Platform Engineering for AI Products
Give your engineering teams the infrastructure they actually want to use. We build internal developer platforms and AI-ready infrastructure so teams ship faster and spend less time fighting tooling.
of developer time is lost to environment, tooling, and deployment friction
Half a team's working week disappearing into config files and manual deployments is not a minor inconvenience — it is a structural problem. When every team manages their own cluster setup, every team solves the same problems differently and breaks in different ways. A well-built internal developer platform removes that repetition. Teams get a consistent path from code to production, infrastructure that scales with load, and dashboards that show what is actually happening. That time comes back as shipped features. A golden-path template that turns 'spin up a new service' from a two-day ticket into a ten-minute self-serve action hands every team that week back.
Inside Platform Engineering
Platform engineering is the discipline of building and maintaining the shared infrastructure, tooling, and workflows that product teams use to build, deploy, and run software. For AI products, that means Kubernetes clusters that run model workloads, GitOps pipelines that keep environments in sync, internal developer platforms that hide the complexity of the cloud, and observability stacks that surface problems before users notice them. The result is a paved road — a set of defaults that make the right way to deploy also the easy way.
Infrastructure as code — every resource is defined in version-controlled Terraform so no environment can drift silently from its declared state.
Kubernetes cluster design — we design and harden clusters for AI workloads: GPU node pools, resource quotas, namespace isolation, and horizontal autoscaling.
GitOps delivery — ArgoCD or Flux keeps application state in sync with Git, so deployments are auditable and rollbacks are a one-line revert.
Internal developer platform — we build or extend a platform layer (Backstage or custom) that gives engineers a self-service portal for services, environments, and secrets.
Observability stack — logs, metrics, and distributed traces wired together so on-call engineers can find the source of a problem without reading raw container logs.
Secret and config management — Vault or cloud-native secret managers keep credentials out of code and out of environment files passed around in Slack.
Cost and capacity management — we tag resources, set budget alerts, and right-size workloads so your cloud bill reflects what you actually need.
Is this right for you?
This service fits best when you recognise yourself below.
Engineering leaders whose teams spend more time fighting infrastructure than building product.
AI product companies scaling from a few services to a multi-team, multi-environment platform.
Organizations moving from ad-hoc deployments to a repeatable, auditable delivery process.
Teams planning to run model inference at scale who need GPU-aware cluster configuration.
The problems behind the brief
Every team deploys differently
Inconsistent deployment patterns mean outages in one team look nothing like outages in another. We establish shared standards so the whole organization benefits from every hard lesson.
Environments drift from production
Dev and staging environments that do not match production produce bugs that only appear on launch day. GitOps and infrastructure as code keep every environment in sync.
No visibility into what is actually running
Logs scattered across services and no single place to trace a request end-to-end makes debugging slow and expensive. We build an observability layer that connects the dots.
Secrets and config managed by hand
API keys in .env files, rotated manually, shared over Slack. That is a security and reliability problem waiting to happen. We centralize secret management and automate rotation.
Cloud costs that grow faster than the product
Untagged resources and over-provisioned instances quietly inflate the bill. We bring cost visibility and autoscaling policies that match spend to real usage.
A clear, repeatable process
No mystery. You always know what happens this week and what comes next.
We review your current infrastructure, delivery pipelines, and team workflows. We document what is working, what is not, and design the target platform architecture before writing any code.
We build the foundational layer: Terraform modules for your cloud resources, Kubernetes cluster configuration, GitOps pipelines, and the core observability stack. Everything is tested in a staging environment before production.
We layer the developer-facing surface on top: internal developer platform portal, service templates, self-service environment provisioning, and secrets management. We run workshops with engineering teams to validate the workflows fit how they actually work.
We migrate existing services onto the platform, run incident simulations to test the observability and rollback paths, and hand over documentation and runbooks. Your team leaves with a platform they know how to operate.
Deliverables
Concrete outputs you keep — not just a conversation.
What good looks like
Deployment frequency increases as teams move from manual to automated releases.
Mean time to restore drops as observability surfaces root causes faster.
Infrastructure drift incidents reach zero as GitOps enforces declared state.
Developer satisfaction improves with a self-service platform that removes waiting for infra tickets.
The stack behind the work
We pick tools to fit your needs, never vendor relationships.
Infrastructure
- Kubernetes
Infrastructure as Code
- Terraform
GitOps
- ArgoCD
Internal Developer Platform
- Backstage
Observability
- Prometheus
- Grafana
Security
- HashiCorp Vault
CI/CD
- GitHub Actions
Common questions about Platform Engineering
Straight answers to the questions we hear most.
Still have questions? Talk to our team
The natural next step
A solid platform makes every other build faster and more reliable. If you are deploying AI models on that platform, MLOps & Deployment is the natural next step — it covers the pipelines that keep models healthy in production. If you need the AI application itself built on top of the platform, Custom AI Applications is the right follow-on.
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