I built two n8n pipelines that validate niche ideas against real data instead of vibes

Описание и применение

Автор создал два автоматизированных пайплайна в n8n, которые проверяют спрос и traction для нишевых идей, используя реальные данные из API Keyword Everywhere, Serper, Apify и OpenAI. Пайплайны выводят количественные показатели спроса, конкурентоспособности и монетизации, помогая избежать бессмысленного строительства.

Задача

Micro‑SaaS idea articles rely on vague “shower thoughts” rather than data, making it hard to assess real demand and traction.

Результат

The utility scanner identified a single umbrella‑site concept with ~14M monthly searches across 40+ tools, high CPCs indicating monetisation potential, and trivial client‑side build complexity. The traction scanner surfaced real products with existing user bases and acquisition prices, enabling rapid transition from signal to PRD.

Рабочий процесс

• Seed a grid of tool‑shaped search terms (e.g., "age calculator", "heic to jpg").
• Expand each seed via Keyword Everywhere API to get volume, trend, CPC, and keyword difficulty.
• Filter results to tool‑shaped queries and canonicalise duplicates.
• Score each keyword on demand, rankability, build simplicity, monetisation, and cluster strength.
• Check top‑10 SERP results with Serper and Chrome Web Store for competition.
• Pull Flippa comps for ad‑monetised content/tool sites.
• Cluster by domain, name umbrella sites with OpenAI, and write briefs to Notion.
• For traction, scan Product Hunt, Show HN, Chrome Web Store, iOS App Store, AppSumo, and Reddit using respective APIs.
• Normalize and deduplicate candidates, match against themes, score on popularity, maturity, simplicity, price fit, and recency.
• Enrich shortlisted candidates with Play Store metadata, Google Trends, and Flippa MRR/asking‑price comps.
• Write accepted briefs to a Notion database and poll it every 5 minutes to generate structured PRDs.

Доказательства

Every "micro‑SaaS ideas" article is someone's shower thoughts. I wanted to know what the data actually says, so I built two automated research pipelines in n8n — one that checks demand (is anyone searching for this?) and one that checks traction (is anyone already building and paying for this?).

Источник

Running the utility scanner against the "everyday tools" niche produced one umbrella-site concept with ~14M searches/month of combined demand across 40+ scored tools:

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The money signal is in the CPCs, not the volumes: JSON Formatter keywords run $15.05 CPC, lawn‑care tools $15.37, Sales Tax $5.49.

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The scanners tell you where to dig; they don't dig for you.

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