Self-growing websites are sites that keep adding pages and fixes after launch, with software doing the research and the writing. Greta.sh listed its Self-Growing Websites product on Product Hunt on 2 November 2026 as a site that "can do its own SEO & AEO automatically." For B2B tech marketers, such tools help most with volume and site upkeep. Accuracy and expert proof still have to come from people.
What Greta.sh says its self-growing websites do
Greta.sh is run by Questera AI, according to the footer of its site. Its homepage describes the product in one line: Greta builds the site, puts it live, "and keeps adding pages you approve."
The self-growing websites page lays the work out in four steps: research, suggest, approve and publish. Greta checks your site and the sources you choose, proposes articles, pages and fixes, and waits for approval. The page says "Nothing goes live until it's approved." Once publishing is switched on, an approved schedule publishes new articles on its own.
The page lists five kinds of work: article, schedule, fix, page and outreach. Article ideas can come from trends on Reddit, YouTube or competitor sites. For search, Greta checks titles, descriptions and similar elements and suggests fixes. For AI search, it checks that assistants such as ChatGPT can read the site.
The Product Hunt listing adds a little more. It describes an MCP server so you can run the site from Claude, ChatGPT or Cursor. In the launch comments, the makers say AI visibility is checked by running buyer-style prompts against ChatGPT, Perplexity and Google AI Overviews, and by watching referrer and bot traffic in server logs. Neither page lists prices; Product Hunt shows a "Free Options" tag.
The Product Hunt tagline uses "SEO & AEO." Greta's own site does not use the term AEO; it says "Google and AI search."
Where automatic SEO and AEO tools help
For a small team, the honest value of these tools is time. Most technical SEO upkeep is repetitive: missing titles, weak meta descriptions, broken pages, slow loads. Software that finds and proposes fixes for those every week saves hours.
Topic research also gets faster. Watching Reddit threads, YouTube trends and competitor pages by hand is slow, and a tool that turns them into a list of article ideas gives a marketer something to react to.
A review queue with a schedule also keeps a blog going. Many B2B sites publish in bursts and then stop for months. A queue of drafts waiting for approval makes it easier to keep a steady pace.
Greta's approval step helps here, because a tool that waits for a person to sign off is easier to use responsibly than one that publishes by default.
What they cannot do for hard-to-explain technical products
Our work at Mustard Seed is helping technical products land in new markets: backup and disaster recovery, DCIM, cloud and DevOps tooling, AI software. In these categories, the buyer is often an engineer who will check a claim against the docs. Three problems stay with the vendor whatever tool writes the draft.
Accuracy
Google's guidance on using generative AI content says generative models predict likely word sequences and can produce hallucinations, and that creators should fact-check and review AI-generated content before publishing. The same review applies to title elements, meta descriptions, structured data and image alt text, since those can show up in search results.
For a backup product, one wrong line about which hypervisor is supported, or what an RPO figure means, can cost a deal. A reviewer who only checks tone will not catch it. Someone who knows the product has to read every technical claim.
Expert proof
Google's page on creating helpful, reliable, people-first content asks who made the content, how and why. It says pages should "clearly demonstrate first-hand expertise and a depth of knowledge," and it treats made-up authors as deceptive. A tool can draft an article about RPO and RTO, but it cannot supply the test results, the customer setup or the support ticket pattern that make a page worth citing. Those come from your engineers, your support team and your customers.
Citations in AI answers
A site that ChatGPT or Google AI Overviews can read still has to earn a mention. Being named in an answer depends on whether the page says something specific and checkable that other sources do not. Generic articles built from the same Reddit threads and competitor pages that everyone else reads give an answer engine little reason to cite you. We covered this in more depth in our post on AEO tools and AI visibility.
The Google line to stay on the right side of
Google's spam policies define scaled content abuse as "when many pages are generated for the primary purpose of manipulating search rankings." One of the examples is using generative AI tools to generate many pages "without adding value for users." The policy applies however the content is made.
The helpful content page also lists a warning sign: extensive automation to produce content on many topics. A weekly schedule of approved articles is a long way from that. A schedule that nobody reads closely can drift toward it.
A working setup for B2B tech teams
If you try a self-growing website tool, or any automatic SEO content system, a few habits keep it useful:
- Give it the narrow topic list your buyers search for, and keep it away from topics where you have no expertise.
- Have a product expert approve every technical claim, every number and every compatibility statement before it goes live.
- Add something the tool cannot: a test, a screenshot from your own product, a customer example you are allowed to share, a named author with a real background.
- Let the tool handle site upkeep (titles, descriptions, broken links) with lighter review.
- Measure AI answer citations and leads by page, and prune pages that bring neither.