SEO and marketing decision
Scalenut
A competent developer can recreate Scalenut’s core workflow (research → generate → publish → audit) using existing tools and the cited open-source SEO agent stack, but the full commercial product (backlinks marketplace, managed strategists, broad AI-visibility across many engines, and polished integrations) is not realistic to replicate quickly.
Visit website↗No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Generate SEO research -> create optimized long-form content -> publish -> monitor rankings and AI visibility
What it still won’t have
- Backlinks marketplace and managed link-building
- Dedicated strategists / human-in-the-loop managed service
- Scale, polished integrations, and enterprise SLAs
- Proprietary datasets and any internal AI-agent orchestration not published
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 5 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build an open-source minimal AI-SEO platform using Next.js (React) + Node.js backend, PostgreSQL for data, and Redis for queues. Implement: (1) a SERP scraper service (Puppeteer) that collects top-10 competitor content for a query and stores TF/IDF and headings; (2) a keyword clustering microservice that groups keywords and exposes API endpoints; (3) an LLM-driven long-form editor that calls the OpenAI API (configurable key), generates outlines, and computes a basic 'optimization score' from competitor signals; (4) a site crawler/content-audit that evaluates on-page issues and stores reports in Postgres; (5) a WordPress publishing connector using WP REST API. Out of scope: backlinks marketplace, paid human strategist workflows, multi-engine AI-visibility tracking (only include a stubbed job to record SERP positions). Provide error handling, input validation, unit tests for core services, and Docker compose for local setup. Include README with deployment steps and a simple GitHub Actions CI that runs tests.
How we checked
How the score was reached
- Partly verdict base52
- 3 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score62
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.
How scoring works →Cited sources · 3
Every page the run actually retrieved.
- official productScalenut - official product
- official pricingScalenut Pricing
- official docsScalenut Help Docs
Integrity checks
What held up, and what did not.
