02 · Capabilities
Production AI systems, architected to last
Most AI advisors stop at the slide deck. I have run the teams that shipped systems which could not fail, for 33 years, and I hand you the system itself: instrumented, secured, cost-governed, and built for your people to own. Four ways to engage, from a single architecture review to a system built, delivered, and handed off.
01
AI Systems Architecture
Architecture for AI systems that have to hold up under real load, multi-tenant, secured, observable, and cost-governed. I have orchestrated enterprise processes across divisions since the early 2000s, and that discipline is exactly what multi-agent AI demands: the same coordination, now applied to agents, retrieval, and orchestration.
Proof: Brand-scoped multi-tenant agent platforms; the live agent on this page.
- ▸Agent & multi-agent architectures spanning human and agent-to-agent (A2A) channels
- ▸Retrieval / RAG design grounded in your own content, with source-traceable, anti-hallucination answers
- ▸Multi-vendor LLM routing across all five major providers, with cost governance and evaluation
- ▸Multi-tenant data models with row-level security and per-tenant isolation
- ▸Guardrails, observability, and evaluation built into the pipeline
- ▸Security, accessibility, and compliance posture designed in from the start
02
Enterprise AI Application Development
Production applications, not demos. I design and deliver the system, integrated with your stack, instrumented end to end, and handed off with documented Best Known Practices your team can run.
Proof: A live executive-agent SaaS, an AI content pipeline publishing to WordPress, and a competitive-intelligence report generator.
- ▸Custom AI agents, copilots, and brand-scoped chat widgets
- ▸Content & document intelligence pipelines (research → draft → refine → publish)
- ▸Workflow and process automation across your existing tools
- ▸Machine-readable API surfaces and A2A endpoints
- ▸Integrations: WordPress / CMS publishing, billing, vector search, embeddings
- ▸Clean handoff with reproducible BKPs and runbooks
03
AI-Native Web & GEO
This is where Content is Code lives. The models already decide who gets recommended; I reverse-engineer why, then engineer content carrying the exact signals they reward so your organization lands in the answer. I also build the endpoints that let other AI systems transact with you directly.
Proof: A GEO engine analyzing AI-platform recommendations; this site's /api/a2a endpoint and agents.json.
- ▸Generative Engine Optimization (GEO): measure and improve how ChatGPT, Claude, Perplexity, Gemini, and Grok describe, recommend, and cite you
- ▸A2A protocol and agent cards published at /.well-known/ for machine discovery
- ▸llms.txt, schema.org structured data, and AI-readable site architecture
- ▸Competitive AI-visibility analysis across platforms
- ▸AI-native sites where a single agent serves humans and machines from one source
04
AI Transformation Advisory
Senior architecture and AI leadership for the executive team. Where AI fits, what to build versus buy, how to de-risk it, and the Best Known Practices to get there, from someone who has led the teams that built and delivered, not just presented.
Proof: A 33-year track record of setting Best Known Practices at federal scale.
- ▸AI opportunity and readiness assessment
- ▸Reference architectures and build / buy / partner decisions
- ▸Vendor and model strategy, with cost modeling
- ▸Security, accessibility, and governance guardrails
- ▸Phased roadmaps and BKP playbooks
- ▸Team enablement and architecture review
The build discipline
Content is Code is how I make you visible to AI. Beneath it runs the engineering discipline and the team leadership I bring to every system, the same Best Known Practices that carried platforms from Fortune 5 boardrooms to federal production without going down.
01
Architect first
Data model, tenancy, security boundaries, and evaluation strategy before a line of agent code. The expensive mistakes happen at the architecture layer.
02
Security & accessibility built in
Not bolted on. A U.S. security-architecture patent and a Section 508 / WCAG / ADA lineage mean compliance is a design input, not a remediation project.
03
Grounded and evaluation-driven
Every answer traces to a source. Pipelines are instrumented and measured, accuracy over personality, evidence over vibes.
04
Multi-vendor and cost-governed
Routing across the five major model providers with monitored spend, so you are never locked to one vendor or surprised by a bill.
05
Production over prototype
The goal is a system in production that your team can operate, not a demo that impresses once and rots.
06
Documented, transferable BKPs
Work is handed off with reproducible Best Known Practices. The federal precedent: a disaster-recovery platform HUD adopted as its development model.
The stack I build on
Bleeding-edge where it earns its place, boring where reliability matters more than novelty.
Models & routing
ClaudeGPTGeminiPerplexityGrokmulti-vendor routingcost governance
Agents & protocols
A2A protocolagent cardsagents.jsonMCPtool usemulti-agent orchestration
Retrieval & data
RAG over brand contentSupabasePostgres + pgvectorrow-level securityembeddings
Application
Next.jsReactFastAPI / PythonVercelStripeWordPress publishing
Orchestration & ops
n8n workflowsClaude Code subagent orchestrationevaluation & observabilityAPI cost monitoring
AI discoverability
GEOllms.txtschema.organswer-engine optimization
Have a system in mind?
The fastest way to scope it is the agent on the home page, or reach me directly.