Scan every PRD, RFC, design doc, release note, customer email, and board update before share. Sentence-level highlights flag the templated passages so engineers see real product reasoning, customers see a team that actually shipped, and the exec readers see a PM in command of the surface area. Built for product managers at startups, scale-ups, and enterprise. Free to try. No card.
Product managers at SaaS startups, scale-ups, and enterprise. Solo PMs owning a product surface, group PMs running squads, and product leads reviewing the rest of the team's specs. The common thread is mixed-audience writing under deadline pressure with AI drafting tools open in another tab.
Product management is a writing job dressed as a strategy job. The PRD lives in Notion or Confluence, the spec lives in a design doc, the ticket lives in Linear or Jira, the release note lives on the changelog page, the weekly update lives in the exec email, the board update lives in a deck, and the customer email lives in the support tool. Every one of those surfaces is read by someone with the authority to question whether the PM thought the problem through. A templated AI tone on any of them reads as a credibility gap.
One PM owning the full surface. The PRD goes straight to engineering and the release note goes straight to customers, with no editor in between. The scan is the editor. Five minutes per document is the cost; the avoided rewrite cycle with engineering is the saving.
Two to six PMs in a product org, each owning a squad. Specs cross-reference each other and a templated tone in one PRD propagates to the rest. The Business tier with shared scan history makes the standard visible across the team instead of negotiated per PRD.
Director or VP of product reviewing the org's PRDs before they go to engineering leadership. The score plus the sentence highlights compresses the review from forty minutes per spec to ten. The recovered review time pays for the entire seat budget in the first sprint.
Each genre carries its own register and its own risk profile when stock AI phrasing slips through. The scan loop is the same across all eight; the rewrite priority differs by audience and stakes.
The most expensive document the PM writes because engineering spends hours inside it. An AI-flavoured PRD reads as a PM who did not think the problem through. Target an Authenticity Score above 80 on every PRD and rewrite the flagged sentences with real customer language and real product reasoning before engineering opens the doc.
Technical specs cross-checked by senior engineers who notice the flat phrasing immediately. The risk is not the score itself; it is the tonal signal that the spec was drafted in haste. Sentence-level highlights point to the specific lines that need a real engineering rationale instead of stock framing.
Tickets are short, which makes the per-ticket score noisy. Batch a sprint's worth of tickets together for one scan so the model has enough signal. The point is concrete acceptance criteria and clear scope, not a per-ticket number.
The trust surface most PMs underrate. Customers read the changelog before they decide whether the team is moving the product forward. Generic templated release notes signal a generic templated team. Run every release note past the scan and rewrite the flagged passages before publish.
Customer-facing emails, churn outreach, beta invitations, and feature announcement copy. Reply rates drop on AI-flavoured outreach inside the first send. Scan every customer-facing piece, target an Authenticity Score above 80, and rewrite the lines flagged at the sentence level.
The weekly exec update and the monthly board narrative. The board reads templated language as a credibility gap because the prose is the only signal they have outside of the metrics. Scan every exec-facing document and rewrite the stock phrasing before share.
Pro at $19.99 a month standard, $14.99 a month on yearly, fits the solo PM running the Chrome extension inside Notion or Confluence. Business at $39.99 a month standard, $29.99 a month on yearly, is the team tier for three to eight PMs sharing scan history across PRDs. Full details on the pricing page.
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Senior engineers read PRDs for a living and spot stock phrasing in the first paragraph. Executive readers spot it in the weekly status because the prose is the only signal they have outside the metrics. Pre-scanning is the workflow that closes the credibility gap.
Engineers read for problem framing, scope, edge cases, and the rationale behind the chosen approach. They notice when the problem statement is generic, when the user story reads templated, and when the rationale section is filler. The build conversation starts on substance only when the spec earns it. Sentence highlights point at the specific filler paragraphs so the PM rewrites those instead of the whole doc.
Exec readers parse the weekly update for risk, dependencies, and whether the PM has the surface area under control. Templated phrasing reads as a PM coasting, even when the underlying work is solid. A scan plus a five-minute rewrite changes the tonal signal without changing the content.
The tax is invisible in any single document and obvious across a quarter. PMs whose specs consistently read as carefully reasoned get more product authority across cross-functional partners. PMs whose specs read templated get more rounds of clarifying questions, longer build estimates, and quieter board reactions.
The Authenticity Score is the diagnostic, not the goal. Rewriting purely to lift the number flattens the spec. Use the sentence highlights to identify the specific lines that drift into stock framing and rewrite those with concrete product reasoning. The score lifts as a side effect and the spec reads tighter.
