FAQ

Can I use your knowledge base for my own learning?

Absolutely — that’s why it’s there.

A few ways to get the most from it:

• Browse by pillar — start at the Knowledge Base home and pick a pillar that interests you (AI, SEO, Growth, E-Commerce, Tools)
• Ask the AI assistant — click the chat widget and ask a question in plain language. It’ll find the relevant articles and synthesize an answer with source links.
• Read the FAQs — quick answers to common questions, each linked to a deeper reference article
• Check the Insights — short thought-leadership takes on trends and strategies
• Follow the Guides — step-by-step walkthroughs for practical implementation

If you have questions about anything you find, or want to discuss a topic further, get in touch.

Do you take on client work?

I’m not actively seeking clients — I have a full-time role that keeps me busy. That said, I occasionally take on select projects when the fit is right and the work aligns with my expertise.

The kinds of projects that interest me most:

• Knowledge base architecture — structuring content so AI systems can reason with it
• AI-powered content systems — building intelligent content pipelines and assistants
• SEO strategy — topical authority, content clustering, and GEO optimization
• Digital marketing systems — the infrastructure that connects marketing efforts to measurable results

If you’re interested in working together, reach out and tell me about your project. The best collaborations happen when there’s genuine alignment between what you need and what I’m passionate about building.

How Do AI Agents Differ from Traditional Automation?

Traditional automation follows predefined rules: if this happens, do that. Zapier triggers, email sequences, scheduled scripts — they execute the same steps every time regardless of context.

AI agents pursue goals: achieve this outcome, figure out how. They can plan multi-step approaches, use tools, adapt when something unexpected happens, and reason about whether they’re making progress.

When to use automation: The task is well-defined, repeatable, and the conditions are predictable. Most business workflows (data syncing, email triggers, report generation) are better served by automation.

When to use agents: The task requires judgment, the conditions vary, or the workflow has too many branches to predefine. Research synthesis, content analysis, and complex troubleshooting are good agent use cases.

The best systems use both — automation for the predictable parts, agents for the parts that require reasoning.

Deep dive: Agentic vs Automation Platforms in the Knowledge Base.

How Do AI and SEO Work Together?

The relationship goes both directions:

AI enhances SEO workflows:
- Content creation — AI drafts, outlines, and optimizes content faster
- Keyword research — AI identifies patterns, clusters, and gaps in keyword data
- Technical audits — AI analyzes crawl data and identifies issues at scale
- Analytics — AI cross-references GSC, GA4, and Ads data in seconds (instead of spreadsheet hours)
- Competitive analysis — AI synthesizes competitor content and identifies strategic gaps

SEO fundamentals power AI search visibility (GEO):
- Structured content — clean headings, atomic paragraphs, and schema markup help AI systems parse your content
- E-E-A-T signals — experience, expertise, authoritativeness, and trust determine whether AI cites you
- Topical authority — comprehensive coverage of a topic makes you a more reliable source for AI models
- Fact density — specific, verifiable claims give AI systems quotable material

The takeaway: if you’re doing SEO well, you’re already building the foundation for GEO. And if you’re using AI tools for SEO, you’re working faster and surfacing insights you’d miss manually.

Deep dive: AI in SEO Overview | Generative Engine Optimization

How Does AI Change Affiliate Partner Discovery?

Traditional affiliate partner discovery is manual: search for creators in your niche, review their content one by one, check audience demographics, assess brand safety, negotiate terms. For a meaningful affiliate program, that’s weeks of work.

AI collapses this into a systematic process:

1. Scan — AI agents crawl platforms (YouTube, Instagram, blogs, podcasts) for creators in your niche using NLP to understand content themes
2. Vet — Automated analysis of audience demographics, engagement authenticity, content quality, and brand safety signals
3. Rank — Candidates scored by predicted performance based on audience alignment, engagement rates, and historical conversion data from similar partnerships
4. Monitor — Ongoing tracking of partner performance with anomaly detection for fraud or quality decline

The shift isn’t just speed — it’s coverage. A human researcher might evaluate 50 potential partners. An AI agent can evaluate 5,000 and surface the top 50 worth pursuing, including partners you’d never have found manually.

