solutions · saas and developer tools
Cover your category's questions in the form assistants quote
Your docs answer how. Nothing on the site answers which, why or instead-of. IT Master writes the category's explainers and comparisons from your own docs and changelog, has a second vendor check them, then ships them to your Next.js site.
reviewed 2026-09-02 · by the IT Master editorial team · how we check facts
- Our docs explain how the product works, and nothing on the site answers the question a developer types before they have heard of us.
- An engineer writes a post when they can spare an afternoon, so we ship twice a quarter while the category leader ships twice a week.
- Ask ChatGPT or Perplexity which tools do this job and the answer names three competitors, none of them us.
- The changelog is full of material that would make good articles, and none of it ever leaves the changelog.
- Every content tool we trialled assumed WordPress, and our marketing site is Next.js.
- The site answers the questions developers type before they know your name.
- Your own docs and release notes become articles that link back to them.
- Articles reach your Next.js site through the SDK, the REST feed or a webhook.
- Assistants have a page of yours to cite when someone asks about your category.
- You pay for the articles that publish, not for the drafts that fail.
Docs answer how. Category demand starts one question earlier
Documentation serves people who have already installed the thing. The demand that grows a developer tool sits one question earlier: whoever searches "webhook retries vs a message queue" or "how to rate-limit a public API" has a problem, not a shortlist, and will never open your /docs.
Topical authority is coverage of that earlier layer — the concept explainers, the approach comparisons, the integration guides, the failure modes. A site that covers a category properly is a better candidate for any single query inside it, and assistants retrieve from the same pool of pages.
IT Master reads demand from DataForSEO and your own Search Console, groups it under the problems your product solves, and writes into the gaps competitors left. Three page shapes carry most of the weight:
- Explainers that define a concept in your category and show where the product fits, without turning into a pitch.
- Approach comparisons — polling against webhooks, self-hosted against managed — argued from what your engineers already know.
- Integration guides for the frameworks and platforms your users pair you with.
Each article joins a topic cluster, links down to the relevant docs page, and ships on a steady cadence rather than in a launch-week dump.
Your changelog is a content backlog nobody works
Every release note describes a change somebody needed, and behind that need there is usually a search. Most notes stay three lines long and never become the article that would have caught it.
IT Master grounds each draft in your own first-party material, wherever that material exists:
- product and release information — what changed, what it replaced, from which version;
- your documentation — real parameter names, limits and error codes;
- support answers, and the questions behind them, from your helpdesk or your community.
From that base it writes pages a competitor cannot copy: a migration guide naming the removed endpoint and its replacement, an explainer quoting your real rate limits instead of a round number, a troubleshooting page assembled from the ten support questions one feature generates. Specifics of that kind are what the novelty score rewards, because they appear on none of the competitor pages fetched during research.
Where your material is thin, the article says less rather than filling the gap from a model's memory. First-party data covers how the grounding works and what happens to a claim nothing can source.
Being the page the assistant cites
When a developer asks an assistant which tools handle a job, the answer is assembled from pages the assistant retrieved seconds earlier. A page earns a place in that answer when it states the answer in a self-contained passage, under a heading phrased the way the question was asked, on a site the assistant already associates with the category. That is answer engine optimisation, and it is a structural property of the page rather than a trick.
Every article is built for that retrieval step: the direct answer in the opening lines of each section, a FAQ block carrying FAQ schema, Article JSON-LD, and headings phrased the way people ask the question. IndexNow pings the search engines as soon as a page is live.
Then it measures. The nightly loop puts the questions each article was written to answer to GPT and Perplexity, and records whether your page is cited, so the citation rate is a number you watch rather than a hope. Nobody can promise a citation. We publish in the shape assistants quote and report honestly on whether they do.
Measure your current site first with the free AI visibility checker, AEO checker and AI crawler check.
Answer engine optimisation is the practice of structuring a page so an assistant such as ChatGPT or Perplexity can lift a passage from it as the answer to a question, and cite the page as its source.
The Next.js path: SDK, REST feed or signed webhook
Most SaaS marketing sites run Next.js or something custom, and most content tools assume WordPress. IT Master ships through whichever door your site already has.
- @itmaster/sdk for Next.js: install the package, add a route, and published articles render inside your own components, typography and navigation. Articles arrive as data, so nothing is pasted into a CMS by hand.
- REST pull feed: any stack fetches published articles with a per-site key, at build time or per request.
- Signed push webhook: the engine posts each article to an endpoint you control, signed so you can verify it before you store it. This is the route for a headless CMS, a static generator or your own pipeline.
- WordPress plugin if the blog itself runs WordPress: install it, paste one key, and posts arrive with Yoast or Rank Math fields filled.
