A full-stack software engineer implements SEO, AEO, and GEO at the architecture level — server-rendered HTML, schema embedded in the component structure, Core Web Vitals hit in the codebase, and AI crawlers able to read every page from day one. Most SEO consultants work on top of someone else's code. An engineer builds the code to rank from the first line.
The quick answer
I'm Brian K Clayton. I'm a full-stack software engineer based in Los Angeles, and I've spent years at the intersection of two disciplines most people treat as separate: writing production code and making that code findable. Most developers don't go deep on search. Most SEO consultants don't write code. I do both — and in 2026, that matters more than it ever has.
Search is no longer one thing. It's Google, Bing, AI Overviews, ChatGPT, Perplexity, Gemini, Claude. Each one has different requirements. Satisfying all of them requires someone who can work at every layer of a website — from HTTP headers to schema markup to content structure. That's what this blog is about, and that's what I do for clients.
The gap most businesses don't see
A business hires an SEO agency, gets a list of recommendations, hands it to their developer. The developer implements what they understand and skips what they don't. Three months later, nothing moved and nobody knows why.
Or the inverse: a developer builds a beautiful website with no schema, no semantic HTML structure, no thought given to how a crawler reads it. The site looks great and ranks nowhere.
Both situations have the same root cause: the person making technical decisions and the person making SEO decisions are different people with different goals. When I work on a site, there's one person doing both. Every architectural decision gets made with search visibility baked in — not bolted on after the fact.
Schema implemented in a component renders correctly on every page that uses it. A plugin applies it inconsistently and breaks on updates. The difference is whether the person doing SEO can write code.
What full-stack engineering means for SEO
I'm making decisions at five layers simultaneously when I build a site. Most consultants only touch one or two.
Layer 1 — Rendering and crawlability
Search engines and AI crawlers need fully-rendered HTML. A Next.js app rendering everything client-side looks invisible to a crawler. I choose SSR, SSG, or hybrid based on what each page needs for both users and crawlers. Static marketing pages get clean static HTML with no JavaScript dependency. This is a build-time decision, not something you fix with a plugin.
Layer 2 — Semantic HTML and document structure
A single properly nested h1, logical heading hierarchy, main, article, nav, aside — not just accessibility. That's how search engines understand what a page is about. I write HTML that means something rather than a sea of div tags requiring JavaScript to interpret.
Layer 3 — Schema and structured data
JSON-LD schema is where most sites leave the most on the table. I implement Organization, Person, LocalBusiness, Service, Article, FAQPage, and BreadcrumbList as page-specific data — not a plugin applying one template to everything. AI engines read this to understand and trust your content. Get it right and you become the cited source.
Layer 4 — Core Web Vitals and performance
LCP, CLS, and INP are Google ranking factors and they're engineering problems. Heavy theme, unoptimized images, render-blocking scripts — fixable at code level. I optimize these during the build. The sites I build consistently score green on PageSpeed Insights because performance is a requirement, not an afterthought.
Layer 5 — Crawl configuration and robots strategy
Which pages get indexed. How link equity flows. Whether canonical tags are accurate. Whether the sitemap reflects reality. I also configure robots.txt to explicitly allow AI crawlers — GPTBot, PerplexityBot, ClaudeBot, Google-Extended — because if they can't read your site, they can't cite it.
AEO: the technical problem nobody talks about
AEO is widely discussed as a content strategy. But the implementation layer is where most sites fail — and it's a purely technical problem.
For an AI answer engine to cite you, several things must be true simultaneously: the page is crawlable, content is server-rendered, FAQPage schema is valid JSON-LD, each answer is self-contained, and the entity is consistently identified across the site. Every requirement is a technical implementation decision.
Write a direct, self-contained answer near the top of the page. Implement valid FAQPage and Article schema. Ensure the page is server-rendered. Build a consistent entity identity. Allow AI crawlers in robots.txt. Do all five and you give the engine everything it needs to cite you.
GEO: getting cited by generative AI
When someone asks ChatGPT or Perplexity "who's the best web developer in Los Angeles" — is your name in the answer? Getting there requires entity credibility: the model needs to have encountered your name and expertise consistently enough across sources to trust recommending you.
The on-site requirements: Person schema with a complete knowsAbout array. sameAs links connecting all your properties. Specific, factual content — generative AI favors concrete, verifiable claims over marketing abstractions. A technically clean site AI crawlers can fully index and trust.
My full technical stack
Vague "expertise" claims fill every consultant's website. Here's what I actually work with at production level:
Front-end and full-stack frameworks
React and Next.js (App Router and Pages Router), TypeScript, Vue.js and Nuxt.js, GraphQL, Tailwind CSS. Next.js is my primary modern stack — server-side rendering, static generation, and excellent Core Web Vitals out of the box.
