Cloud Infrastructure for AI-Native Companies
We get your AI to production on AWS. From model serving and agent infrastructure to platform engineering and cost optimization — we handle the cloud platform layer so your team can focus on the product.
What We Do
Your ML team built the model. We get it running in production. Deployment on AWS using vLLM, RayServe, and SageMaker — GPU infrastructure management, inference optimization, and monitoring. We also deploy and operate AI agent systems: multi-agent pipelines, orchestration frameworks, tool integration, and the infrastructure layer that keeps agents reliable at scale.
We design and implement Internal Developer Platforms that reduce cognitive load and accelerate delivery. Kubernetes-native, GitOps-driven, with self-service workflows that let your engineers deploy without filing tickets. We build the platform as a product — not an afterthought.
AWS-focused architecture reviews, migration planning, and Well-Architected assessments. We help startups avoid the re-architecture tax at scale and help growth-stage companies modernize without downtime. Every engagement includes knowledge transfer — we don't create dependencies.
Comprehensive cloud cost audits, reserved instance strategies, right-sizing, and ongoing FinOps practice implementation. We typically find 20-40% savings in the first engagement. We build the dashboards and alerting so you never lose visibility again.
Why Us
We Figure It Out
Technology changes. Frameworks come and go. What doesn't change is the ability to take something new, understand it fast, and make it work in production. We've been doing this through every infrastructure shift for 13 years — containers, Kubernetes, serverless, and now AI workloads.
Production, Not Prototypes
Most DevOps consultancies stop at CI/CD. We operate in the gap between 'it works on my laptop' and 'it runs reliably at scale in production.' Model serving, agent orchestration, GPU scheduling — the hard infrastructure problems that block AI teams from shipping.
Knowledge Transfer Is the Deliverable
We measure success by how quickly your team can operate independently. Every engagement includes documentation, training, and runbooks — because the goal is to make ourselves unnecessary.
Startup Speed, Enterprise Rigor
We move at startup pace without cutting corners on security, compliance, or reliability. Small team, no bureaucracy, direct access to senior engineers.
Insights
Getting vLLM to Production on AWS EKS: Architecture Decisions That Matter
Platform Engineering for AI Startups: What Your DevOps Hire Won't Tell You
The FinOps Playbook: How We Cut a Startup's AWS Bill by 35%
Selected Work
Production ML serving infrastructure for clinical AI
Deployed a production model serving layer using vLLM and RayServe on AWS, enabling real-time inference for clinical decision support. Built with HIPAA-compliant architecture patterns and automated GPU scaling.
3 models in production · < 200ms p95 latency · 99.9% uptime
Multi-agent pipeline deployment for an enterprise AI product
Architected and deployed the infrastructure layer for a multi-agent AI system — orchestration framework, tool integration, monitoring, and auto-scaling on AWS. Designed for reliability at production scale.
7-stage agent pipeline · Fully automated deployment · Zero-downtime updates
Let's Build Something
Whether you're shipping your first model to production or scaling an existing platform, we'd love to hear what you're working on.