Developer platforms.
Golden paths, not guesswork.

Teams route around anything slower than doing it themselves, and a paved road nobody drives on is just expensive infrastructure. The measure of a platform is adoption, and adoption is earned by removing more work than it adds.

Intellivetrix builds internal developer platforms as products: a small number of well-supported golden paths, self-service provisioning that does not require a ticket, security and compliance encoded as defaults rather than checklists, and clear escape hatches for teams whose needs genuinely fall outside the paved road.

This is also the foundation enterprise AI runs on. The gateway, policy engine and observability that govern AI workloads sit on the same Kubernetes, IaC, CI/CD and identity substrate as everything else—which is why Intellivetrix treats platform engineering and AI platform work as one discipline rather than two practices.

Platforms are judged on how many teams choose them, so we design for the fastest path to production rather than the most complete abstraction.

Controls are encoded into templates and pipelines, so meeting the standard is what happens when a team does nothing special.

The same platform that runs your services runs your AI workloads, including GPU scheduling and model serving.

Teams with genuinely different requirements can step off the paved road without leaving the platform altogether.

Building and running an internal product whose customers are your own engineers. It provides self-service paths to provision, build, deploy and operate software, so application teams spend their time on product rather than on assembling infrastructure independently.

CI/CD is one component. A platform covers the whole path, including environment provisioning, service scaffolding, security controls, observability defaults and the interface through which teams consume all of it. The distinguishing feature is self-service: teams get what they need without filing a ticket.

Adoption is treated as the success measure from the start. That means a small number of genuinely supported golden paths rather than broad coverage, a path that is faster than the alternative rather than merely more compliant, and documented escape hatches so teams with unusual needs are not forced to abandon the platform entirely.

Yes, and we consider that the point. Model serving, GPU scheduling and AI gateway components run on the same substrate as everything else, which is why Intellivetrix treats platform engineering and enterprise AI platform work as one discipline.

Kubernetes, Terraform and comparable IaC tooling, GitOps delivery, policy-as-code engines, service meshes and mainstream observability stacks, across AWS, Azure, Google Cloud and on-premises environments. Choices are made against your existing estate and team skills rather than a fixed stack.