Deep dive: AI Partner Discovery and Analysis in the Knowledge Base.

How does the AI assistant on this site work?

The AI assistant uses a technique called Retrieval-Augmented Generation (RAG):

1. Your question is embedded — converted into a vector representation that captures its meaning.
2. The knowledge base is searched — the vector is matched against all articles in the knowledge base using Pinecone (a vector database) to find the most relevant content.
3. Context is retrieved — the top matching articles are pulled as context.
4. An LLM generates the answer — a language model reads the context and generates an answer grounded in the knowledge base, citing sources.

This means the assistant only answers from what’s actually in the knowledge base — not from its general training data. If the confidence score is too low (meaning the knowledge base doesn’t have a good answer), it tells you honestly rather than guessing.

You can also switch between different agent personas — Assistant, Researcher, Editor, Strategist, and Analyst — each with a different style optimized for different types of questions.

How Does This AI Assistant Work?

The assistant uses a multi-layer approach to find and deliver accurate answers:

Layer 1 — Structured Resolution
Before any AI is involved, the system checks its own structured metadata:
- Does your question match an existing FAQ title?
- Does it match a pre-mapped synthetic question on any KB article?
- Do the key concepts in your question point to specific articles?

If there’s a confident match, you get a pre-curated answer instantly — no vector search needed.

Layer 2 — Semantic Search (RAG)
If structured matching doesn’t find a confident answer, your question is converted to a vector embedding and searched against all 600+ articles in Pinecone (a vector database). The top matching articles become context for the AI.

Layer 3 — LLM Generation
The AI model (configurable — supports OpenAI, Anthropic Claude, and Google Gemini) receives the retrieved context plus your question and generates a grounded response. It’s instructed to only answer from the provided context and to say when it doesn’t have enough information.

Layer 4 — Conversation Memory
The assistant remembers your conversation within the session. You can say “tell me more about that” or “how does that apply to e-commerce?” and it understands the context from previous exchanges.

What it won’t do:
- Make up information not in the knowledge base
- Push you toward a consultation or sale
- Answer confidently when the knowledge base doesn’t have enough relevant content

Deep dive: Advanced Retrieval Techniques in the Knowledge Base.

How Is AI Used in E-Commerce?

AI touches every stage of the e-commerce buyer journey:

The biggest mistake in e-commerce AI: starting with the technology instead of the customer journey. Map where your customers drop off first, then apply AI to those specific friction points.

Deep dive: E-Commerce Knowledge Base for the complete buyer journey framework.

How is this site built?

The short version: WordPress as the CMS, with a custom-built plugin that powers everything unique about this site.

The longer version:

• WordPress + Avada — the content management layer and theme
• SIE WordPress Plugin — a custom plugin I built that handles the knowledge base CPT, AI chat assistant, agent personas, related content widgets, and integration with external services
• Obsidian — where I actually write and organize content locally, in structured Markdown with YAML frontmatter
• Automated sync pipeline — a bidirectional sync system that pushes content from local Markdown files to WordPress and pulls WordPress content back to local
• Pinecone — a vector database that indexes every article for semantic search, enabling the AI assistant to find relevant content by meaning rather than just keywords
• LLM providers — the AI assistant supports OpenAI, Anthropic (Claude), and Google Gemini as configurable providers

The whole system is designed so that content authored in Obsidian flows automatically to the live site, gets indexed for AI retrieval, and stays governed and consistent. It’s the SIE in action.

How Should I Evaluate AI Tools Before Committing?

