SEO

SEO / GEO / AEO — Search & AI Visibility

Traditional search optimization plus optimization for AI answer engines (2026 landscape).

The Three Layers of Visibility

Traditional SEO Fundamentals

Technical SEO Checklist

✓ robots.txt allows crawling of important pages
✓ sitemap.xml submitted to Search Console
✓ Canonical URLs set on every page
✓ No duplicate content (or rel=canonical pointing to original)
✓ Mobile-responsive (Google is mobile-first indexing)
✓ Core Web Vitals pass (LCP < 2.5s, INP < 200ms, CLS < 0.1)
✓ HTTPS everywhere
✓ Structured data (JSON-LD) on key page types

Metadata Template (Next.js)

// app/blog/[slug]/page.tsx
import type { Metadata } from "next"

export async function generateMetadata({ params }: Props): Promise<Metadata> {
  const post = await getPost(params.slug)

  return {
    title: `${post.title} | YourBrand`,
    description: post.excerpt.slice(0, 160),
    keywords: post.tags,
    alternates: {
      canonical: `https://example.com/blog/${post.slug}`,
    },
    openGraph: {
      title: post.title,
      description: post.excerpt,
      type: "article",
      publishedTime: post.publishedAt,
      authors: [post.author.name],
      images: [{ url: post.ogImage, width: 1200, height: 630 }],
    },
    twitter: {
      card: "summary_large_image",
      title: post.title,
      description: post.excerpt,
      images: [post.ogImage],
    },
  }
}

Structured Data (JSON-LD)

// Article schema
function ArticleSchema({ post }: { post: Post }) {
  const schema = {
    "@context": "https://schema.org",
    "@type": "Article",
    headline: post.title,
    description: post.excerpt,
    image: post.ogImage,
    datePublished: post.publishedAt,
    dateModified: post.updatedAt,
    author: { "@type": "Person", name: post.author.name },
    publisher: {
      "@type": "Organization",
      name: "YourBrand",
      logo: { "@type": "ImageObject", url: "https://example.com/logo.png" },
    },
  }

  return (
    <script
      type="application/ld+json"
      dangerouslySetInnerHTML={{ __html: JSON.stringify(schema) }}
    />
  )
}

// FAQ schema (also boosts AEO — see below)
const faqSchema = {
  "@context": "https://schema.org",
  "@type": "FAQPage",
  mainEntity: faqs.map(faq => ({
    "@type": "Question",
    name: faq.question,
    acceptedAnswer: { "@type": "Answer", text: faq.answer },
  })),
}

AEO — Answer Engine Optimization

Goal: get your content selected as a featured snippet or voice search answer.

Question-Answer Format

## How long does it take to set up a SaaS MVP?

A typical SaaS MVP takes 4-8 weeks to launch, depending on feature scope.
The breakdown: 2-3 days for project setup, 1 week for auth and billing
integration, and 2-4 weeks for core feature development.

<!-- Direct, complete answer in the FIRST sentence — this is what gets
extracted into featured snippets and voice answers -->

AEO Content Structure

1. Question as H2/H3 heading (matches actual search queries)
2. Direct answer in first sentence (40-60 words ideal for snippets)
3. Supporting detail/context after
4. Bullet points or numbered lists for scannable answers

## What is Row-Level Security in PostgreSQL?

Row-Level Security (RLS) is a PostgreSQL feature that restricts which rows
a user can access in a table, based on policies you define. Unlike
application-level checks, RLS enforces access control at the database
layer itself.

Key benefits:
- Enforced even if application code has bugs
- Works across all database clients (not just your app)
- Reduces duplicate authorization logic

Voice Search Optimization

• Target natural language questions ("how do I...", "what is...", "why does...")
• Write answers in conversational tone — voice assistants read these aloud
• Keep primary answer under 30 words when possible (voice answers are brief)

GEO — Generative Engine Optimization (2026)

Goal: get cited, quoted, or referenced by AI systems (ChatGPT, Perplexity, Claude, Google AI Overviews).

