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.
NY-based · Working remotely worldwide
AI infrastructure consulting. Ship systems your team actually owns.
I help you ship AI infrastructure your team actually owns: scoped around your constraints, documented for handoff, and ready for your engineers to operate after I leave.
Frontier model APIsPrivate and local LLM clustersHybrid architecture strategy
Interactive architecture workbench
Work in progressWant to model the stack yourself?
Open the dedicated workbench to compare model paths, retrieval, governance, network boundaries, tool access, resilience, and AI-assisted architecture changes.
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 Automation Audit
Diagnostic & entry pointA deep-dive technical audit of your existing automation flows, LLM prompts, tool integrations, and data boundaries.
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.
Public proof
Case studies and technical walkthroughs.
Public examples stay limited to work that can be described accurately without exposing client-confidential information.
Review current technical evidence
Start with the delivery examples above, then browse the technical video library for implementation walkthroughs and engineering demonstrations.
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
Confirm fit and the next step
The free initial consultation is for goals, constraints, fit, and scoping. If there is a fit, the next step may be a proposal, an architecture assessment, or an implementation engagement.
02
Assess before building when needed
Detailed discovery, architecture, workshops, roadmaps, and technical recommendations are scoped as paid consulting—often as a fixed-price assessment—before implementation starts.
03
Build and 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.
9:48
AI / DevOpsJun 13, 20269:48
I Built a Self-Hosted Memory System for AI Agents
A deep walkthrough of a self-hosted memory architecture for AI agents — covering persistence, retrieval, and production-grade infrastructure design.
3:21
AI / DevOpsMar 22, 20263:21
I Built an AI DevOps Engineer That Writes, Reviews, and Merges Code
Building an autonomous AI DevOps agent that handles code writing, review, and merge operations across a real development workflow.
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
On consumer hardware, local LLM reliability is mostly a memory, context, and concurrency problem. This article keeps the deployment shape deliberately small so failures are easy to see.
9 min read
RAG Architecture Patterns for Enterprise
Most RAG failures are not model failures. They come from retrieval, permissions, stale content, or a pipeline nobody can inspect when an answer goes wrong.
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.
FAQ
Common questions before the first call.
Architecture-specific questions — frontier vs. private vs. hybrid, GPUs, existing Kubernetes — are answered in the consulting FAQ.
What does Quinn Favo do?
Quinn Favo is an independent AI infrastructure and DevOps consultant who helps teams ship production AI systems they can own and operate themselves: frontier model API integrations, private and local LLM clusters, hybrid architectures, RAG pipelines, MCP agent systems, and workflow automation.
How do engagements start?
With a free 30-minute initial consultation focused on goals, constraints, fit, and the right next step. If there is a fit, I can send a proposal or recommend a scoped assessment. Proposal review and scope-clarification conversations are not billed separately. Detailed discovery, architecture, workshops, or advisory work is paid consulting and is scoped before it begins, usually inside a fixed-price engagement.
What does an engagement cost?
Packages are scoped with fixed timelines, from $1,500–$2,000 for a one-week AI Automation Audit up to $14,000–$20,000 for a six-to-eight-week LLM Private Cloud build. Discounted case-study rates are available when the work can be documented as an anonymized technical case study.
How long does an engagement take?
One week for an AI Automation Audit, three to four weeks for AI Ops Starter, four to six weeks for Agent Mesh, and six to eight weeks for LLM Private Cloud. Custom AI Architecture is scoped after discovery.
What do we actually get at the end?
A documented system rather than a demo: runbooks, infrastructure clarity, CI/CD, and a clean handoff so your own engineers can operate it after the engagement ends. The exit path for the consultant is part of the deliverable.
Where are you based, and do you work remotely?
Based in the New York area and working remotely with teams worldwide.
Start here
Start with a free consultation to choose the right next step.
We will review your goals and constraints at a high level and decide whether the next step is a proposal, a fixed-price assessment, or an implementation engagement. Detailed architecture work is scoped before it begins.
Quazmoz software
Android and Wear OS products I ship alongside consulting.
Quinn Favo is the developer behind Quazmoz, a portfolio of focused Android and Wear OS utilities published on Google Play. This is supporting engineering work, not a replacement for the consulting focus of this site.
CountCue Wear OS Timer
Wear OS
Run multiple named countdowns and a stopwatch directly on Wear OS with haptic cues, local history, notification actions, Tile and watch-face complication shortcuts, plus an optional one-time Pro upgrade.
View app
FidgetDrop Haptic Fidget
Android
A privacy-first Android haptic fidget app with 54 tactile experiences, including 10 free fidgets, motion-driven play, optional stylus enhancements, local records, and a one-time FidgetDrop Pro upgrade. No ads, accounts, or tracking.
View app
WebHookDeck: Watch Webhooks
Android + Wear OS
Configure trusted HTTP actions on Android, sync selected favorites, and trigger them from Wear OS with confirmation, status feedback, Tile and complication access, plus an optional one-time Pro unlock.
View app