Get AI into production — not stuck in a proof of concept
Most AI projects never make it past the demo. I help you design and deploy GPU-accelerated AI platforms that run reliably in production, with clear cost controls and governance from day one.
The challenge
Without a strategy
- ❌ GPU costs spiral — $50K/month on unused capacity
- ❌ Models work in notebooks, fail in production
- ❌ No governance — shadow AI everywhere
- ❌ Vendor lock-in to a single cloud provider
With the right platform
- ✓ Right-sized GPU allocation saves 40-60% on compute
- ✓ Models deploy reliably with automated pipelines
- ✓ Clear AI governance and compliance framework
- ✓ Multi-cloud flexibility with Kubernetes
What you receive
Concrete deliverables, not slide decks.
AI Readiness Scorecard
Maturity assessment across data infrastructure, model operations, team skills, and governance. Clear gaps identified with prioritized action plan.
GPU Cost Analysis
Cloud vs on-prem TCO comparison for your specific workloads. Right-sizing recommendations with MIG partitioning and spot instance strategies.
Production Architecture
Model serving infrastructure design with vLLM/TGI, auto-scaling, A/B deployment, observability, and rollback procedures. On Kubernetes, OpenShift, or RHEL AI.
Working Deployment
A running system your team owns — MLOps pipelines, monitoring dashboards, and runbooks. Not a demo, a production platform.
Technologies I work with
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Ready to get AI into production?
30-minute discovery call. We look at your AI goals, current infrastructure, and identify the fastest path to production.
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