PRDs in Notion, blog drafts in Notion, customer comms templates in Notion, team wikis in Notion. A lot of that prose passes through ChatGPT first, through Notion AI second, and a writer cannot reliably remember which paragraph came from which assistant by the third revision. TextSight reads any Notion page you paste in, flags the sentences that read like ChatGPT, Claude, Gemini, or Notion AI wrote them, and returns an Authenticity Score with sentence-level highlights in under six seconds. There is no native Notion plugin yet; the realistic 2026 workflow is copy from Notion, paste into app.textsight.ai. Free to try, no card.
PRDs, customer comms, blog drafts, knowledge base entries, team wikis. The pre-publish pattern is the same across all five surfaces: paste, scan, fix the flagged blocks, ship.
Notion is the default writing surface for a generation of teams. Product managers draft PRDs in Notion, content teams plan blog posts in Notion, ops teams write playbooks in Notion, students keep coursework in Notion. The Notion AI sidebar and the inline writing assistant are part of the standard workflow now, and the unofficial ChatGPT-in-another-tab workflow has not gone anywhere. By the third revision of a typical page, even the writer who started it is fuzzy on which paragraphs came from which assistant. The page reads as a mosaic.
Notion is the most common PRD home outside Confluence. Specs that read templated land badly with engineering. Editorial teams plan and draft blog posts inside Notion before pushing to a CMS. Ops teams keep canonical support replies, sales playbooks, and onboarding emails as long-text properties on database rows. Students manage research notes, essay drafts, and dissertation chapters in Notion before submission. All four audiences face the same question before sharing: does this read human enough to ship.
By the time the Notion page has been shared with the team, the cheapest moment to fix AI-flavoured prose has already passed. Sharing is the audit deadline. The realistic 2026 pattern is to use Notion AI while drafting and to run a pre-publish scan on the final text. Paste the page into TextSight, read the sentence-level highlights, fix what reads like a model, paste the revision back, share. The whole loop adds about five minutes per page and pays for itself the first time an engineer reads substance instead of stock phrasing.
No native Notion API integration yet (the plugin is on the 2026 Business-tier roadmap). The paste flow takes under thirty seconds end to end and returns the same Authenticity Score the future plugin will surface inline.
Open your Notion page, click into the body, press Ctrl+A (Cmd+A on Mac) to select all visible blocks, then Ctrl+C (Cmd+C) to copy. Notion's block model flattens cleanly on paste: paragraphs become paragraphs, headings stay headings, bulleted and numbered lists ride along, callouts and quotes paste as plain text. Toggle blocks contribute only the visible toggle label by default; expand any toggles you want included before selecting. Embeds and database UI chrome are stripped, which is what you want for prose scoring.
Open app.textsight.ai in another tab. The detector tab is the default landing surface, so the text box is ready. No signup needed for the first scan; the free tier allows three scans a day before signup is required. The 5,000-character per-scan ceiling covers roughly three Notion blog-post sections, which is the natural unit for sectional editing anyway.
Paste into the text box with Ctrl+V (Cmd+V), click Scan. Results stream back in three to six seconds for a 1,500-word page. The Authenticity Score appears at the top, sentence-level highlights render below, and the per-sentence reasons (vocabulary tell, rhythm pattern, perplexity, structural symmetry) populate on hover. The score sits on a 0 to 100 scale with five named bands from Likely AI to Likely Human.
Switch back to Notion, rewrite the flagged blocks in place, then paste the revised page back into TextSight for a second scan. Most pages go from red to green in two or three iteration cycles. If a sentence flags on every pass, run it through the integrated AI rewriter (Light, Balanced, or Maximum) and paste the rewritten line back into Notion.
Free covers casual scanning. Pro is the right fit for regular Notion writers. Business is for multi-seat Notion teams. Full details on the pricing page.
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Notion AI runs on the same model families as ChatGPT, Claude, and Gemini. Both flag as AI in TextSight when generated, with the exact band shifting by five to fifteen points depending on prompt, model, and writer edits.
The Notion AI sidebar, the slash-command Continue, the highlight-and-rewrite pass, and the AI Summary button all sit on a mix of OpenAI and Anthropic models with Notion-specific prompt scaffolding. The classifier reads the prose, not the source app, so a Notion AI Summary triggers the same sentence-level highlights and Authenticity Score drop as a ChatGPT paste would.
Notion AI tends to produce a templated Q&A or bullet-point style that scans recognise quickly. The default outputs lean on short hedging phrases, parallel sentence structures, and a polite explanatory cadence. AI Summary compresses prose into bullets that feel rhythmically uniform, three bullets each starting with a present-tense verb, each ending with a benefit clause. Sentence-level highlights show exactly which lines triggered, so the writer rewrites only the templated passages instead of rebuilding the whole page.
