How I Built a Review Site with 800+ Articles Using AI

The stack, the workflow, and what actually worked

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A few months ago, I wanted to build a review site for Chinese consumer brands — products like GaN chargers, USB-C hubs, smart home devices, and laptops that are popular in Asia but don't get much coverage in English-language tech blogs.

The goal was simple: produce useful, data-driven reviews at scale. No clickbait, no affiliate-first garbage. Just honest comparisons with real specs and real user feedback.

Here's how I built it, what the workflow looks like, and what I learned along the way.

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1. The Stack

The site runs on a minimal stack:

• Next.js (static export) — fast builds, great DX
• Decap CMS — Git-based CMS so editors don't need a database
• Vercel — free hosting, instant rollbacks
• GitHub — content and code in one repo

No backend to manage. Every article is a Markdown file in the repo. Decap CMS gives the content team a nice UI on top, but the source of truth is Git.

2. Why AI for Content?

I didn't want to build yet another AI-generated content mill. The approach was different:

• Research first: AI gathers product specs, pricing, and real user reviews from multiple sources
• Human structure: Each article follows a template (overview → specs → performance → real user feedback → verdict)
• Data verification: Pricing and specs are checked against official sources before publishing
• Images are real: No AI-generated product images. Every photo comes from official brand sites or verified listings

The AI handles the heavy lifting — research, formatting, translation of Chinese reviews — while humans control the quality bar.

3. The Content Pipeline

Here's the actual workflow for each article:

1. Product selection — Identify trending products on JD.com, Taobao, and Tmall
2. Spec collection — Pull official specs from brand sites and verified product pages
3. User review aggregation — Collect real buyer feedback (good and bad) from verified purchasers
4. Article generation — Structure everything into a consistent, readable format
5. Review pass — Check all specs against official sources, verify pricing, remove any hallucinated claims
6. Image sourcing — Download official product images from brand sites (no guessing CDN URLs)
7. Publish — Commit to Git, Vercel deploys automatically

4. What Actually Worked

✓ Real data beats SEO tricks

The articles that perform best aren't the ones with keyword-stuffed titles. They're the ones with actual benchmarks and real user experiences. A USB-C cable buying guide with measured charging speeds and compatibility testing gets more engagement than any generic listicle.

✓ Consistency matters more than perfection

Publishing 3-5 articles daily (focused, well-researched ones) built organic traffic faster than trying to write one perfect article per week. Search engines reward freshness.

✓ User reviews are gold

Chinese e-commerce platforms have incredibly detailed review systems, often with photos. Translating and aggregating authentic user feedback gives articles depth that pure spec sheets can't match.

5. What Didn't Work

✗ Pure AI generation

Early tests with full AI generation produced articles that looked good but lacked depth. They'd say "great product" without explaining why. The fix was adding real user quotes and verified test data.

✗ Guessing CDN image URLs

We tried building an automated image pipeline using pattern-matched CDN URLs. It failed constantly. The solution was going back to sourcing images manually from official brand sites.

✗ Over-optimizing for search

The first batch of articles tried too hard to match search patterns. They read like SEO sludge. The fix was writing for humans first and treating keywords as a secondary concern.

6. Numbers After 800 Articles

• 800+ published articles across 19 categories
• 100% real product images (every article has at least one genuine photo)
• Organic traffic growing steadily since launch
• AdSense approved and running

Not explosive growth, but steady, sustainable progress.

7. Key Takeaways

1. AI is a multiplier, not a replacement — The best results come from AI handling research and structure while humans handle verification
2. Content quality is a flywheel — Good content attracts better readers, which attracts better engagement, which signals quality to search engines
3. Don't skip the boring parts — Checking specs against official sources, sourcing real images, and verifying user reviews takes time but builds trust
4. Ship fast, iterate faster — Get the first 50 articles up, then improve based on what the data tells you

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If you're building something similar or have questions about the workflow, drop a comment below. Happy to share more details about specific parts of the pipeline.