Making the AI Economy
trustworthy at scale.
We don't build models. We build the engineering layer underneath that makes everything run — secure, reliable, observable, performant, and cost-efficient.
What we do
Consulting. Implementation. Maintenance.
Three engagement types, one goal — making your AI infrastructure reliable, cost-efficient, and scalable.
Consulting
Infrastructure Audit
We diagnose what's broken, what's expensive, and what to do about it. Always a document, never guesswork.
Consulting
Architecture Design
Moving off managed platforms? We architect the path to owning your AI infrastructure.
Implementation
Model Serving
Kubernetes, GPU scheduling, vLLM, Triton. Autoscaling inference with canary deployments.
Implementation
RAG Infrastructure
Vector databases, retrieval pipelines, hybrid search, caching layers, and evaluation frameworks.
Implementation
GPU Cost Optimization
Right-sizing, spot instances, quantization (GPTQ, AWQ, GGUF), and multi-tenancy for GPU sharing.
Implementation
AI Platform Engineering
CI/CD for models, experiment tracking, model registry, feature stores, and self-service deployment.
Implementation
Cloud Architecture
AWS/GCP/Azure infrastructure, networking, security, IAM, multi-region, and IaC with Terraform/Pulumi.
Maintenance
Ongoing Ops
Monthly health reviews, cost monitoring, GPU capacity planning, incident support, and quarterly roadmaps.
50+
AI systems shipped to production
99.9%
Uptime across managed infrastructure
10+
Years of infrastructure engineering
Ready to scale your AI infrastructure?
Tell us about your AI infrastructure challenges and we'll scope an engagement that fits.
Start a conversation