Trane Technologies manages millions of connected heating, ventilation, and air conditioning (HVAC) assets worldwide, but getting a single operational answer could mean cross-referencing multiple dashboards and drilling through menus for 20 minutes or more. For organizations operating at this scale, that kind of friction slows operations, defers corrective action, and creates material business impact across the enterprise.

In 3–4 weeks, Trane’s engineering team built an AI-powered agentic solution on Amazon Bedrock AgentCore that reduced a 20-minute multi-screen diagnostic workflow to a 20-second natural language interaction. This is based on Trane’s internal benchmarking with technicians over several weeks. This represents a 60x improvement in time-to-insight, helping shift operations from reactive response to more proactive, data-driven optimization.

In this post, we describe the architectural approach and key design decisions behind the solution:

Trane Technologies is a global climate innovator with over $21 billion in annual revenue and operations in more than 100 countries. Through its strategic brand Trane, the company manages millions of connected HVAC assets, spanning data centers, hospitals, manufacturing facilities, and commercial real estate portfolios. At the heart of this vast landscape is Trane Cloud, a digital hub that aggregates real-time performance data from millions of HVAC systems. Trane Cloud transforms raw equipment telemetry into actionable intelligence for predictive maintenance, energy optimization, and operational excellence.

While dashboard-based building management systems provide a foundation for monitoring and control, extracting cross-system insights can still require users to navigate multiple screens, layered menus, and disconnected dashboards. By combining natural language processing with deep integration into Trane Cloud, users can access that operational context through a single conversational interface.

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