Opportunities › AI-driven content quality monitoring for marketing teams › Proposed app
Plainpage
Check if your pages read human and get cited by AI search, before you publish
- Score
- 44
- confidence 69
- Evidence
- 10
- items on the page
- Rivals
- 1
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2024
- New market · AI estimate
ScreensConcept
Desktop app: Content teams review drafts, check structural patterns, compare cited pages and edit writing in detail before publishing from their desks. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Draft check
Review before publishing3 flags
About this appAI proposal
Plainpage is a pre-publish checker and citation tracker for B2B content teams. Paste a draft or a URL and it flags structural habits that make writing read as machine-made, such as restating the main point in a closing paragraph, and shows how your page compares with pages AI search tools cite for the same questions.
You choose the questions your buyers ask. Plainpage checks which pages ChatGPT, Google AI Overviews and Claude cite for them, then shows what the cited pages have in common in structure and format. You get edit suggestions, not a score to chase.
It is built for teams that use AI in drafting but do not want their brand to sound like AI slop. The checker explains each flag in plain words and leaves the decision to the editor. Priced below the monitoring-only tools in this market so a small team can start.
- Tagline
- Write like a person. Get cited like an authority.
- Voice
- PlainCandidCalm
- Why this name
- Plain speaks to clear, human writing; page is the unit teams publish and AI tools cite. Two simple words, easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Structure check
Highlights patterns linked to machine-written text, like a closing paragraph that repeats the opening point, with a line-by-line reason for each flag.
No member yet
Citation tracker
Follow the questions your buyers ask and see which pages AI search tools cite for them, and whether yours appears.
No member yet
Cited-page patterns
Compare headings, length, lists and answer placement across cited pages so you know what to change in your own draft.
No member yet
Edit suggestions
Concrete rewrites for flagged passages that keep your facts and voice instead of generic smoothing.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#14352EInk Green · primary#F7F3EAPaper · surface#1D2420Charcoal · text#E8A317Marigold · accent#B5533CClay · flags and warnings#7FA38FSage · data
Typography
Aa
Plainpage
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
PricingAI proposal
Sitefire's pricing page (verified) lists two paid plans from $249 to $499 a month, plus custom prices. These tiers sit below that to test whether smaller teams will pay. Starting point only.
Free check
Free
- 5 draft checks a month
- Structure flags with explanations
- No citation tracking
Team
$99 a month
- Unlimited draft checks
- 25 tracked questions
- 3 AI search engines
- Weekly digest
- 3 seats
Growth
$199 a month
- 100 tracked questions
- Cited-page pattern reports
- Voice notes
- 10 seats
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. Users send read-only draft reports to writers, freelancers and clients who open them outside the product. Each report carries a small Plainpage mark and a link to check a page.
- Do they become customers?
- Freelancers and agency writers who open reports are also content producers who could buy. Test whether report viewers click through and sign up for the free check.
- First customers
- Content leads at B2B SaaS companies with 5 to 30 employees who publish weekly blog content and use AI in drafting.
- Where to reach them
- Hacker News and Show HN: Launch the free checker with a short write-up of the structural patterns it flags, inviting feedback.
- LinkedIn content marketing groups: Post teardown examples of flagged drafts and cited-page patterns, and offer free audits.
- B2B marketing communities: Offer free audits in Slack groups for SaaS marketers and collect feedback.
- First milestone
- Ten paying Team subscribers from B2B SaaS content teams within 60 days of launching the free checker.
Why people already pay for this
- VerifiedHacker News post received 74 points and 28 comments
- AI inferenceMarketing teams universally recognize AI-generated content as low-quality but want to leverage AI in their content creation process.
- Public claimContent structure is a reliable differentiator: AI-generated blog posts repeat their main point at the end 77% of the time versus only 12% for human posts.
- Public claimA structural analysis model achieved 98% accuracy identifying AI-generated versus human blog posts on unseen data.
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · AI search samplingLicensed AI search or SERP data providerNeeded to read which pages AI engines cite for each question, without scraping against terms.
- This app · Text analysisStructure classifier plus LLM APIDetects structural patterns and writes edit suggestions for flagged passages.
RisksAI inference
- AIDetection claims such as 98% accuracy are public claims from one post, not verified. Flags could be wrong on human text, so the product must frame them as hints for editors.
- DataReliable, affordable data on which pages AI engines cite is hard to get, and answers vary by run, location and model. Cost may push prices up.
- CommercialA funded competitor already offers citation monitoring and a quality checker. Plainpage must win on price and draft-stage workflow.
- CommercialA public claim says good AI content can still be cited, so the slop angle may sell less than citation insight.
- PlatformAI search engines may change citation formats or restrict access, breaking tracking.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.