Teams moving from demo to production
You have AI experiments, internal workflows, or a prototype in motion and need the infrastructure, security, and delivery discipline to make it real.
AI infrastructure consulting
Ship AI infrastructure your team actually owns when I leave.
Senior consultant for AI infrastructure, DevOps, and agentic workflows. I help teams build with frontier models, private LLM clusters, or hybrid architectures, scoped, documented, and handed off to your engineers.
Frontier model APIsPrivate and local LLM clustersHybrid architecture strategy
Production AI infrastructure
Frontier, private, and hybrid architecture
Documented handoff to your engineers
DevOps, MCP, RAG, and workflow automation
Who it’s for
Technical consulting for teams that need the right architecture, not a trend-driven one.
If you are deciding between managed frontier APIs, private inference, or a hybrid stack, I help narrow the path and build the production layer around it.
Organizations with compliance or ownership constraints
You need to decide when to use frontier APIs, when to keep workloads private, and how to route between both without creating operational debt.
Engineering leaders who need a handoff, not dependency
You want architecture, implementation, runbooks, and decisions captured clearly so your team can own the system after the engagement ends.
What I deliver
Packaged engagements that stay easy to scan.
Start with the package that matches your current risk, then expand from there. Each engagement is scoped around usable infrastructure and a clean handoff.
AI Ops Starter
Fastest path to valueA scoped AI workflow stack with the right model layer, one business integration, and a real production use case.
Agent Mesh
Most requestedModel-agnostic agents with auditable tool access across cloud, infra, and internal systems using MCP patterns.
LLM Private Cloud
Ownership firstPrivate or air-gapped inference with observability, RAG, and infrastructure your team can operate independently.
Custom AI Architecture
Complex engagementsCustom-scoped delivery for mixed environments, migration decisions, platform hardening, or unclear architecture direction.
Featured case study
A private LLM deployment for a financial-services environment.
One example of where private infrastructure beat a managed API for the client’s constraints.
Challenge
A financial services organization needed LLM capabilities without sending proprietary data to commercial APIs while operating under strict compliance requirements.
Approach
Designed and deployed an air-gapped LLM environment on client-owned infrastructure, plus a scoped RAG layer over internal documents and a documented handoff for the engineering team.
Outcome
40% lower inference cost
Compared with commercial API usage, while maintaining full data residency compliance.
Process
A delivery model built to reduce decision risk early.
The goal is to decide architecture quickly, prove it with your real environment, and leave your team with working systems they can operate.
01
Audit the constraints
We review your data boundaries, latency needs, security posture, team bandwidth, and where the current architecture is creating risk.
02
Build the right path
I recommend frontier APIs, private infrastructure, or a hybrid route based on the actual tradeoffs instead of pushing a preferred stack.
03
Hand off cleanly
You get working systems, documentation, deployment patterns, and enough context for your engineers to keep moving without me.
In public
Proof in code, videos, and technical writeups.
Articles and videos are not the offer. They are the evidence that the consulting is grounded in real implementation work.
5:07
AI / DevOps•Apr 3, 2026•5:07
Run LLMs on Your CPU’s NPU (NO GPU Needed) – Full Setup Guide
Learn how to run Large Language Models locally on your machine using just your CPU's Neural Processing Unit (NPU). No expensive GPU required!
2:49
AI / DevOps•Jan 15, 2026•2:49
Run LLMs for FREE: Open WebUI + Hugging Face API Setup Guide
A complete guide to setting up Open WebUI and connecting it to the Hugging Face API to run powerful LLMs completely for free.
Technical articles
Architecture notes on local deployment, RAG system shape, and production guardrails.
YouTube breakdowns
Real setup walkthroughs for local inference, hosted tooling, and developer-facing AI workflows.
GitHub and implementation proof
Public work that shows the shape of the engineering, not just the sales copy.
10 min read
Deploying Llama 3 8B on Consumer Hardware
A practical deployment pattern for getting strong local LLM performance from modest hardware without overcommitting memory or adding operational sprawl.
9 min read
RAG Architecture Patterns for Enterprise
A field guide to retrieval pipelines that stay observable, secure, and maintainable as they move from demos into real internal systems.
Engineering principles
Build the architecture that fits the requirement, then make it legible.
That means fewer black boxes, clearer operating boundaries, and no pushing private infrastructure where a frontier API would be the smarter answer.
Model choice follows constraints
Frontier models when capability and speed matter most. Private clusters when ownership, compliance, or unit economics dominate. Hybrid when both matter.
Everything stays inspectable
Pipelines, agent actions, infrastructure changes, and deployment decisions should be observable by your team instead of hidden behind magic.
Production means handoff
The outcome is not a clever demo. It is a documented system with runbooks, infrastructure clarity, and an exit path for the consultant.
Start here
If you need a clean recommendation before you commit to a build, book the call.
The discovery call is the fastest way to review your current stack, identify the right engagement, and decide whether frontier, private, or hybrid is the right direction.