Shopify and Webflow we set up on request; Wix and Squarespace get a copy-paste queue. Whichever door it comes through, an article carries its JSON-LD, internal links, hero image and FAQ block, so the site receives a finished page rather than a body of text to dress.
The model that writes is never the model that checks
The failure that matters to a technical audience is a confident paragraph that is wrong: a parameter that does not exist, a limit lifted from a competitor's docs and attributed to you. The answer is structural rather than a promise. Writers are Claude models. The judges are Gemini and GPT, a different vendor, so no model marks its own work.
Four checks run in parallel on every draft: a fact-check against the retrieved sources, a novelty score against every competitor page fetched during research, an E-E-A-T critique, and AI-tell detection. Fail one and the draft goes back to the writer with the specific objections, for up to three revision rounds. A draft that still fails is rejected, and is not charged at the full rate.
Across 186 Standard-tier runs, 51% of drafts passed every check first time; the rest were revised or rejected. Pro-tier judges are stricter: 25% first-pass, across a small sample of 16 runs.
The four tiers — Lite, Standard, Pro and Premium — differ in the writer model and in how strict the judges are. The whole flow is on how it works; you pay per published article, see pricing.
A live example: 1xai.ir
1xai.ir is an AI developer tools site running on the engine described here: demand discovery, research, a Claude writer, cross-vendor checks, publish, then nightly measurement. Two things about it matter to a SaaS team.
The first is language. Its articles are published in Persian, written by a Claude model and checked by judges from another vendor — the same pipeline that serves the English-language sites. If your users are not all English-speaking, ask us about your language before you start rather than after your first batch.
The second is the category. Developer tools is crowded and heavily documented, and official docs and long-established blogs usually hold the top of the results. That is exactly where a rewrite of the existing top ten has nothing to add, and where the novelty check says so — which sends the draft back to your own limits, releases and support threads for something the other ten do not have.
We do not publish 1xai.ir's traffic, rankings or citations. The example shows the mechanism, not a result. The developer tools page goes deeper into the category, and the free site check shows which of your category's questions your site currently leaves unanswered.
questions people ask
Our marketing site is Next.js, not WordPress. How do articles get in?
Through the @itmaster/sdk package, the REST pull feed or a signed push webhook, whichever suits your build. The SDK is the shortest path for Next.js: install it, add a route, and published articles render inside your own components and design system. If you would rather own the fetch, the REST feed returns published articles against a per-site key, at build time or per request. The webhook posts each article to an endpoint you control, signed so you can verify it before storing it, which suits a headless CMS or a custom pipeline. WordPress blogs use the plugin instead.
Will it invent features or limits our product does not have?
That is the failure the validation gauntlet exists to catch. Before writing, the engine retrieves from your own first-party material: documentation, release notes, support answers. After writing, a fact-check by a model from a different vendor than the writer compares each claim against those sources and against the competitor pages fetched during research. A draft carrying an unsupported claim goes back for revision with the specific objection, up to three times, and is rejected if it still fails. Where your material does not cover a point, the article says less rather than filling the gap from a model's memory.
Can you guarantee ChatGPT or Perplexity will cite us?
No, and treat anyone who says yes with suspicion. What the engine does is publish pages in the shape assistants quote: a direct answer at the top of each section, headings matching the question as people ask it, a FAQ block with schema, and Article JSON-LD, across a site that covers the category rather than one post. Then it measures. The nightly loop puts the questions each article answers to GPT and Perplexity and records whether your page is cited, so you watch the citation rate move, or fail to, against real queries.
What can it actually do with our changelog?
Treat it as grounding and as a topic source. A release note describes a change somebody needed, and behind that need there is usually a search. The engine grounds drafts in the release notes, documentation and support answers you connect, so a migration guide can name the removed endpoint and its replacement, and an explainer can quote your real limits rather than a round number. Those specifics appear on no competitor page, which is what the novelty check rewards. When the product moves on and an article starts decaying in Search Console, the nightly loop queues it for a refresh.
How is it priced?
Per published article, from a prepaid balance, with no subscription. You top up, articles publish, and the balance is drawn down for each one that ships. A draft that fails the checks after its revision rounds is rejected and is not charged at the full rate, so you are not paying for the engine's misses at the price of its hits. Four quality tiers are available, Lite, Standard, Pro and Premium, differing in the writer model used and in how strict the judges are. The figures per tier are on the pricing page.
Does it write in languages other than English?
Yes. 1xai.ir, a live tenant in the AI developer tools category, publishes in Persian, written by a Claude model and checked by judges from a different vendor, on the same pipeline that serves the English-language sites. Nothing is translated from an English draft after the fact. Which languages we can take on depends on the search data available for them, so ask us before you start rather than after your first batch.
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