About the Client
Mainspring Energy is a California-based linear generator manufacturer redefining how local power is delivered to capacity-constrained companies and communities. Their solutions serve utilities, data centers, enterprises, and industrial clients across key markets, with over 450 MW in field operations and late-stage development for Fortune 500 organizations.
Goals
Seeing a clear opportunity to accelerate diagnostic operations with AI, Mainspring launched JEN, an innovation initiative to support fault diagnosis across its generator fleet. Their team had already validated the approach through a successful proof of concept and wanted a strategic partner to design the architecture that would take JEN to production scale.
- Extend AI-supported diagnosis across hundreds of distinct fault scenarios with consistent quality
- Set a long-term architectural direction able to evolve with the business
- Gain architectural clarity before committing to a full production investment
- Build evaluation and observability into the system from the start
- Keep every diagnostic cycle economically viable as usage grows
The broader ambition was a confident path toward autonomous diagnostic operations.
Solution
Mainspring partnered with CloudX (now part of Bridgenext Latin America) through an AI Labs engagement: a fixed-scope, senior-led, three-week exploration designed for complex initiatives that need broad discovery to define the right path forward.
The work began with a one-week on-site immersion at Mainspring’s facilities, where our senior AI specialists worked side by side with the company’s engineering team. Together, they mapped JEN’s full architecture and studied how Mainspring’s domain experts troubleshoot faults, including every system JEN draws on. That deep understanding of expert workflows shaped every design decision that followed.
With that foundation, the teams defined an agentic design that turns JEN into a scalable AI system. Specialized AI agents coordinate across diagnostic workflows, while built-in evaluation and observability let Mainspring measure performance and protect quality as the system grows. Cost controls sit inside the architecture itself, keeping each diagnostic cycle efficient as scope expands.
As the design took shape, Mainspring identified new opportunities that had not been visible at the outset. The engagement closed with an executable implementation blueprint, giving their engineers a plan they could act on immediately.
Results
The strongest signal of a successful engagement is a client ready to move forward with confidence. Mainspring’s engineering team now holds a clear, rigorous and well-documented target architecture for JEN, and is moving toward production with confidence and precision.
- A Design Built to Scale: A multi-agent architecture designed to expand across hundreds of diagnostic fault scenarios
- Ready to Execute: The engineering team received an executable implementation blueprint ready for immediate action.
- Growth Without Rework: A progressive discovery model lets JEN take on new scenarios without manual intervention or new custom code for each one.
- Measurable Quality: A defined evaluation and observability framework lets the team track performance and prevent regressions
- Cost Efficiency by Design: Cost controls embedded in the architecture project a lower AI cost per diagnostic cycle
