Capabilities · AI Visibility & Web Transformation
I take the whole problem, not a slice of it.
An SEO consultant stops at the recommendation. An agency redesigns and moves on. A strategist hands you a deck. I diagnose why the market and the AI models prefer another answer, then rebuild the content, website, measurement, and systems required to change it, and I lead the program through implementation.
Human-directed, AI-accelerated
Human-directed, AI-accelerated. I manage every step. Nothing is vibe-coded, and nothing ships on a prompt and a hope. AI is my development partner, I review everything it and its sub-agents produce, and I built the interface I work in so I can see the work and approve every change as it happens. That discipline is what makes the output production-grade instead of AI slop, and it is why one person moves this fast.
Also under Work
Three engagements ran on these capabilities, with the numbers and their limits. Read the case studies →
The problem
Go ask ChatGPT about your category. See who it recommends.
Buyers ask the AI models before they ever hit search. If the models recommend your competitor, you lose deals you never saw. Somebody has to own the whole system that changes that answer.
Your website says who you were three years ago. Content goes stale the week it ships, and the CMS turns every fix into a project.
Vendor dashboards answer the vendor's questions, not yours. I build the measurement around your category, and you own it outright.
What I can do for you
I handle every link in the chain, and each one has already moved something.
I handle these myself rather than subcontracting the parts I do not do. Each row says what the capability is, what you get out of it, and what it has already moved.
I find out why AI recommends someone else, then I change the answer.
Locked prompt libraries run a category's real buying questions across ChatGPT, Claude, Perplexity, Gemini, and Grok. Official-domain verification, forced reasoning, grounded citations, normalized into records you can retest on a fixed cadence. Then I write the content, the schema, and the machine layer that move the result.
I call the method Content is Code. The models are the web's primary readers now, and anything algorithmic can be reverse-engineered. I measure how they choose, then engineer the exact signals they reward.
Message, content, website, and machine layer, rebuilt as one system.
Straight to code with AI, no CMS sitting between a decision and the live page. Positioning and message architecture, information architecture, the build itself, structured data, accessibility, performance, security, analytics, and lead capture. You own the hosting, the domain, and the source outright.
Human-directed, my HALO method. Nothing is vibe-coded and nothing ships that I have not read. That discipline is why one person moves this fast without shipping slop.
WordPress share: 43.6% peak mid-2025 to ~41.5% July 2026.
First decline since W3Techs tracking began in 2011.
7,966 new WordPress CVEs in 2024, 96% from plugins.
Agents, A2A endpoints, and compliance-grade chat you own outright.
Architecture and data modeling before a line of agent code. Retrieval grounded in your own content, multi-vendor model and agent orchestration with monitored spend, custom reporting, durable workflows, observability and evaluation, and a handoff your team can actually run.
Grounded means grounded. Every answer traces to a source, and when nothing supports the question the system declines rather than guessing in your name. A confident wrong answer costs more than a missing one.
I have founded the companies and led the teams. I still ship the code.
Founder and CEO, managing partner, co-founder, technical architect. I have carried the P&L, the client relationships, the architecture decisions, and the deploy. Fortune 5, federal, state government, and 40+ university platforms, delivered from Austin without interruption since 1994.
I take the whole program: executive diagnosis, cross-functional leadership across marketing, content, web, data, and engineering, vendor and architecture decisions, implementation through delivery, and the reporting cadence that proves it worked. I run it as an operating model rather than a one-time audit. Every cycle is measured, decided by a person, built, shipped under a human gate at publish, and retested against a locked benchmark.
An SEO consultant stops at the recommendation. An agency redesigns and moves on. A strategist hands you a deck. I diagnose why the market and the AI models prefer another answer, then rebuild the content, website, measurement, and systems required to change it, and I lead the program through implementation.
However you buy it, one person carries the chain.
One integrated capability
01 · Offer
Fractional AI Visibility Director
A senior owner across strategy, content, website, measurement, and systems.
For: You are poorly represented in AI answers and search, and no one owns the whole system that would fix it.
Outcome: Get seen, cited, and preferred by the AI models your buyers now ask, on an operating model that keeps working after launch.
What I handle
- Executive diagnosis and priorities
- Cross-functional leadership across marketing, content, web, data, and engineering
- Architecture and vendor decisions
- Implementation ownership through delivery
- Reporting and operating cadence
- Accountability for the result
First engagement
A head-to-head read of how AI systems describe, cite, and recommend you against a real competitor field.
What stays with you
Approvals, subject-matter access, and the decision to act on priorities.
Governance
Human approval on everything outbound, a documented data and security posture, and a clean handoff you own.
Ongoing
Retained as the AI-visibility function: measure, prioritize, report, repeat.
Illustrative interface. Figures are examples, not client metrics.
Proof: I architected Trinzik's five-platform Research Engine and the editorial operating model that acts on it.
