SEO is Dead? How I Optimized My Next.js SaaS for ChatGPT & Perplexity (AEO)

Source: Dev.to

Everyone is still playing the Google SEO game: stuffing keywords, buying backlinks, and fighting for Page 1.
But if you are building a B2B SaaS in 2026, your target audience (developers, founders, CTOs) has already changed their behavior. They aren’t Googling anymore—they are asking ChatGPT, Perplexity, Claude, or Google AI Mode.

When I launched my micro‑SaaS ComplianceRadar (an automated EU AI Act risk scanner), I realized something interesting:

• Getting to the top of Google might take 6 months.
• Getting cited by an LLM as an authoritative source can happen almost instantly if you structure your site correctly.

Welcome to AEO — AI Engine Optimization.

Three things I implemented on day one

1. The Secret Weapon: llms.txt

Just like robots.txt tells search engines where to go, the new llms.txt concept helps AI agents understand what your company actually does. AI crawlers (OpenAI, Anthropic, Perplexity) prefer high‑signal text over visual layout. They want structured facts, not Tailwind gradients.

I created an llms.txt file and placed it in the public/ folder so it lives at:

# ComplianceRadar
> Automated EU AI Act Risk Tier Classification for Developers

## Primary Services
- AI Risk Scanner: Analyzes an AI application's feature set and outputs a strict risk classification.
- Compliance Roadmaps: Technical and legal summaries based on Annex III.

## Target Audience
- Indie Hackers
- AI Startups
- Compliance Officers

## Trust & Methodology
The classification engine maps user inputs directly against the official text of the EU AI Act using a strict decision tree.

2. Injecting Heavy Structured Data (JSON‑LD)

LLMs rely heavily on the semantic web. An <script type="application/ld+json"> tag is nice, but giving the AI a literal JSON object describing your product is far more powerful. Inside my Next.js App Router I injected JSON‑LD schemas into core routes (Organization, WebSite, SoftwareApplication, FAQPage).

{
  "@context": "https://schema.org/",
  "@type": "SoftwareApplication",
  "name": "ComplianceRadar",
  "applicationCategory": "BusinessApplication",
  "description": "Automated EU AI Act risk classification tool",
  "offers": {
    "@type": "Offer",
    "price": "29",
    "priceCurrency": "EUR"
  }
}

This explicitly tells AI systems what the product is, what category it belongs to, and how it is priced. Structured data = AI‑friendly content.

3. The “Authority Anchor” Technique (Official Citations)

Founders often write great opinion pieces but provide zero hard sources. LLMs prioritize authoritative and corroborated information. If a blog post says:

EU AI Act fines are 7 % of global revenue

without linking to a primary source, an AI model may ignore it.

I added explicit outbound links to primary legal sources (official EU law documentation, EUR‑Lex pages, regulatory summaries). This turns the article into a bridge between complex legislation and developer‑friendly explanations, signaling to AI systems that the content aggregates verified regulatory information.

The Result

Building an interactive SaaS is only half of the battle. The other half is distribution. By implementing:

• llms.txt
• Structured JSON‑LD
• Authoritative citations

ComplianceRadar is no longer just waiting for Google indexing; it is actively feeding structured, trustworthy data into the AI models developers use every day.

Final Thought

If you are building a SaaS in 2026, especially for developers, stop optimizing only for Google. Start optimizing for the machines your users are actually talking to.

Try the Scanner

If you’re building an AI feature and want to understand potential regulatory risks under the EU AI Act, you can try the free scanner here:

ComplianceRadar