Key takeaway
Nellis Auction integrated Anthropic’s Claude across engineering, design, and project management as part of its AI adoption program with Qubika. Claude has accelerated day-to-day delivery, with the team delivering 40% more tickets than before adoption. Beyond that, one of its most valuable effects has been enabling teams to build additional capabilities for the platform. In this article, we provide the example of the traffic intelligence platform that demonstrates the shift: this was an additional operational capability developed alongside ongoing product delivery that now provides continuous visibility into production health.
Using technology to transform the reverse logistics industry
Nellis Auction operates at the intersection of e-commerce and retail reverse logistics, one of the fastest-growing segments in the U.S. supply chain sector. They acquire, transport, and sell new and used merchandise from major retailers including Amazon, Walmart, Target, and Best Buy. Headquartered in Las Vegas, the company helps retailers recover value from returned and excess inventory through its auction and re-commerce platform. They operate in Nevada, New Jersey, Arizona, and Texas, and employ approximately 1,200 people.
The platform spans a high-traffic web application and React-Native iOS and Android applications supporting product cataloging, search, live bidding, and pickup scheduling. More than 100,000 products are listed every day, creating an environment where engineering priorities are constantly driven by operational demands and customer-facing delivery.
Implementing Claude in development, design, and testing
Working with Qubika, Nellis deployed Claude Code, Claude Cowork, and Claude for Teams across four functions: Web Core, Mobile, Design, and Project Management. The engagement spans software development, design assistance, documentation, project management, and automated testing, and is structured across three phases: enablement, development and design acceleration, and sustained adoption.
Claude is applied across the full delivery cycle: better-refined tickets entering development, faster QA through test generation, and accelerated engineering work. Comparing delivery before and after adoption, the team now delivers 40% more tickets.
Using Claude to build a production traffic intelligence platform
One example of Claude’s impact is the development of a production traffic intelligence platform for mobile applications.
The system continuously analyzes traffic stored in BigQuery, using SQL to aggregate telemetry, identify anomalies against historical baselines, and present operational insights through a dashboard. It provides visibility into status code distribution by endpoint, traffic and error deviations from previous periods, application version adoption across Android and iOS, and the distinction between automated bot traffic and genuine customer activity.
Claude extends the pipeline beyond monitoring by interpreting the results. When anomalies are detected, Claude cross-references them against an internal knowledge base describing the platform and recent production deployments, producing likely explanations alongside the raw metrics. Instead of identifying that something changed, the system immediately provides engineering teams with relevant operational context, significantly reducing the effort required to investigate issues.
The significance of the project is not simply the technical implementation, but the way it was delivered. It was developed alongside normal product commitments rather than as a dedicated initiative, demonstrating how Claude enables engineers to deliver valuable operational capabilities without competing directly with committed roadmap work.
Assessment & conclusion
This example illustrates a broader pattern emerging from the adoption of Claude at Nellis Auction.
The immediate value of AI is often framed in terms of writing code faster or delivering planned features more efficiently. At Nellis, those gains are measurable (a 40% increase in tickets delivered). However, an equally important outcome is that engineering teams now have the bandwidth to invest in additional capabilities, such as internal tooling, observability, automation, and operational intelligence. Claude simply changes the equation by lowering the effort required to build and maintain them.
This traffic intelligence platform demonstrates how Claude is becoming an integral part of the engineering workflow at Nellis Auction, enabling teams to strengthen the platform and deliver against business priorities.