The AI tools landscape changes weekly. New tools launch, existing ones pivot, pricing shifts constantly. Before committing time or budget to any tool, run it through the STRIVE framework:

The biggest mistake I see: adopting a tool because it demos well, then discovering it doesn’t integrate with anything else in your workflow. Integration and ROI matter more than features.

Deep dive: AI & Marketing Tools Knowledge Base for detailed evaluations of specific platforms.

Should I Use Local or Cloud AI Tools?

Use local when: You’re working with sensitive data, need offline access, want predictable costs, or are experimenting with open-source models. Tools like Ollama make running local LLMs straightforward.

Use cloud when: You need the most capable models (GPT-4, Claude Opus, Gemini Pro), real-time access to the latest releases, or don’t want to manage hardware. Cloud models are consistently better at complex reasoning, long-context tasks, and multimodal work.

In practice: Most people use both. Cloud for high-stakes work requiring the best models. Local for experimentation, privacy-sensitive tasks, and development.

Deep dive: Top 10 Local LLMs | AI Foundation Models

What AI Tools Do You Recommend for Getting Started?

You don’t need everything. Start with these five categories, in this order:

1. Foundation model — ChatGPT, Claude, or Gemini. Pick one, learn it deeply. Use it for drafting, analysis, research, and problem-solving. This alone covers 80% of what most people need from AI.

2. SEO platform — Ahrefs or SEMrush. Keyword research, rank tracking, competitor analysis, and site audits. Non-negotiable for organic visibility.

3. Analytics — Google Search Console + GA4. Both free. GSC shows how search engines see your site. GA4 shows how users behave on it.

4. Automation — Zapier, Make, or n8n. Connects your tools so data flows between them without manual work. n8n is open-source and self-hostable.

5. Knowledge management — Obsidian (free) for building structured knowledge. This is what I use to manage the entire knowledge base that powers this site.

What I’d add later: A dedicated content tool (Frase or Clearscope for SEO content), an email platform (Klaviyo or ActiveCampaign), and local AI via Ollama for privacy-sensitive work.

What I’d skip: Anything that promises to “automate your entire marketing” — the tools that try to do everything usually do nothing well.

Deep dive: AI & Marketing Tools Knowledge Base for detailed evaluations of 130+ tools.

What Are Core Web Vitals and Do They Affect SEO?

Core Web Vitals (CWV) are Google’s standardized metrics for measuring real-world user experience on the web. They’re part of Google’s Page Experience signals and serve as a ranking factor — not the most important one, but a meaningful tiebreaker.

The Three Metrics

Do They Actually Affect Rankings?

Yes, but with nuance. CWV is a ranking signal, but it won’t override relevance and content quality. Think of it as a competitive edge: if two pages are equally relevant, the one with better user experience wins.

Where CWV matters most:
- Mobile search — where performance issues are more pronounced
- Competitive SERPs — where many pages target the same keywords
- User behavior — slow, janky pages drive users away regardless of rankings

The biggest practical impact isn’t the ranking signal itself — it’s that poor CWV correlates with higher bounce rates and lower engagement, which hurt your SEO indirectly.

Deep dive: Core Web Vitals: Measuring and Optimizing User Experience in the Knowledge Base.

What Are the Five Growth Marketing Domains?

The Growth knowledge base is organized into five operational domains, each covering a distinct marketing channel with AI-driven strategies:

Each domain has its own taxonomy, SOPs, and strategic playbooks. The common thread: every strategy is built for the Agentic Web — prioritizing machine-readable content, GEO optimization, and AI agent integration alongside traditional marketing execution.

Deep dive: Growth Marketing Knowledge Base for the full domain breakdown.

What Are the Main Categories of AI Tools?

The AI tools landscape is sprawling, but it organizes into clear categories:

You don’t need tools in every category. Start with the categories that address your biggest bottleneck, and expand from there.

Deep dive: AI & Marketing Tools Knowledge Base for detailed evaluations of 130+ tools.

What Is a Knowledge Base and Why Does AI Need One?