A lot of Notion writers do not think of Notion AI output as AI-written the way they would a ChatGPT paste. The user-facing copy frames Notion AI as a writing assistant rather than a generator, but at the model level the output is the same kind of text. For any policy that requires disclosure of AI assistance (academic, editorial, internal compliance), a writer who used Notion AI to draft a section has not actually used a different system from a writer who pasted from ChatGPT.
Notion stores content as blocks (paragraph, heading, callout, toggle, database row), not as a continuous text stream. The flatten is mostly lossless for prose, and the parts that do not carry through are the parts you do not want scored.
Paragraph blocks become paragraphs. Heading blocks (H1, H2, H3) become heading lines. Bulleted and numbered list blocks ride along as lists. Callouts and quotes paste as plain text, dropping the icon and the coloured background but keeping the prose intact. Toggle blocks contribute the visible toggle label, plus the nested content if the toggle is expanded before the selection.
Linked database views, embeds, synced blocks, page mentions, and database row chrome (the property cells and the row header) are not part of the prose flatten. Inline page links collapse to their visible title. Page covers and icons are not text. None of this changes the score because the classifier reads prose, not page structure.
A lot of Notion content lives inside database rows. Marketing CMS rows, support reply templates, sales playbooks, content calendar entries. Open the row as a page, find the long-text property (often called Body, Notes, or Content), copy it, paste, scan. Same flow as a regular page, just one step deeper. Short text properties under fifty characters are usually too small for the detector to score reliably.
PRD writing, customer comms, blog drafts, knowledge base articles, team wiki content. All five share the same paste-then-scan loop; the differences are in cadence and tier.
Product specs read templated when AI-drafted, and engineering reads templated specs as a signal the PM did not think the problem through. PMs love ChatGPT for filling in PRD boilerplate, success metrics, and the obligatory risks section. Paste the PRD into TextSight before the spec review, rewrite the flagged sections with real product reasoning, then share. Pro at $14.99 a month on yearly fits the daily PRD cadence and keeps Slack threads about whether the PM actually thought about the feature from happening.
Customer success replies, onboarding emails, sales playbooks, and support response templates. Customers read templated comms as a generic team. Pre-scan the response template, fix the AI-shaped sentences, then save the reply. Works for both one-off replies and reusable template libraries kept as database rows.
Editorial teams plan and draft blog posts in Notion before pushing to a CMS. A typical workflow uses Notion AI for outlines and section openings, then the writer fills in the body. Paste the final draft into TextSight, confirm the Authenticity Score sits above 75, then publish. Multi-author Notion pages scan the same as solo pages.
Public knowledge bases and help centres often run on Notion Sites. Customers can smell ChatGPT prose from the first paragraph, and it makes the company look lazy. Scan each article before flipping the publish toggle. If the page is a high-traffic landing surface, the score predicts how robotic the live version will read once visitors land on it.
Company memos, all-hands updates, leadership messages, onboarding wikis. AI-drafted versions feel robotic to employees, and the AI score is a proxy for how robotic. Same workflow as the marketing surfaces: paste, scan, edit the red blocks, re-scan, share. The free tier covers a typical week of light wiki maintenance; Pro fits a comms lead running daily updates.
Honest scope: there is no Notion slash command, no gallery listing, no API bridge today. The paste flow returns the same score the future plugin will surface inline.
The planned Notion plugin will add a right-click action on any Notion page that calls TextSight via the Notion API, render flagged passages as inline Notion comments on the relevant blocks, and offer a one-click Rewrite that creates a suggested edit in a comment thread. The underlying scan and the Authenticity Score model are identical to the web app.
Three reasons Notion is harder than Word or Google Docs to integrate. First, block-based structure: a scan-this-page feature has to walk the block tree, extract prose in document order, and ignore database UI chrome. Second, database fields: a lot of Notion content lives inside database rows with rich-text properties, which needs different API logic from page content. Third, API quotas: Notion's API has rate limits that make passive background scanning impractical, so any integration has to be explicitly triggered.
Realistic timing on the native plugin: not before 2027 unless Business-tier demand spikes. The Word add-in and the Google Docs Workspace add-on are higher priority because the audiences are larger and the integration surfaces are simpler. If a native Notion plugin would meaningfully change your workflow, tell us on the contact page. Demand signal directly affects roadmap priority. Until then, the Chrome extension covers the right-click-to-scan use case on Notion in the browser.
Multi-model detection on the same paste flow. ChatGPT, Claude, Gemini, Notion AI.
All models →The Docs paste flow, multi-author scanning, and the Workspace add-on roadmap.
For Docs →Right-click to scan on any site, including Notion in the browser.
See the extension →Full tier breakdown for Free, Starter, Pro, and Business. Annual billing saves 25%.
See pricing →Free to try. No card. Your first scan in about six seconds.