What LLMs Look For When Citing Sources

1. Clear, extractable claims — specific facts, numbers, definitions stated plainly
2. Authoritative structure — proper headings, not walls of text
3. Recency signals — dates, "last updated," version numbers
4. Original data/research — content that can't be found elsewhere is more citable
5. Clean semantic HTML — <article>, <section>, proper heading hierarchy

GEO Content Patterns

<!-- ✅ Citable: specific, attributable claim -->
According to our 2026 survey of 500 SaaS companies, 73% use PostgreSQL
as their primary database, up from 61% in 2024.

<!-- ❌ Not citable: vague, no source -->
Most companies use SQL databases these days.

<!-- ✅ Definition format LLMs extract easily -->
**Row-Level Security (RLS)** is a PostgreSQL access-control mechanism
that filters query results based on the requesting user's identity,
enforced at the database engine level rather than in application code.

<!-- Structured for extraction: term in bold, clear definition follows -->

Author & Source Authority Signals

// E-E-A-T signals (Experience, Expertise, Authoritativeness, Trust)
// LLMs and search engines both weight these

const authorSchema = {
  "@context": "https://schema.org",
  "@type": "Person",
  name: "Author Name",
  jobTitle: "Senior Backend Engineer",
  worksFor: { "@type": "Organization", name: "Company" },
  sameAs: [
    "https://linkedin.com/in/author",
    "https://github.com/author",
  ],
}

Llms.txt (Emerging Standard, 2025-2026)

# llms.txt — at your domain root, similar to robots.txt
# Signals to AI crawlers what content is most important/citable

# YourBrand

> One-line description of what your site/product does.

## Documentation
- [API Reference](https://example.com/docs/api): Full API documentation
- [Getting Started](https://example.com/docs/start): Quickstart guide

## Key Pages
- [Pricing](https://example.com/pricing): Current pricing tiers
- [Changelog](https://example.com/changelog): Recent product updates

Landing Page SEO/AEO Writer Template

# [Primary Keyword] — [Value Proposition]

<!-- H1: include primary keyword naturally, keep under 60 chars -->

[Hook paragraph: 2-3 sentences, state the problem and your solution.
Include primary keyword in first 100 words.]

## What is [Primary Keyword]?
<!-- AEO: direct definition, extractable -->
[40-60 word direct answer]

## Why [Primary Keyword] Matters
<!-- Supporting context, secondary keywords naturally woven in -->

## How [Product] Solves [Problem]
<!-- Feature breakdown with H3s for each feature -->

### [Feature 1]
[Benefit-focused description]

### [Feature 2]
[Benefit-focused description]

## Frequently Asked Questions
<!-- AEO + FAQ schema — high snippet/voice-answer potential -->

### [Question matching real search query]
[Direct answer, 40-60 words]

### [Question matching real search query]
[Direct answer, 40-60 words]

## [CTA Section]
[Clear single call-to-action]

On-Page Checklist for Landing Pages

• H1 contains primary keyword, appears once
• Primary keyword in first 100 words
• H2/H3 use question format where relevant (AEO)
• Meta description: 150-160 chars, includes keyword + CTA
• Internal links to 2-3 related pages
• Image alt text descriptive (not keyword-stuffed)
• FAQ section with schema markup
• Page load < 2.5s LCP
• At least one specific, citable fact/statistic (for GEO)

Measuring Success

Key Rules

1. Direct answers in the first sentence — this is what gets extracted for both AEO snippets and GEO citations
2. Specific, attributable facts beat vague claims — numbers, dates, named sources are more citable by LLMs
3. Question-format headings — match real search queries and voice search patterns
4. FAQ schema everywhere relevant — cheap to add, high AEO value
5. Structured data (JSON-LD) on all key page types — Article, FAQPage, Product, Organization
6. llms.txt at domain root — emerging signal for AI crawler prioritization
7. Core Web Vitals are non-negotiable — slow pages rank worse and convert worse
8. One H1 per page, logical heading hierarchy beneath it
9. Original data/research is the highest-value content for GEO — AI systems prefer unique sources
10. Update dates visibly — "Last updated" signals recency to both crawlers and LLMs