A knowledge base is a structured collection of information — articles, guides, reference documents — organized with consistent metadata so both humans and machines can find and reason with it.

Without a knowledge base, an AI model answers from its training data — which can be outdated, generic, or flat-out wrong for your specific domain. With a knowledge base, the AI answers from your knowledge: verified, current, and domain-specific.

This is the foundation of Retrieval-Augmented Generation (RAG):
1. User asks a question
2. The system searches the knowledge base for relevant articles
3. The AI generates an answer grounded in that specific content
4. Sources are cited so the user can verify

What makes a knowledge base AI-ready:
- Consistent metadata — every document tagged with titles, summaries, concepts, and relationships
- Semantic summaries — abstracts optimized for vector search retrieval
- Machine-readable structure — clear headings, atomic paragraphs, structured data
- Relationship links — explicit connections between related documents

This site’s knowledge base has 600+ articles with all of these elements. It’s what powers the AI assistant — and it’s why the assistant can cite specific sources instead of making things up.

Deep dive: Anatomy of the SIE Knowledge Base in the Knowledge Base.

What Is Agentic Marketing?

Agentic marketing is the shift from manually executing marketing campaigns to deploying AI agents that handle discovery, execution, and optimization autonomously.

Traditional marketing automation follows rules: if subscriber opens email, wait 3 days, send follow-up. Agentic marketing uses AI agents that reason about goals: increase qualified leads from this segment by 20% — figure out how.

The key differences:

This is the direction the GROWTH knowledge base is built around — marketing strategies designed for an Agentic Web where AI agents are both the tools marketers use and the audience they optimize for.

Deep dive: Growth Marketing Knowledge Base for strategies across Ads, Email, Affiliate, Creator, and Social.

What Is an AI Agent and How Is It Different from a Chatbot?

An AI agent is an autonomous system powered by a Large Language Model (LLM) that can perceive its environment, make decisions, plan multi-step actions, and use tools to achieve a goal. A chatbot responds to what you say. An agent goes and does things.

Here’s the practical difference:

A chatbot can answer “What’s our return policy?” An agent can review a customer’s order history, check the return window, generate a shipping label, and email it — all from a single request.

The strategic value of agents is the shift from human-in-the-loop (where you constantly verify micro-tasks) to a Fleet Commander model (where you provide high-level intent and agents handle execution). This reduces what I call the Human Correction Tax — the time and cost spent fixing unreliable AI outputs.

Every agent is built on three core components: an LLM for reasoning, tools for taking action, and memory for maintaining context across steps.

Deep dive: Introduction to AI Agents in the Knowledge Base.

What Is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is the simulation of human intelligence in machines — systems designed to analyze information, recognize patterns, learn from data, and make decisions with minimal human intervention.

In practical terms, AI is the technology behind the tools you already interact with daily: search engines, recommendation systems, voice assistants, and chatbots. But its real power lies in what it enables at a strategic level:

• Efficiency — Automating repetitive tasks so humans can focus on creativity and strategy.
• Insight — Analyzing massive datasets to uncover patterns no human could spot manually.
• Personalization — Tailoring experiences at scale, from marketing messages to product recommendations.
• Accuracy — Detecting patterns and predicting outcomes with growing precision.

AI isn’t a single technology. It’s an umbrella term for several subfields — including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Generative AI — each handling different aspects of intelligent behavior.

The key distinction worth understanding: today’s AI is Artificial Narrow Intelligence (ANI) — excellent at specific tasks, but not general-purpose thinking. It augments human expertise rather than replacing it. I think of the human role as a “Fleet Commander” — providing strategic direction while AI handles tactical execution.

Deep dive: What Is Artificial Intelligence (AI)? in the Knowledge Base.

What Is Context Engineering?

