A regional fiber-based internet service provider on the East Coast ran a 10-person back-office dispatch team that had become a chronic bottleneck in their field service operations. Field technicians were required to call into the dispatch office for equipment health checks, troubleshooting guidance, and work order closure — a process that left technicians sitting idle in their vehicles for 30 to 45 minutes after completing 15 minutes of actual work. With a field force handling up to five site visits per day, the compounding downtime was degrading both customer experience and operational throughput.
BlueLabel re-architected the client's existing Custom GPT pilot at the API layer using OpenAI's Agents API, building two integrated tools for a regional fiber ISP: a voice-enabled AI assistant for field technician troubleshooting and a dispatch automation workflow that triages service orders, runs hardware health checks, and closes work orders without dispatcher involvement.
The engagement began with an existing Custom GPT pilot that worked in isolation but couldn’t scale — lacking the API-level integration needed to connect with the telecom’s operational systems. BlueLabel re-architected it from the ground up using OpenAI’s Agents API.
Two tools were built in parallel. The first was a voice-enabled AI assistant that gave field technicians natural-language access to troubleshooting documentation and equipment data — enabling them to get answers in the field without calling the back office. The second was an automated dispatch workflow that handled the operational mechanics: triaging incoming daily service orders, running hardware health checks against the telecom’s OSS/BSS platforms, and autonomously closing completed work orders.
The legacy OSS/BSS integration was the primary engineering challenge — connecting modern AI tooling to older telecom infrastructure without disrupting live operations. Encoding senior dispatchers’ institutional knowledge into the AI required careful structure and became one of the most defensible aspects of the system.
The full build ran in 4–8 weeks. At scale serving 150+ technicians, the monthly AI infrastructure cost came to $1.78 — a result of the architectural decision to move off the Custom GPT tier to the Agents API.
Mid-market and enterprise companies in field-service-intensive industries — telecom, utilities, logistics, facilities — where back-office bottlenecks create measurable idle time for frontline workers and where an internal AI pilot has proven the concept but failed to scale reliably across the team.
