Delivered a production-grade MVP of StarOps in customer pilots within months, without requiring the client to build a large in-house platform team.
Ingenimax needed to bring StarOps, an intelligent, agent-driven platform for cloud-native application delivery, to market quickly and into customer pilots. The challenge was to enable teams to deploy and operate Kubernetes, CI/CD, and AI/GenAI inference reliably and at scale, without building a large in-house platform team. The solution also had to stay vendor-agnostic, integrate seamlessly with existing stacks, and provide developer-friendly workflows that minimized operational toil while accelerating time to value.
We partnered with Ingenimax as an embedded, lean Agile team, driving end-to-end product development for StarOps. Our primary focus was creating a secure, repeatable “golden path” for cloud-native delivery that covered Kubernetes (EKS) baselines, autoscaling, model serving with KServe, observability, CI/CD, and security. By emphasizing rapid feedback loops, weekly demos, and a shared backlog, we kept iterations short and aligned with evolving client priorities.
To maximize speed and quality, we applied AI-assisted development (using tools like Cursor for refactoring, scaffolding, and documentation) and a prototype-first approach (using AI tools like v0 and design-through-code instead of lengthy design cycles). Combined with async-first communication and minimal ceremony, this lean execution model enabled high shipping cadence, faster environment bring-up, safer deployments, and reduced operational overhead.
Our collaboration enabled Ingenimax to rapidly deliver a production-grade MVP of StarOps without over-engineering or unnecessary complexity. The platform now provides a secure, observable, and cost-aware path for cloud-native delivery that the client can confidently evolve as adoption grows. By aligning closely with the client’s team, time zones, and workflows, and by leveraging AI-native development practices, we accelerated delivery while minimizing hand-offs. We also built AI agents that make these workflows adjustable and repeatable, so StarOps clients can deploy the same secure, scalable patterns on their own infrastructure.