Context engineering is the discipline of managing everything an AI model has access to at the moment it generates a response. This includes:

• System prompt — the model’s instructions and persona
• Tool definitions — what tools the model can call and how
• Retrieved documents — content pulled from knowledge bases via RAG
• Conversation history — prior messages in the chat
• Tool outputs — results from previous actions

Prompt engineering is a subset — it focuses on what you say to the model. Context engineering manages everything the model knows.

Why it matters: an AI model’s output quality is directly determined by the quality of its context window. Too much irrelevant information and the model gets confused. Missing key context and it hallucinates. The right context, properly curated, is the difference between an AI that sometimes helps and one that reliably delivers.

This is especially critical for agentic systems where the model makes decisions, calls tools, and executes multi-step workflows. Every decision the agent makes is shaped by what’s in its context window.

Deep dive: Agentic Context Engineering in the Knowledge Base.

What Is E-E-A-T and How Does It Affect Rankings?

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. It’s Google’s framework for evaluating content quality, drawn from its Search Quality Rater Guidelines.

Important distinction: E-E-A-T is not a single ranking factor you can toggle on. It’s a set of principles that influence many ranking systems — especially for “Your Money or Your Life” (YMYL) topics like health, finance, and legal advice.

The Four Components

• Experience — Has the creator actually done the thing they’re writing about? First-hand knowledge, case studies, and original insights signal real experience.
• Expertise — Does the creator have demonstrable knowledge or credentials in the subject? This can be formal (degrees, certifications) or practical (years of hands-on work).
• Authoritativeness — Is the creator or site recognized as a go-to source in this space? Backlinks, citations, and reputation matter here.
• Trustworthiness — Is the content accurate, transparent, and honest? Trust sits at the center of the framework — it’s the outcome of the other three.

Why It Matters More Now

With AI systems citing web sources in their answers, E-E-A-T signals directly influence whether your content gets selected. AI models prioritize sources that demonstrate clear expertise and verifiable trust. Building E-E-A-T isn’t just about Google rankings anymore — it’s about being a citable source across the entire AI search ecosystem.

Deep dive: E-E-A-T Signals in the Knowledge Base.

What Is GEO (Generative Engine Optimization)?

Generative Engine Optimization (GEO) is the practice of structuring content so that AI search systems — like Google’s AI Overviews, Perplexity, and ChatGPT — discover it, understand it, and cite it as a source in their generated answers.

Traditional SEO optimizes for a list of blue links. GEO optimizes for being the source AI models pull from when synthesizing answers.

How GEO Differs from SEO

The Four Core GEO Strategies

1. Fact density — Pack your content with specific, verifiable claims. Original data, statistics, and citations make your content more “quotable” by AI.
2. Semantic structure — Use clear headings, atomic paragraphs, and structured data (schema). AI models parse structured content more reliably.
3. Entity authority — Build consistent information about your brand/authors across trusted sources. AI models cross-reference entities.
4. E-E-A-T signals — Experience, Expertise, Authoritativeness, and Trust are even more critical for GEO. AI systems prioritize trustworthy sources.

GEO doesn’t replace SEO — it builds on it. Strong technical SEO and quality content are still the foundation. GEO adds an optimization layer for how AI models consume and cite that content.

Deep dive: Generative Engine Optimization (GEO) in the Knowledge Base.

What Is Predictive Engagement Scoring for E-Commerce?

Predictive engagement scoring uses AI to analyze visitor behavior in real-time and assign a conversion probability score. Instead of treating all visitors the same, the system identifies who’s likely to buy, who’s on the fence, and who’s about to leave.

Signals the AI watches:
- Pages viewed and view sequence
- Time on page and scroll depth
- Mouse movement and click patterns
- Cart additions and removals
- Return visit frequency

What happens at each threshold:

The shift is from reactive (“they abandoned the cart, send an email”) to predictive (“they’re about to abandon, intervene now”). That timing difference can mean a 15-30% improvement in recovery rates.

Deep dive: AI for CRO, Predictive Engagement & Cart Abandonment Recovery in the Knowledge Base.

