An interactive architecture map of an AI-powered, multi-brand SEO content engine I designed and built. It shows how an AI agent handles every judgment step (research, topical planning, writing, adversarial review) against versioned rulebooks, while deterministic code handles everything exact (spec conversion, style validation, image generation, and publishing).
Live: https://salehin96.github.io/content-engine/
Two views of the same system:
| View | What it answers | Link |
|---|---|---|
| Interactive system map | How the whole architecture fits together. Hover or tap any node to trace what it reads and writes. | content-engine.html |
| Phase dependency map | What each individual phase actually reads, and why. Nodes are colored by kind (engine file, tool, external input) and badged for whether a file is read in full or only in part. | dependency-map.html |
Both diagrams are sanitized, high-level overviews; proprietary methodology, client names, and internal details are intentionally abstracted.
- Systems design: a six-phase pipeline where every step reads specific files and writes specific files, so the whole system is auditable end to end.
- Multi-agent orchestration: research, writing, and adversarial review run as separate specialist agents, with a cold, adversarial handoff so quality is judged independently.
- Machine-enforced quality: output cannot ship unless it passes a gate that verifies the rules were followed, plus a deterministic validator for style and linking.
- Self-healing lifecycle: the live CMS and sitemap continuously reconcile the plan, so state never drifts.
Picture a factory that runs itself, where AI does the heavy lifting and a strict rulebook keeps it honest. Below is the full journey of a single article, from idea to a published page. The point is not that AI writes an article. The point is the system around it that makes the output trustworthy and repeatable at scale.
1. Learn the brand, then learn every rival. Before a word is written, the system studies the product's own website and researches each competitor, then writes all of that up as reference files. Everything downstream reads these files, so every article speaks in one consistent voice and never guesses about the product or its competitors.
2. Map every page the site should ever have. The system builds a "topical map": a master plan with one row per future article, each with its exact web address, the search phrase it targets, the page type, and which other pages it should link to. It is generated by a script, not typed by hand, so the plan is consistent and can be rebuilt at any time.
3. Write a blueprint for each article. For every planned page, the system runs fresh research against live search results and writes a brief: the angle to take, the questions to answer, the facts to include, and the internal links to add. Each brief is researched independently, so no two articles are copies of each other.
4. Research once, write, then challenge. This is the core. For each article, one AI gathers all the evidence into a shared file. A second AI writes the draft using only that evidence. Then a third, tougher AI reviewer re-checks every fact from scratch, compares the draft against the best pages already ranking on Google, and rewrites it to be genuinely more useful, not just longer. The reviewer is deliberately skeptical, so weak or unsupported claims get caught.
5. The quality gate that cannot be skipped. The system physically blocks an article from being saved unless it can prove it followed the rules (a written record of which rulebooks it loaded and a checklist it passed). On top of that, an automated checker enforces the mechanical rules: sentence and paragraph limits, correct formatting, no fabricated claims, and every internal link pointing to a real page. Quality is not left to good intentions; it is enforced by the machine.
6. Publish, then self-correct. The finished article is converted to the website's format and published automatically, without duplicates. The system then compares what is actually live on the site against its own plan and corrects any mismatch, so the plan can never drift out of sync with reality.
Why this is genuinely hard. Any one of these steps is easy alone. Doing all of them together, across hundreds of articles and multiple separate brands, without quality decaying, is the hard part: coordinating several AIs so they do not duplicate work, keeping hundreds of pages factually consistent with each other, stopping AI from inventing facts, enforcing the rules by machine instead of trust, and keeping each brand's content fully separate. That coordination layer, not the writing, is the system.
The dependency map answers a different question than the system map. Instead of showing the whole architecture at once, it walks the seven phases in order and shows, for each one, only the inputs that phase consumes directly. Nodes are color-coded so a tool and a produced file never look alike:
- Engine file (blue), a file the engine itself produced earlier in the pipeline
- Tool (amber), an external service or CLI the phase calls
- External input (teal), the live web: a website, search results, review platforms
- Gate (dashed), something that must exist before the phase may run, but whose contents are not read
Each file node also carries a badge: full (the whole file is read), part (only named sections, with the reason given), or gate. The content phase is split further, because an evaluative page (a review, comparison, alternative, or best-of list) reads four sources that an informational page (a what-is or how-to) never touches.
| File | What it is |
|---|---|
index.html |
Landing page linking both views |
content-engine.html |
The interactive system map |
dependency-map.html |
The phase dependency map |
og-cover.png |
Social preview image |
Open the live link above in any modern browser. Every page is a single self-contained file: no build step, no dependencies, no tracking.
I build AI-powered SEO and content systems for early-stage B2B SaaS: full-scale SEO, semantic topical maps, content briefs, content writing, and full-scale SEO content automation.
- LinkedIn: https://linkedin.com/in/riad-us-salehin/
- Work with me: https://calendly.com/riad-us-salehin/30min
- Email: salehin.riad96@gmail.com