrival. Each scan logs a timestamp, giving W.R. Long something they didn't have before: visibility into which handoffs between stations are running long. At the final station, a standard networked printer produces the customer's packing and shipping documents, and a connected scale pulls the box's weight straight into the system. A Salesforce integration ties the whole workflow back to the order record.
Four physical devices and a CRM integration, running together, in two weeks. Underneath all of it, RoleModel built solid architectural patterns and a full suite of automated system tests, a guardrail W.R. Long’s original app didn’t have. The patterns set the direction, and the suite keeps everything on course: when something new gets added, whether by W.R. Long's leader or by RoleModel, with AI or without it, the work picks up the conventions already in place and the tests catch anything that drifts. That's what lets W.R. Long hand the work off, without wondering what the next change might break.
W.R. Long has a tested, working, end-to-end MVP that took two weeks to build. Comparable scope without AI-assisted development would typically run $50,000 to $75,000; this shipped with one designer and two developers, one of whom focused on review.
What W.R. Long has now that it didn't have before isn't just new software. It's a tested foundation, production analytics for its own fulfillment process, and a partnership still being built, with more refinement to come as the team moves toward full rollout.
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