Everything on this page is real, in production, and mine — designed, built, and operated by me, end-to-end. Statuses are honest: “in progress” means in progress.
Owned the multi-year buildout of an AI company's data center — from half a rack to six full colocation racks — and built the software suite that runs it. Roughly 90% of the buildout was achieved with open-source software and used equipment.
Result: An AI company runs ~$5M of GPU compute for a fraction of typical build cost, because ~90% of the buildout used open-source software and refurbished enterprise hardware.
I joined when infrastructure was about half a rack of equipment and have specified, purchased, installed, and operated nearly all of the growth since — from the first enterprise GPU server through today's multi-rack AI operation.
Beyond operating the hardware, I designed and built a suite of internal platforms — nearly all from scratch, most open-source or custom-coded — that give the organization visibility and control over the whole environment.
Lead architect and operator. I specified, purchased, installed, and operate nearly all of this hardware, and designed and built the internal software that monitors and manages it — running the AI compute fleet, the domain and database servers, virtualization, monitoring, identity, and the DMZ web tier as one person across the whole stack.
Applications that fix themselves — and an AI-orchestration layer that runs the rest. Built with scripting and AI so systems detect, diagnose, and recover from their own faults, escalating to a human only when one is truly needed. Not chat — an operating system for delegated technical work.
Result: Known faults resolve before anyone gets paged — 1,450+ days of continuous uptime — because remediation knowledge is encoded as playbooks the systems execute on themselves.
The work I'm proudest of is the systems that keep themselves running. Across my own applications and the platforms I operate, scripting and AI combine to handle faults before a person has to: an autonomous remediation agent watches for known error patterns and executes approved recovery playbooks without human intervention; container-level auto-healing restarts failed services; and an event-log pipeline analyzes logs with a mix of rules and AI, then opens its own tickets when something genuinely needs a human. The goal is simple — fewer 2 a.m. pages, and systems that recover on their own.
Running a portfolio of applications and brands means context-switching between unrelated technical surfaces all day: an ecommerce stack one hour, a marketing page the next, a Postgres backup chain after that. A multi-agent operations layer turns that into a coordinated system instead of a stream of one-shot prompts.
Specialized agents own scopes (orchestrator, infrastructure, content, research). A durable encrypted vault holds session notes that survive across conversations and agents, so handoff between a research run today and an implementation run next week is seamless. Scheduled routines run autonomously on a cron. Browser automation, MCP servers, and a custom skill library extend what any agent can do.
I'm not "vibe coding." This is production AI workflow architecture, with the same operational discipline I'd apply to any production system: defined scopes, durable state, audit trails, recovery procedures, scheduled jobs. The agents I work with are tools — designed, instrumented, and operated. That's the difference between using AI and integrating it.
Self-hosted, enterprise-grade ITSM built from scratch to replace commercial tools quoted at up to $152K/year. 10-container Docker stack with an autonomous AI remediation agent, 41 ticket types, and full Slack/Active Directory integration.
Result: $152K a year in license spend never left the building, because the platform was built in-house and runs on hardware the company already owned.
A growing AI company needed enterprise-grade IT service management — ticketing, approvals, hardware tracking, change management, access requests. Commercial options were evaluated: InvGate quoted $30,520/year, ServiceNow enterprise tier runs $152K+. Both were overpowered for internal use and locked into vendor timelines for customization. The decision was made to build instead of buy.
A complete, self-hosted ITSM platform — designed, architected, and built from scratch. Every layer: database schema, REST API, React frontend, authentication, approval workflows, AI integration, Slack connectivity, Active Directory sync, SLA enforcement, and the hosting infrastructure it runs on.
Sole architect and developer. Defined requirements, designed the data model, built every layer of the stack, integrated AI and Slack, stood up the hosting infrastructure, and currently operate the system in production.
Let an overseas data-labeling team work on sensitive client data with no ability to copy or exfiltrate it — across continents, at usable speed.
