A report tells you how to market your book. The engine actually does it. Upload your manuscript. We extract every framework, story, quote, and stat you ever wrote, learn your voice, detect your brand, and turn it all into a year of publish-ready content. You flip the switches.
See it workPDF, Word, or Markdown. Messy drafts welcome: stale tables of contents, missing chapter headings, and 100 MB of embedded images have all been handled.
Privacy: your file is encrypted, deleted after processing, and never used to train AI models. The atom database we build is yours, exportable anytime.
The full engine detects your brand from your manuscript's cover and interior design, then asks you to confirm it. For this text-only demo, tell us who you are and pick the look your quote card should carry.
Live preview. Your quote card fills with a verbatim line from your own chapter after the run.
The engine reads brand signal from your book itself: cover art, interior graphics, typography. Most authors have two identities. Pick which one this content publishes under; you can render visuals in both.
Below is unretouched engine output from the sample manuscript: a LinkedIn post in the author's voice and quote cards in both detected brands.
The Most Dangerous AI Output Is the Well-Written One
Everyone worries about the obviously wrong answer. That one takes care of itself - you catch it, you roll your eyes, you move on.
The answer that should worry you is the fluent one. Confident, well-structured, sounds exactly right. Under time pressure, polish and accuracy are easy to confuse, and AI produces polish on demand.
Here's the thing: AI doesn't make one kind of mistake. It makes three, and each one gets caught a different way.
1. Hallucination - confident fabrication. Made-up citations, companies that don't exist, statistics with sources you can't find. Most common with names, dates, numbers, and URLs. The check: independently find any source it cites. If you can't find it, it doesn't exist.
2. Reasoning errors - real premises, wrong conclusion. The information is correct, but a step got skipped, or the advice doesn't fit your situation. Most common in multi-step problems and anything legal, financial, or strategic. The check: ask for its assumptions, alternatives, and risks.
3. Currency errors - the world moved. Pricing, laws, regulations, someone's job title. The model answers confidently from a snapshot that expired. The check: ask 'could this have changed?' and verify against the official source.
Naming the category tells you how to check the work. You don't need to verify everything - that erases the time AI saved you. You don't get to verify nothing - that's how polished nonsense ends up in front of your board.
The people winning with AI right now aren't the ones who trust it most. They're the ones who know exactly when not to.
This framework is Section 0.3 of AI at Work. Link in comments.
Twelve monthly themes, planned from your book's own idea taxonomy. Content generates on a rolling horizon, so month nine is written in month eight, never recycled from month two.
Your manuscript is encrypted, deleted after processing, and never used to train models.
The atom database and voice profile are your assets. Export them anytime.
Every piece traces to your pages. The engine never invents a fact, stat, or quote.
Nothing publishes without sign-off. Compliance-aware for regulated industries.