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Strategic Impact
Our Approach
Knowledge Source Consolidation
Scattered documentation was pulled into one grounded source. Equipment manuals, service bulletins, and maintenance procedures were consolidated so the agent could reason over the manufacturer's actual technical library rather than a technician's memory.
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Symptom-Driven Diagnostic Agent
A technician describes what they see and the agent works the problem. The Copilot Studio agent guided diagnosis from symptom or equipment identifier through likely causes and corrective steps, drawn from the consolidated documentation.
Service History Integration
The agent knew the machine's past. Prior service records were connected so diagnosis accounted for what had already been done to a specific unit, not just the generic fault tree.
Resolution Logging to Dataverse
Every fix became data. Resolutions, steps taken, and outcomes were logged to Dataverse automatically, turning each field call into structured history that improved the next diagnosis.
Offline and Field-Ready Access
The tool worked where the technicians did. Access was designed for field conditions and unreliable connectivity, so the agent was usable at the customer site, not just back at the depot.
BabyBots Impact Assessment
The Impact
The client put diagnostic expertise in the hands of every field technician, regardless of tenure. Troubleshooting that once depended on memory or a call to a senior tech now runs through an agent grounded in the manufacturer's own documentation and each unit's service history. Downtime at customer sites shrank, and every resolution is captured as structured data rather than lost when the technician drives away.
The engagement began by shadowing how technicians diagnosed failures in the field today, from the documentation they reached for to the calls they made when stuck. Mapping that reality defined what the agent needed to know and where it had to work.
- Copilot Studio troubleshooting agent grounded in equipment documentation
- Consolidated technical library of manuals, bulletins, and procedures
- Symptom and equipment-identifier driven diagnostic flow
- Service-history integration for unit-specific diagnosis
- Automated resolution logging to Dataverse and field-ready access
Project Journey
Project Timeline
Phase 1
Field Troubleshooting Copilot — Field Diagnosis Discovery (Phase 1)
Shadowed how technicians diagnosed failures in the field and the documentation and calls they relied on.
Phase 2
Field Troubleshooting Copilot — Knowledge Consolidation and Grounding (Phase 2)
Consolidated equipment manuals, bulletins, and procedures into a single grounded source for the agent.
Phase 3
Field Troubleshooting Copilot — Diagnostic Agent Build (Phase 3)
Built the symptom and equipment-identifier driven diagnostic flow in Copilot Studio.
Phase 4
Field Troubleshooting Copilot — Service History and Resolution Logging (Phase 4)
Integrated unit service history and automated resolution logging to Dataverse.
Phase 5
Field Troubleshooting Copilot — Field Testing and Rollout (Phase 5)
Tested the agent under field conditions, confirmed offline-ready access, and rolled it out to technicians.
Manufacturing / Copilot Studio
Diagnostic expertise should not walk out the door with your senior techs.
The tool worked where the technicians did. Access was designed for field conditions and unreliable connectivity, so the agent was usable at the customer site, not just back at the depot.
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BabyBots is an enterprise technology partner with expertise across process automation, AI systems, data intelligence, and platform architecture.
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