The Challenge
Building a software alternatives site the traditional way means writing dozens — sometimes hundreds — of comparison pages by hand. Each page needs unique content, proper SEO markup, structured data, and internal linking. At that scale, manual content creation becomes the bottleneck.
We wanted to launch Clord with comprehensive coverage across 10 popular software tools, each with 5 use-case-specific comparison pages. That's 50 detailed comparison pages plus 10 hub pages — 60 pages minimum, all needing to be genuinely useful to readers.
What We Actually Built
In a single overnight session, we generated:
- 10 hub pages — one for each software tool (Notion, Slack, Trello, etc.)
- 50 comparison pages — 5 use cases per tool, each with 5-6 detailed alternatives
- 300+ alternative reviews — with pros, cons, pricing, ratings, and verdicts
- 250+ FAQ items — unique, contextual questions and answers for every page
- Full internal linking — every page connects to related comparisons
- Schema.org structured data — FAQPage, Article, and CollectionPage markup on every page
- Programmatic sitemap — auto-generated from the content
The total? 66 SEO-optimised pages with roughly 200,000 words of unique, structured content.
The Architecture That Made It Possible
The key insight wasn't just "use AI to write content." It was designing a system where AI could produce consistently structured, high-quality output at scale.
Here's the high-level approach:
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Data-driven page generation — every page is rendered from structured JSON, not from freeform markdown. This means consistency is guaranteed by the template, not by the AI's mood.
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Typed schemas — we defined TypeScript interfaces for every data structure before generating a single word. The AI fills the schema; the code renders it.
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Separation of content and presentation — content lives in
/data/as JSON files. The Next.js app reads them at build time. This means we can regenerate content without touching the codebase. -
SEO built into the architecture — canonical URLs, breadcrumbs, structured data, and internal linking are all computed from the data, not manually added.
The Results
Every page launched with:
- Unique title tags and meta descriptions
- Proper H1 > H2 > H3 heading hierarchy
- FAQPage structured data (for potential rich snippets)
- BreadcrumbList structured data
- Internal links to related comparisons
- Comparison tables for quick scanning
- Detailed alternative reviews with pros/cons/pricing
What We're Not Telling You (Yet)
The exact prompting strategies, agent coordination patterns, and quality control workflows we used are the kind of thing we'll cover in depth in a future course.
What we can say: this wasn't a "paste into ChatGPT and hope for the best" situation. It involved structured prompting, output validation, and iterative refinement. The architecture did most of the heavy lifting — the AI was filling a well-designed system, not inventing one.
Key Takeaways
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Design the system first, generate content second. If your architecture enforces quality (typed schemas, consistent templates), AI-generated content becomes dramatically more reliable.
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Programmatic SEO is a multiplier. One well-designed template turns 1 page into 66. The marginal cost of each additional page approaches zero.
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Structured data beats freeform content. JSON schemas give you consistency, testability, and the ability to programmatically validate everything.
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AI is the content engine, not the architect. The human decides what to build and how it should work. The AI fills in the details at scale.
If you're building content-heavy sites and still writing every page by hand, you're leaving an enormous amount of leverage on the table.