What Is Prompt Engineering and Why Does It Matter?

Prompt engineering is the art of crafting clear, effective instructions to get useful output from AI language models. It’s the bridge between what you want and what the model delivers.

Think of it this way: the same AI model can give you a mediocre answer or a brilliant one — the difference is often entirely in how you ask.

The Basics

Good prompts follow a few core principles:

• Be specific — “Summarize the benefits of solar energy in three sentences” beats “Tell me about solar energy.”
• Provide context — Give the model background it needs to understand your request.
• Assign a role — “You are an expert copywriter” dramatically changes output quality and style.

Prompt Engineering vs. Context Engineering

These terms get confused often:

• Prompt engineering is about what you say to the model — your direct instructions.
• Context engineering is about everything the model knows at a given moment — system prompts, tool definitions, retrieved documents, message history, and more.

Prompt engineering is a subset of context engineering. Both matter, but prompting is where you start.

Why It Should Be Your First Move

Before investing in complex solutions, always try to solve the problem with better prompts first:

1. Prompt Engineering — fastest, cheapest, most iterative
2. RAG (Retrieval-Augmented Generation) — when the model needs external knowledge
3. Fine-Tuning — last resort for teaching new behaviors

Deep dive: Prompt Engineering Basics in the Knowledge Base.

What Is RAG (Retrieval-Augmented Generation)?

RAG is a technique that gives AI models access to external knowledge at query time. Instead of relying solely on what the model learned during training, RAG retrieves relevant documents from a knowledge base and includes them as context when generating a response.

The process:
1. Your question is converted into a vector embedding (a numerical representation of meaning)
2. That vector is compared against all documents in a vector database to find the most relevant matches
3. The matching documents are retrieved and passed to the AI model as context
4. The model generates an answer grounded in that specific context

Why it matters: Without RAG, AI models answer from their training data — which can be outdated, generic, or simply wrong for your domain. With RAG, the model answers from your knowledge, making responses accurate, current, and verifiable.

The AI assistant on this site uses RAG. When you ask a question, it searches the knowledge base via Pinecone (a vector database), retrieves the most relevant articles, and generates an answer grounded in that content. That’s why it can cite specific sources.

Deep dive: Embeddings and Vector Databases in the Knowledge Base.

What Is Responsible AI and Why Should I Care?

Responsible AI is the discipline of designing, building, and operating AI systems in ways that are safe, ethical, compliant, and aligned with human values. It’s about moving from “what is technically possible” to “what is acceptable and sustainable.”

Why It Matters Now

As AI systems become more autonomous — not just generating text but taking actions inside real business workflows — the risk model changes. The concern isn’t just “bad outputs” anymore. It’s bad transactions: payments processed incorrectly, records updated without authorization, access granted to the wrong systems.

A useful way to think about it: treat autonomous AI agents as digital insiders — actors operating inside your systems with varying degrees of privilege. Like human employees, they need guardrails, oversight, and accountability.

The Eight Core Principles

1. Beneficial and Purposeful Use — Deploy AI to create clear, legitimate value.
2. Privacy and Data Protection — Handle personal data lawfully and minimally.
3. Fairness and Non-Discrimination — Avoid unjust bias and inequitable outcomes.
4. Transparency and Explainability — People should know when AI is used and understand its limits.
5. Human Oversight and Accountability — Humans remain in charge; someone is always responsible.
6. Security — Protect AI systems from compromise and misuse.
7. Intellectual Property — Respect ownership and attribution.
8. Operational Excellence — Monitor, evaluate, and continuously improve.

These principles are technology-agnostic — they apply whether you’re working with predictive models, generative AI, or fully autonomous agentic systems.

Deep dive: Responsible AI Principles in the Knowledge Base.

What Is SEO and Why Does It Still Matter?

Search Engine Optimization (SEO) is the practice of improving your website’s visibility in organic (non-paid) search results. When 93% of online experiences still begin with a search engine, showing up matters.