02 · Offer
Complete Web Presence Transformation
Message, content, website, and machine-readability rebuilt as one system.
For: Your message, content, and website no longer reflect the company you have become, and it goes stale the moment it ships.
Outcome: A modern, code-first web presence that people and AI read accurately, with an editorial system that keeps it current.
What I handle
- Positioning and message architecture
- Information architecture and content overhaul
- Modern code-first website implementation
- Structured data and machine-readable content
- Accessibility, performance, security, and governance
- Conversion paths, analytics, and operational handoff
First engagement
A positioning and presence audit against real comparables, with a scoped build plan.
What stays with you
Brand and compliance rules, expert review, and sign-off before anything publishes.
Governance
Human approval on everything outbound, a documented data and security posture, and a clean handoff you own.
Ongoing
An editorial operating system your team runs, or a full managed retainer.
Design
Frontend
export default function App() {
const data = useSWR()
return <Grid cols={3} />
}Backend
- GET/metrics200
- POST/users201
- PUT/config200
Data
Illustrative interface. Figures are examples, not client metrics.
Proof: I build agentic websites: straight-to-code sites with editorial approvals and machine surfaces. This site is one.
03 · Offer
Custom AI System Build
Proprietary research, reporting, agents, and workflows beyond packaged tools.
For: You need instruments a vendor dashboard cannot provide, and you want to own them.
Outcome: Custom systems integrated with your stack, instrumented for production, and handed off for your team to run.
What I handle
- System architecture and data modeling
- Retrieval and RAG grounded in your own content
- Multi-vendor model and agent orchestration
- Custom reporting and executive dashboards
- Automation, integrations, APIs, and durable workflows
- Observability, evaluation, and clean handoff
First engagement
A scoped build with phases, milestones, and a fixed data and security posture.
What stays with you
Access to systems and data, and approval at each phase gate.
Governance
Human approval on everything outbound, a documented data and security posture, and a clean handoff you own.
Ongoing
Optional managed operation, iteration, and support.
Illustrative interface. Figures are examples, not client metrics.
Proof: I built Trinzik's Research Engine, its model and agent orchestration, editorial workflows, and this site's own agent and machine endpoint.
The systems I built
Every system here is live today, and clients run on most of them.
Trinzik is the Austin AI company I co-founded; my brother John runs it, and I architected and built the entire production platform it sells. It is the clearest proof of what I do. I built these systems; the client results Trinzik reports are Trinzik engagements delivered on the platform.
Agent · A2A · chat all responding now
Five-platform Research Engine
Locked prompt libraries run across ChatGPT, Claude, Perplexity, Gemini, and Grok. Official-domain verification, forced reasoning, normalized records, repeatable retests. The output is a defensible read of why one answer is preferred over another.
This site answers machines too
A discoverable, callable agent card and a live A2A endpoint. One knowledge base serves people and other AI systems.
GET /.well-known/agent-card.json { "name": "Bob Michaels · Executive Agent", "endpoint": "/api/a2a", "status": "live" }
Agentic Websites
Straight to code with the research and content engine baked in. Editorial approvals on everything, machine surfaces correct by construction, same-URL cutover with no ranking loss. The client owns the hosting, domain, and source.
Compliance-Grade Chatbots
On-site AI that answers only what it can cite from your own pages and declines what it cannot. Per-industry rule packs, a full audit trail, per-tenant isolation. Regulated buyers can put it in front of customers.
HALO: human-directed, AI-accelerated
Nothing vibe-coded. I direct every step, review everything the model and its sub-agents produce, and approve every change in an interface I built so I can see the work as it happens.
The systems tell the truth under pressure
Measurement purity, citation grounding, anti-fabrication, and a human in control. The systems draft at machine speed. People decide what ships, and every byline belongs to someone who signs off on what carries their name.
Built by Bob as Trinzik's technical architect. Results shown are from Trinzik client engagements using the platform.
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
Human-directed, never autonomous
Nothing is vibe-coded, and nothing ships on a prompt and a hope. I direct every step, AI is the development partner, and I review everything it and its sub-agents produce before it lands. That is why AI makes the work faster without making it slop.
02
Architect first
Data model, tenancy, security boundaries, and evaluation strategy before a line of agent code. The expensive mistakes happen at the architecture layer.
03
Security and 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.
04
Grounded and evaluation-driven
Every answer traces to a source. Pipelines are instrumented and measured, accuracy over personality, evidence over vibes.
05
Multi-vendor and cost-governed
Routing across the major model providers with monitored spend, so you are never locked to one vendor or surprised by a bill.
06
Production over prototype
The goal is a system in production that your team can operate, not a demo that impresses once and rots.
07
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
Agents & protocols
Retrieval & data
Application
Orchestration & ops
AI discoverability
Not sure which one you need?
Most engagements start the same way: a head-to-head read of how the AI models describe, cite, compare, and recommend you against a real competitor field. That read tells us what to do next.