PRDs and RFCs need precision. They also need a voice that reads as a real PM working a real problem. The two are not in tension when the scan is part of the loop; precision survives the rewrite and the templated framing disappears.
AI drafts default to generic problem statements: optimise, streamline, enhance. The rewrite pass replaces those with the actual user behaviour, the actual frequency, and the actual cost. Sentence highlights flag the generic verbs so the PM names the specific failure mode instead.
Templated user stories read as composite users that nobody actually met. The rewrite pass swaps in the specific persona, the specific workflow, and the specific friction. Engineering reads the story and immediately understands the build target.
The rationale section is where AI drafts collapse. Stock phrasing about alignment, scalability, and best practice reads as filler. The rewrite pass replaces the filler with the actual trade-offs considered, the actual options rejected, and the actual reason the chosen approach wins. The doc earns the build conversation.
AI drafts hedge: should, could, may. Engineering parses hedging as ambiguity and asks clarifying questions in the doc instead of building. The rewrite pass strips the hedge words and commits to specific criteria. The ticket starts on substance.
Customers read the changelog before they decide whether the team is moving the product forward. They read the feature email before they decide to try the new thing. Generic templated copy signals a generic templated team, and that signal compounds across releases.
A templated release note reads as a team going through the motions. A scanned and rewritten release note reads as a team that cared which line of copy went in front of the customer. The difference is five minutes per release; the cumulative effect across a quarter is measurable in feature adoption and retention.
Feature announcements, beta invitations, churn outreach, and onboarding emails all live or die on the first three lines. AI-flavoured openers signal mass send and reply rates drop. Scan every customer email, target an Authenticity Score above 80, and rewrite the flagged lines.
Help docs and in-product tooltips are the long-tail trust surface. Customers hit them when they are already mildly frustrated and templated copy makes the frustration worse. The scan catches the stock phrasing in the help-doc draft before publish.
The first-run experience sets the tone for the whole product relationship. Templated onboarding copy reads as a product that does not respect the user's time. Scan the full onboarding flow as a batch and rewrite the templated lines before the next release.
PRDs and specs do not only get read by engineering. Sales reads them to understand what the demo will say next quarter. Customer success reads them to write the help docs. Marketing reads them to write the launch post. When the source spec is templated, every downstream artefact inherits the tone.
Sales engineers read the PRD to understand what the demo will show. A flat spec produces a flat demo because the language travels directly from the doc to the customer call. Scanning the spec lifts the floor for every downstream sales artefact in the launch.
Product marketing reads the spec to write the launch blog, the email, and the landing page. AI-flavoured framing in the spec produces AI-flavoured framing in the launch, and the launch reads templated across every surface. Scanning the source spec raises the bar for the whole campaign.
CS reads the spec to write the help articles and the in-product tooltips. Generic spec language becomes generic help language, which is exactly where customers feel templated copy most. Scanning the source spec catches the stock phrasing before it propagates into the support tool.
The compound benefit is the whole point. Five minutes of PM scan time saves five rewrites across sales, marketing, CS, and the help centre. The launch reads tighter end to end because the source document set the standard.
Product managers live in Notion, Confluence, Linear, and Jira. The Chrome extension scans whatever doc is open in any of them. The REST API on Business wires scans into ticket creation and status webhooks for product ops engineering. Native plugins are on the 2026 roadmap; the API plus extension is the path today.
The extension scans whatever document is open in the editor. Highlight the spec, hit the extension, and the score plus sentence highlights come back in about six seconds. No copy-paste into a separate tool, no context switch out of the document.
The Business tier exposes a REST API. A product ops engineer can wire scans into Linear status changes or Jira ticket creation via webhook, so any new PRD or large ticket runs through the scan automatically. The audit log shows which scan ran on which ticket with timestamps.
The web app at app.textsight.ai is where the deeper review happens: paste in the full PRD, read the sentence highlights, run the AI rewriter on the flagged passages, and export a PDF for the spec review meeting. White-label PDFs on Business make the export brandable for enterprise product orgs.
Native Notion, Confluence, and Linear plugins are on the 2026 roadmap but not live. The Chrome extension covers the per-doc workflow in all of them today, and the REST API covers the automation case. Most product teams adopt the extension first and add the API integration after they have a workflow that benefits.
More for product managers.
The in-house marketing counterpart for the launch post that downstreams from your PRD.
For marketing →Per-writer workflow for the help-doc and changelog writers downstream from your spec.
For writers →REST API reference for Linear, Jira, and Notion webhook integrations on Business.
Read the docs →Free, Starter, Pro, Business. Yearly billing saves 25%. Pro fits the solo PM.
See pricing →Free to try. No card. Pro at $14.99 a month on yearly for the solo PM running scans on every PRD, spec, and release note.