SEO rests on three pillars:

• Technical SEO — Making sure search engines can crawl, index, and render your site. Think site speed, mobile-friendliness, structured data, and clean architecture.
• On-Page SEO — Optimizing the content itself. Title tags, headings, keyword placement, internal linking, and content quality.
• Off-Page SEO — Building authority through external signals. Backlinks, brand mentions, and reputation across the web.

But what about AI search?

SEO isn’t disappearing — it’s expanding. With Google’s AI Overviews and tools like Perplexity synthesizing answers from web content, your content now needs to be good enough that AI models choose to cite it. This evolution is called Generative Engine Optimization (GEO), and it builds directly on strong SEO fundamentals.

The key difference from paid advertising: SEO traffic compounds over time. Ads stop the moment you stop paying. A well-optimized page can drive traffic for years.

Deep dive: What Is SEO? A Comprehensive Guide in the Knowledge Base.

What is the Knowledge Base and how is it organized?

The knowledge base is a structured library of everything I’ve learned across 30 years in digital marketing — organized for both human readers and AI systems.

It’s built around six pillars:

• The CORE — Architecture, governance, and operating principles behind the SIE
• AI Knowledge — From fundamentals to advanced agentic frameworks
• SEO Knowledge — Search strategy, technical SEO, and AI-driven optimization
• Growth — Lead generation, conversion, analytics, and marketing strategy
• E-Commerce — Product strategy, merchandising, and online store operations
• AI Tools — Technologies I use and evaluate for marketing, content, and automation

Every article carries consistent metadata — semantic summaries, synthetic questions, key concepts, and relationship links — so the AI assistant can reason with the content, not just search it.

You can browse the full knowledge base here.

What Is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open standard that defines how AI systems connect to external tools and data. Instead of every AI app building custom integrations from scratch, MCP provides one universal interface.

It’s often called “the USB-C of AI” — one consistent plug that connects models to external systems safely and predictably.

Why It Exists

AI models are powerful reasoners, but they’re isolated by default. They can’t check your database, call an API, or read a file unless someone builds that bridge. MCP standardizes the bridge so it works across any AI application.

Introduced by Anthropic in 2024, MCP has been adopted by OpenAI, Microsoft, Google, JetBrains, Supabase, and others — making it a foundational layer of the agentic AI ecosystem.

How It Works: Three Components

MCP follows a client-server architecture with three roles:

1. The Host — The user-facing AI application (a chat app, an IDE, a custom assistant). It captures your input and displays responses.

2. The Client — A component inside the Host that handles communication with MCP Servers. Think of it as the adapter — the Host decides what to do, the Client knows how to talk to the server.

3. The Server — The external program providing capabilities (tools, data, APIs). It wraps functionality in a standardized way any MCP Client can invoke.

This architecture means you can add new capabilities to your AI system just by connecting a new MCP Server — no custom code required.

Deep dive: MCP Foundations and Architecture in the Knowledge Base.

What is the Strategic Intelligence Engine (SIE)?

The SIE is my ongoing exploration of a question: What if a website’s knowledge base was structured so well that AI agents could actually reason with it?

It’s the system powering this entire site — a living knowledge architecture with three core layers:

1. The Master Hub — A structured knowledge base with consistent metadata, semantic summaries, and machine-readable formatting across every article.
2. The Knowledge Pipeline (KPL) — An operational layer that automates content updates and ensures the knowledge base stays current and governed.
3. The Agent Loop — Autonomous AI agents that enrich content, identify gaps, and perform maintenance.

It started as an experiment, became an obsession, and turned into a fully operational system. The AI assistant you can chat with on this site is a live demonstration of what the SIE enables.

What Is the STRIVE Framework for E-Commerce AI?