Result: An offshore team works on sensitive client data every day with zero exfiltration risk, because the data never leaves the network — only pixels do.
An offshore labeling team needed to work directly on confidential client data, but two hard constraints collided: the data could never leave the secure environment, and direct intercontinental connectivity was far too slow to be workable. Off-the-shelf remote access either leaked data or crawled.
Designed and implemented the solution end-to-end — the access architecture, the cloud relay, the VPN security, and the locked-down desktop environment the team works in.
Designed a 3-spine/6-leaf, all-100G data center network for an AI company's GPU server fleet — replacing a flat network with a BGP underlay, VLT peer pairs, ECMP load balancing across all three spines, and Anycast Gateway per zone.
Result: GPU workloads get full east-west bandwidth with no single point of failure, because traffic routes across three spines on equal-cost paths instead of one fragile Layer 2 tree.
A growing AI company's server fleet — including GPU compute nodes — was running on a flat network architecture. As the fleet scaled, the flat topology created bottlenecks, single points of failure, and limited east-west bandwidth. The network needed to be redesigned from the ground up to support the demands of AI/ML workloads: high throughput, low latency, full redundancy.
I designed the complete network fabric architecture: topology, hardware selection, routing protocol, VLAN scheme, redundancy model, and Fortinet firewall integration. The design is complete and implementation is in progress under my direction.
Lead network architect. Produced the full design: topology diagrams, hardware BOM, BGP configuration templates, VLAN scheme, VLT pairing design, and Fortinet integration plan. Presenting and implementing the design with the infrastructure team. When complete, this replaces an entirely flat Layer 2 network with a production-grade Layer 3 spine-leaf fabric.
The throughline of my work: I spot a real, unsolved problem, define the product, build it end-to-end, and then refine it relentlessly until it's genuinely great. Time To Plate is just one example — here are several solutions I've taken from idea to running software.
Result: Six-plus recurring pain points became production software instead of permanent costs, because I own the full path from problem definition to deployed system.
My strength isn't grinding through someone else's codebase — it's product judgment. I find the pain point, design the data model and the experience, build the solution with modern AI-accelerated development backed by my own scripting and automation, and then fine-tune it through round after round until it actually solves the problem and feels great to use. I own the outcome from the first idea to the system running in production.
Product owner, architect, and operator on every one of these — I define what to build and why, drive the build, refine it until it's right, and run it in production.
Chicago-area creative technology company I co-founded and lead as CEO, CTO, and CISO — 7 live product brands spanning apparel, software, music, and education, all running on shared self-hosted infrastructure I designed and operate.
Result: Seven live brands run on the operating budget of one, because they share automation and infrastructure designed once and reused everywhere.
Mad Monkey Creative LLC is a Chicago-area umbrella company I co-founded with musician and educator Rick Kelly. The LLC serves as the legal and operational home for a portfolio of independent product brands — each with its own identity, audience, and product line, but sharing the infrastructure I design and operate.
Original design apparel — the flagship brand and primary creative identity of the LLC.
Music-themed apparel for musicians and gear enthusiasts.
Children's apparel line extending the MMC creative identity to younger audiences.
Recipe manager and event execution engine — the LLC's flagship software product. Live at timetoplate.com.
Guitar lessons and music education offerings from co-founder Rick Kelly.
Independent record label for original music releases under the MMC umbrella.
Gamified fan hunt experience — an interactive scavenger hunt concept.
All 7 brands run on shared self-hosted infrastructure I designed and operate. Apache vhosts per brand, PM2 process management for Node.js services, centralized logging, and a unified deployment workflow. No third-party hosting platforms — everything runs in-house on hardware I manage.
Co-Founder, CEO, CTO, and CISO. I set company direction and own all technical infrastructure and security: server provisioning, network design, web serving, deployment pipelines, SSL/domain management, and the software products (including Time To Plate) that live under the LLC umbrella. Rick Kelly leads creative direction, brand identity, music, and education products; I lead everything technical.