AI is changing how service organizations make decisions, and Ashok’s journey explains why Circuitry.ai is focused on that opportunity.
In a recent episode of BAM’s Coffee with a Founder, host Beck Bamberger sits down with Ashok Kartham, CEO and founder of Circuitry.ai, to talk about the experiences that shaped Circuitry.ai and how he sees AI changing the service, parts, and warranty industry.
Watch the podcast: Coffee with a Founder Podcast: Ashok Kartham, Circuitry.ai
A career shaped by service
Ashok’s connection to service began well before Circuitry.ai. Early in his career, he worked as a consultant on service, support, and warranty systems at John Deere. The experience introduced him to the complex network of people, knowledge, systems, and decisions required to keep equipment running.
It also revealed an opportunity to use technology to improve how manufacturers and dealers deliver service.
Ashok went on to found 4CS, which was acquired by PTC, and later founded Mize, which merged with Syncron. Both companies addressed different parts of the service lifecycle, from delivering technical knowledge to supporting mobile experiences and transactional workflows.
Each chapter gave Ashok a deeper understanding of service organizations and the challenges faced by technicians, contact center teams, dealers, and manufacturers.
Why start another company?
Ashok saw an opportunity to combine emerging AI capabilities with the service knowledge he had developed over more than two decades. The possibility was too compelling to ignore.
“It felt like this was something I needed to work on. I couldn’t just stay on the sidelines and let it happen.”
That conviction became the foundation for Circuitry.ai and its focus on Service Decision Intelligence. Rather than building another general-purpose AI tool, Ashok saw an opportunity to apply AI to the specific decisions made across service, parts, and warranty.
A vision for keeping equipment running
The vision behind Circuitry.ai can be expressed through a straightforward question:
“What I saw was the value of AI helping people make better decisions. There is a lot of data being created, but how do we convert that data into decisions that generate better outcomes?”
Ashok saw the potential for AI to do more than make information easier to find. It could bring together service knowledge and operational data to help teams make more accurate decisions.
That mission led Ashok and the Circuitry.ai team to think beyond individual AI use cases. Service Decision Intelligence connects service knowledge, product and asset data, parts information, warranty policies, repair history, and enterprise systems so people and Service AI Workers can determine what should happen next.
Service is built on decisions
When equipment needs attention, service teams must determine what failed, which repair will resolve it, what part is required, whether the work is covered, and what should happen next.
As Ashok explains in the conversation, making those decisions requires more than retrieving a manual or generating an answer. The full context may include symptoms, fault codes, diagnostic data, asset history, service procedures, technical bulletins, bills of materials, parts catalogs, warranty policies, repair evidence, and the outcomes of similar service events.
Circuitry.ai’s Service Decision Intelligence brings that information together through service-specific data models that connect products, components, symptoms, repairs, parts, policies, and outcomes. Service AI Workers can then apply decision models, business rules, confidence thresholds, and next-best-action logic to recommend, score, route, or automate a decision.
In practice, this creates a continuous cycle: understand the service context, determine the right decision, take an approved action through connected systems, and learn from the result. Clear, lower-risk steps can be automated, while exceptions are routed to experts with the relevant context, explanation, and audit trail.
Why domain expertise matters
Although industries such as medical devices, automotive, construction, and industrial equipment may appear very different, their service environments share important characteristics.
Complex bills of materials, diagnostic information, technical bulletins, service procedures, equipment histories, warranties, and service contracts all contribute to how a service decision is made. Much of that information is also distributed across different systems and formats.
Understanding those relationships requires more than a general-purpose AI model. It requires knowledge of the service lifecycle and the context surrounding each decision.
That’s why Circuitry.ai has focused on vertical AI built specifically for service. Circuitry.ai’s leadership team brings decades of experience delivering service solutions for global manufacturers and service organizations. That domain expertise enables Circuitry.ai to support teams with equipment-specific recommendations and power Service AI Workers that can assist with or execute defined parts of the service journey.
Building what comes next
For Ashok, Service Decision Intelligence represents the next chapter in a career spent applying each new generation of technology to complex service challenges. What began with systems for organizing and delivering service information is evolving into Service AI Workers that can understand context, support decisions, take governed actions, and learn from outcomes.
The technology may be new, but the objective remains familiar: help service organizations make better decisions, keep equipment running, and deliver better outcomes.
For Ashok, that progression reflects a career spent looking at familiar service challenges through each new generation of technology. Today, AI is creating possibilities that seemed out of reach only a few years, or even a few months, ago.