STRIVE is a six-criteria evaluation framework used throughout the e-commerce knowledge base for assessing AI tool categories:

The framework prevents the most common e-commerce AI mistake: buying a tool because the demo looked impressive, then discovering it doesn’t integrate with your platform, costs more than it returns, or handles customer data in ways that create compliance risk.

Apply STRIVE before any tool commitment. It takes 30 minutes and saves months of regret.

Deep dive: AI Strategy for E-Commerce in the Knowledge Base.

What Is the William Bernard Standard?

The William Bernard Standard is the ethical governance framework that underpins the Strategic Intelligence Engine. Named after my father, it translates principles of integrity into architectural constraints — rules the system enforces, not just follows.

The Four Pillars:

• The Quiet Hand (Humility & Service) — Focus on helping the user, not self-promotion. Cite sources rather than taking credit. Prioritize the human over the process.
• The Iron Word (Reliability) — Commitments are explicit and honored. Systems are designed for auditable, verifiable performance. If the system says something, it’s traceable.
• The Unshakable Compass (Integrity) — Prioritize long-term value over short-term gains. Always advise based on the user’s best interests, even when it’s difficult.
• The Steady Presence (Antifragility) — Build resilient systems that handle stress and learn from failure. Every system failure becomes a protocol update.

These aren’t aspirational values on a wall. They’re encoded into the AI assistant’s behavior: it cites sources, admits when it doesn’t know, refuses to fabricate, and gets smarter from its mistakes.

Deep dive: The William Bernard Standard in the Knowledge Base.

What Is Topical Authority and How Do I Build It?

Topical authority is the demonstrable expertise and trustworthiness your website holds on a specific subject. It’s what separates a site that ranks for one keyword from a site that dominates an entire topic.

How It Works: The Pillar-Cluster Model

Instead of writing isolated articles targeting individual keywords, you build an interconnected network:

1. Pillar page — A comprehensive overview of the broad topic (e.g., “SEO Fundamentals”)
2. Cluster pages — Detailed articles covering subtopics (e.g., “What Is E-E-A-T,” “How Search Engines Work,” “Search Intent”)
3. Internal links — Every cluster page links back to the pillar, and the pillar links out to each cluster

This creates a web of content that signals deep expertise to search engines.

Why It Matters

Google’s ranking systems now evaluate quality holistically across your entire site, not just page-by-page. An isolated brilliant article has far less impact than a well-structured network of good articles covering a topic comprehensively.

This also makes your site more resilient to algorithm updates. Sites with genuine topical depth tend to recover from core updates faster — they’re building a durable asset, not gaming individual rankings.

This knowledge base is itself an example of the model in action: six pillars, hundreds of interconnected articles, all structured with consistent metadata.

Deep dive: Topical Authority: The Pillar-Cluster Strategy in the Knowledge Base.

What topics do you write about?

The site covers the intersection of digital marketing, AI, and intelligent systems — organized around six pillars:

• AI — fundamentals, models, agents, prompt engineering, MCP, ethics and governance, future trends
• SEO — fundamentals, research and strategy, content optimization, technical SEO, AI-driven search (GEO), measurement
• Growth — lead generation, email marketing, affiliate programs, social media, advertising, creator/influencer marketing
• E-Commerce — strategy, acquisition, engagement, conversion, retention, and emerging trends
• Tools — evaluations and guides for AI, analytics, content, SEO, and automation platforms
• The CORE — how the Strategic Intelligence Engine itself works

I also publish FAQs (quick answers), Insights (thought-leadership takes), and Guides (practical walkthroughs) that distill knowledge base articles into more accessible formats.

Who is Adam Bernard?

I’m a digital marketing professional with 30 years in the industry — spanning SEO, web development, content strategy, e-commerce, and AI implementation. I currently work full-time in B2B marketing in the precast concrete sector.

On the side, I build the Strategic Intelligence Engine (SIE) — a passion project exploring how structured knowledge and AI agents can power smarter websites. This site is where I share what I’ve learned, what I’ve built, and what I’m working on.