Back-end and application development
Laravel (PHP) for custom web apps, REST APIs, and admin panels. WordPress at the theme and plugin level — custom development, not page-builder assemblies that bloat and break.
Automation and business systems
n8n for workflow automation — connecting CRMs, email, forms, AI models, and APIs into automated pipelines. ERPNext for operations — CRM, inventory, accounting, HR, and project management on one open-source platform.
Infrastructure and DevOps
Linux system administration across Ubuntu, Fedora, and Kali. Docker for containerization. Self-hosted cloud infrastructure including production Nextcloud deployments. Comfortable at the command line, comfortable owning a full stack from DNS to deployment.
AI and emerging technologies
AI agent development and LLM integrations — building systems that use language models as a processing layer in automated workflows: classification, summarization, lead qualification, and content generation pipelines combined with n8n.
Security
Cybersecurity through Clayton Enterprises — threat detection, endpoint device security, data protection, and backup and recovery systems.
Crawlability is a server configuration. Schema is a JavaScript object rendered in HTML. Performance is a build process. Entity clarity is a data architecture decision. You can't optimize for search at any of these layers without being able to work at all of them.
The three-layer approach: how SEO, AEO, and GEO work together
SEO is the foundation. Clean code, fast load times, semantic HTML, accurate meta, crawlable structure, strong internal linking, local signals. If this layer is broken, nothing above it works.
AEO is the extraction layer. Once a page is crawlable, the question is whether its content can be extracted as a direct answer. FAQPage schema, self-contained answer statements, entity clarity. AEO turns ranked pages into cited answers.
GEO is the trust layer. Whether generative AI models trust your entity enough to proactively recommend you when asked a broad question. It compounds — the more you publish well-structured, citable content, the more AI models recognize your entity as authoritative.
Real proof: Essential Foods Group
I designed and built the web presence for Essential Foods Group, a California-based national CPG brokerage. Clean static HTML with folder/index.html routing. Full schema implementation across every page. A dedicated Los Angeles page for local search. Optimized for Core Web Vitals from the build. No page builders. No bloat.
"Brian's expertise, reliability, and commitment to outstanding outcomes were clear at every stage. If you're looking to enhance your website and grow your brand, Brian is the perfect collaborator."
Armond Anderson — CEO, Essential Foods Group
What this blog covers going forward
This is the first post in an ongoing series documenting how I approach the technical side of search visibility. Not strategy — actual implementation. Upcoming: auditing your site for AI crawler access; schema types that matter most for local businesses; why static HTML outperforms WordPress on Core Web Vitals; how to structure an n8n automation that qualifies inbound leads; building a GEO content strategy that compounds over months.
If there's a specific topic you want covered — a technical problem you're hitting, a question about AI search you can't find a straight answer to — reach out. The best posts come from real problems.
Want this applied to your site?
Free website and AI visibility audit — direct review of your technical SEO, Google and AI search visibility, and lead generation gaps.
Los Angeles-based full-stack software engineer and technical consultant. Builds websites that rank on Google and get cited by AI engines, and automates the lead generation systems behind them. Founder of Clayton Enterprises.
Frequently asked questions
A full-stack engineer implements SEO at the code level — server-rendered HTML, schema in the component architecture, Core Web Vitals optimized in the codebase, and crawlability by design. Most SEO consultants work on top of someone else's code and are limited by what the platform allows.
AEO structures content so AI search engines extract and cite your business as a trusted answer. An engineer implements FAQPage and Article schema correctly in the HTML, ensures server-rendered content for AI crawlers, and structures pages around direct question-and-answer pairs.
GEO optimizes content so generative AI tools like ChatGPT, Gemini, Claude, and Perplexity surface and recommend your brand. AI engines can only cite what they can read and trust — robots.txt, clean server-rendered HTML, structured data, and entity schema are all engineering decisions that determine this.
React, Next.js, TypeScript, Laravel, WordPress, Vue.js, and Nuxt.js for web development. n8n for workflow automation. ERPNext for business systems. Docker for containerization. Linux (Ubuntu, Fedora, Kali) for system administration. AI agents and LLM integrations for automation.
SEO targets ranked links in traditional search engines. AEO targets being cited as the direct answer by AI-powered answer engines. GEO targets being surfaced and recommended inside answers generated by ChatGPT, Gemini, and Perplexity. All three require both strong content and correct technical implementation.
Yes. Based in Los Angeles, works with businesses across California and nationwide. Book a free website and AI visibility audit to start — covers technical SEO, Google and AI search visibility, and lead generation gaps.