Kubernetes GitOps AI/ML Infra IaC

Platform Engineering Services

We build developer-friendly internal platforms that make your engineering team 10x more productive. From self-service deployments to GPU-accelerated ML pipelines, we design the infrastructure foundation that lets your team focus on shipping.

platform-ctl
# Deploy a new service $ platform deploy --env production Provisioning resources... done Running tests... done Deploying to K8s... done # Spin up ML training job $ platform ml train --gpu a100 --data s3://models GPU node pool ready 2x A100 Training pipeline started ETA 45m

Without a Platform, Your Team Is Stuck

Your developers shouldn't be infrastructure experts. But without a proper platform, they're forced to be.

Slow Deploys

Manual intervention, YAML debugging, and cross-team tickets. What should take minutes takes days.

Config Chaos

Every service needs custom setup, secrets, and monitoring. Developers spend more time on infra than code.

Knowledge Silos

One person holds all the infrastructure knowledge. When they're out, deployments stop.

No ML Infra

Data scientists fight for GPU access, ML models are deployed manually, and there's no pipeline standardization.

Our Platform Engineering Solution

We build the internal platform that makes deploying apps and ML models trivial

Self-Service Infrastructure

Developers deploy with a single command. No tickets, no waiting. We build tooling that makes infrastructure invisible.

GitOps Workflows

Every change goes through Git. Automated testing, approval workflows, and instant rollbacks built in.

Security by Default

Network policies, RBAC, secret management, and compliance baked in from day one. Security becomes automatic.

Built-In Observability

Metrics, logs, traces, and alerts come standard. Every service gets monitoring without extra configuration.

Environment Parity

Dev, staging, and production are identical. No more "works on my machine" bugs. Test with confidence.

Knowledge Transfer

We document everything and train your team. No black boxes. Your team becomes fully self-sufficient.

AI/ML Infrastructure, Production-Ready

Your data science team shouldn't manage Kubernetes nodes or GPU drivers. We build the MLOps platform that lets them focus on models, not infrastructure.

GPU Cluster Management

Auto-scaling GPU node pools (A100, H100, T4). Pay only for what you use with spot instance optimization.

ML Pipeline Orchestration

Automated training, validation, and deployment pipelines with Kubeflow, MLflow, or Argo Workflows.

Model Serving at Scale

Deploy models with KServe/Triton. Auto-scaling inference endpoints with A/B testing and canary rollouts.

Feature Stores & Data Pipelines

Centralized feature management, real-time and batch data pipelines, and model versioning.

# MLOps pipeline definition pipeline: name: fraud-detection-v2 stages: - data_prep: spark-on-k8s - training: gpu: 2x-a100 - evaluation: accuracy > 0.95 - deploy: canary: 10% monitoring: drift_detection: enabled auto_retrain: weekly
Kubeflow
MLflow
Triton
NVIDIA GPU
Ray
Feast

The Results You Can Expect

Real metrics from teams we've helped transform

10x

Faster Deployments

From days to minutes. Push to production multiple times per day with confidence.

70%

Less Infra Time

Developers spend less time fighting infrastructure and more time building features.

90%

Fewer Incidents

Standardized platforms mean fewer misconfigurations and production issues.

5x

Faster ML Iterations

Self-service GPU access and automated pipelines accelerate model development cycles.

40%

Cost Reduction

Optimized resources, spot instances, and automated scaling cut cloud bills drastically.

24/7

Peace of Mind

We monitor and maintain your platform around the clock. Sleep better at night.

How We Build Your Platform

Our proven 6-step process for platform transformation

1

Discovery & Audit

We analyze your current setup, interview your team, and identify bottlenecks. We map your deployment workflows, ML needs, and infrastructure pain points.

2

Platform Design

Create a detailed architecture tailored to your needs. Choose the right tools, define standards, and design workflows for both app deployments and ML pipelines.

3

Foundation Build

Set up Kubernetes clusters, networking, security, GPU node pools, and base infrastructure. Everything configured with infrastructure-as-code.

4

Platform Services

Deploy CI/CD, monitoring, logging, secret management, MLOps tools, and developer tooling. A complete production platform with ML capabilities.

5

Migration & Onboarding

Move existing apps and ML workloads to the new platform. Train your engineering and data science teams. Create comprehensive documentation.

6

Optimize & Support

Continuous monitoring, cost optimization, and improvements. We handle incidents, updates, and scaling as your platform and ML needs evolve.

Ready to Build a Platform Your Team Will Love?

Let's discuss your setup and show you how a modern platform with integrated ML infrastructure can transform your engineering and data science velocity.

Schedule a Platform Audit