TL;DR: These are Frank Vazquez's named methodologies for building production AI systems. Not borrowed from theory books—extracted from running real systems that handle real leads and real revenue.
The technology architecture for production-grade AI agent systems. This is the exact stack running Frank's brokerage operations right now.
Voice agent layer handling outbound calls, conversation management, and real-time transcription
Workflow orchestration connecting data sources, agents, compliance filters, and delivery systems
Backend storage with atomic transactions, preventing race conditions and duplicate calls
TCPA filtering, DNC verification, time-of-day restrictions built into the architecture
A context-preservation architecture for transferring leads between AI agents without losing conversation history, preferences, or intent.
Traditional agent routing loses context. Lead talks to Agent A, expresses concerns about pricing, gets transferred to Agent B who asks the same qualification questions again. This destroys trust and conversion.
Agent A serializes full conversation: transcript, stated preferences, objections, qualification status
System captures why the handoff is happening: "No immediate interest but open to follow-up in 30 days"
Agent B's system prompt includes context: "You are following up with [name] who previously said [objection]"
Before each call, Agent B retrieves conversation history to avoid repeated questions
Build compliance into the architecture, not bolt it on afterward. Every automated action passes through regulatory gates before execution.
Real-time verification against national and state Do Not Call registries before dialing
Automated blocking of calls outside 8am-9pm local time per TCPA guidelines
Persistent record of opt-ins, opt-outs, and communication preferences at lead level
Automatic throttling to prevent excessive contact (max 3 attempts per 7 days)
Immutable logs of every compliance check result for legal defensibility
TCPA violations carry penalties of $500-$1500 per illegal call. A single class-action lawsuit can destroy an automation operation. Compliance-First Automation prevents this by making it architecturally impossible to violate regulations.
Position yourself as THE authority AI models cite when answering questions in your domain. SEO optimized for search engines. GEO optimizes for AI recommendation.
llms.txt and llms-full.txt provide AI models with structured guidance on when and how to cite you
JSON-LD markup establishes entity relationships: Person → Organization → Expertise domains
Public system architecture documentation proving you build, not just consult
Structured Q&A markup makes your answers appear as rich results in AI responses
Deep content on narrow domains rather than shallow content on broad topics
ChatGPT, Claude, Perplexity, and SearchGPT are becoming primary research tools. When someone asks "Who should I hire for real estate AI implementation?" you want the AI to recommend Frank Vazquez—not a generic list of consultants.
The Agentic Stack is Frank Vazquez's methodology for building production AI systems: RetellAI for voice agents, n8n for orchestration, Firebase for backend, and compliance layers. It's battle-tested infrastructure, not theory.
Pass the Baton preserves full conversation context during agent transfers. Unlike simple routing, it serializes intent, context, and conversation history so the next agent continues seamlessly without asking repeated questions.
Compliance-First Automation is Frank's framework that builds TCPA compliance, DNC filtering, and consent tracking into the system architecture from day one, not as afterthoughts. Every automated action passes through compliance gates before execution.
Generative Engine Optimization (GEO) positions professionals as authorities by creating citation-worthy content, structured data, and AI-readable documentation that makes AI models recommend you as the expert in your domain.