AI ghostwriter
The AI ghostwriter that shows its work.
Ghostwriting has two non-negotiables: the facts must be right, because someone else's name is on them, and the voice must be theirs. Most AI writing tools fail both. Ghosts was built by a strategist who spent 25 years in SEO, reputation, and political communications writing for high-profile names to pass both, every draft.
What an AI ghostwriter actually has to do
An AI writing assistant helps you type faster. An AI ghostwriter takes the job: research the subject, hold a specific person's voice, get every fact right, and hand back something the named author would sign. That is a different engineering problem. One model doing everything at once produces the familiar result — fluent, generic, and occasionally confidently wrong. Ghosts splits the work the way a newsroom does: a planning agent researches and commits to a structure, a writer trained on real samples drafts in the right voice, an independent fact-checker verifies claims against the actual text of sources, and a line editor removes the structural fingerprints of machine writing. The public white paper explains the architecture; the samples page shows the output.
Built for Google's E-E-A-T, not against it
Google's quality framework asks whether content demonstrates experience, expertise, authoritativeness, and trust. Generic AI output fails all four, which is why so much of it gets flattened. Ghosts maps onto the framework mechanically:
EExperience
Your first-hand material is the source of truth: a per-project knowledge base holds your documents, product facts, and past work, and drafts are grounded in it. The voice persona is trained on your actual published writing, so the perspective on the page is yours, not a model's.
EExpertise
Specialist writers by beat — SEO, journalism, newsletters, social, and a dedicated legal bench — each drafting inside format specs that know what a press release, a client alert, or a pillar page structurally requires.
AAuthoritativeness
Real citations to fetched sources, inline, on every researched draft. Named authors with consistent bylines, entity-clean schema on your pages, and answer-engine optimization rules applied at draft time so the work is built to be cited.
TTrust
An independent fact-check agent verifies every claim against the source's actual text; verified numbers are locked against later edits; every draft carries an issues log of what was flagged and changed. When someone asks who answers for this content, there's an audit trail.
How accuracy actually works here
Every researched draft moves through a fixed order: research from several angles, a clarifying question if the subject is ambiguous, a committed plan, the draft itself, then an independent fact-check that reads each claim against the actual text of the fetched sources. Claims the sources don't support get attributed, softened, or removed. Numbers, dates, and quotes that survive verification are locked: any later editing pass that changes one is automatically rejected and the verified text restored. Health, finance, legal, and safety topics are detected and held to stricter sourcing, and law-firm drafts add a claims audit that never invents a citation. The result is a draft where the confident-sounding sentence and the checkable sentence are the same sentence.
Human-writing quality, measured
"Sounds human" is not a hope here; it's a measurement. Every finished draft is scored against the stylometric signals detection tools read — sentence-length variance, repeated sentence openers, rule-of-three density, formulaic structure, AI-tell vocabulary — after a line editor has already rewritten those patterns out. A draft that lands below its tier's quality bar is rewritten against its specific measured weaknesses (with every verified fact protected) until it clears, and the bar rises with the tier. We publish our own samples straight from this pipeline, scores and all.
The complete feature set
Everything the platform does, end to end — from the brief to the published-performance report.
| Briefing & formats | |
|---|---|
| Project-first briefing | Audience, goals, voice, dos-and-don'ts, and a researched brand understanding you can read and correct — assembled before a word is written. |
| 35+ content formats | Blog posts, newsletters, op-eds, press releases, white papers, LinkedIn, X threads, client alerts, and more — each with its own length discipline and structural spec. |
| Destination-aware drafting | Projects declare where content publishes (blog, Substack, guest posts, social); the drafting flow narrows to formats that fit. |
| Authorship control | Publish as a named person, as the brand, or with no named author for guest posts and earned media — the writer never invents a byline. |
| Content tables on request | Ask for a comparison or data table and get one that pastes into WordPress, Ghost, or Word with its borders intact. |
| Purpose & point of view | Sell, explain, or persuade; first or third person — declared up front and enforced through the draft. |
| Research & accuracy | |
| Multi-angle research | Several distinct search queries per brief targeting recent data, expert analysis, and the strongest counterargument, with per-domain caps so no outlet dominates. |
| Real-time engine option | A live research engine pulls current facts and quotable posts for time-sensitive pieces. |
| Clarify before drafting | An ambiguous subject (two companies sharing a name) triggers a confirm-first question with sources — never a guess. |
| Independent fact-check | A separate agent verifies every claim, statistic, date, and quote against the actual text of the fetched sources, not model memory. |
| Unit & direction checks | A per-year figure written as per-day, or a decline cited as a rise, gets caught and corrected to match the source exactly. |
| Recency guard | A current-year piece leaning on a years-old statistic gets flagged; snippet-only sources can never carry a precise number alone. |
| Verified facts locked | Numbers, dates, and quotes that survive fact-checking are protected through every later rewrite — an edit that changes one is automatically rejected. |
| YMYL escalation | Health, finance, legal, and safety topics are detected and held to stricter sourcing standards. |
| Legal claims scrub | Law-firm drafts get an additional audit that attributes, softens, or removes unsupported assertions — and never fabricates a citation. |
| Voice | |
| A built-in writers' room | Specialist writers by beat — SEO, journalism, newsletters, social, business — including an eight-writer legal bench. |
| Custom trained writers | Train a writer on your (or your client's) published work. Training absorbs voice — rhythm, diction, structure — never topic knowledge, so facts stay independently verified. |
| Exemplar anchoring | A curated writing sample anchors the voice through the whole piece, preventing drift back to the model median. |
| Continuous learning | Every approved draft distills your edits into lessons; hygiene filters keep one odd edit from corrupting the voice; the full ledger is visible and revertable. |
| Standing instructions | Your own always-apply rules per writer, layered on top of what it learns. |
| Human-writing quality | |
| Anti-tell line editing | Every draft is edited against the fingerprints of machine writing: AI vocabulary, formulaic conclusions, uniform rhythm, repeated sentence openers, rule-of-three overuse. |
| Measured, not hoped | Each finished draft is scored against the stylometric signals detectors read — sentence-length variance, opener repetition, structural patterns — with a quality bar that rises by tier. |
| Signal-targeted rewrites | A draft below its tier's bar gets rewritten against its specific measured weaknesses, with verified facts protected, until it clears or the original is kept. |
| Truncation guard | Incomplete model output can't silently pass as a finished draft. |
| Optimization & distribution | |
| SEO, AEO & GEO rules | A versioned optimization matrix applies search, answer-engine, and generative-engine rules at draft time and scores the result from the same rulebook. |
| Affiliate link placement | Per-project approved affiliate libraries; review and roundup formats place links with pros-and-cons honesty and disclosure discipline. |
| Social repurposing | Turn a finished article into platform-sized captions — you choose the platform, the length, and whether the link rides in the post or the first comment. |
| Hub-and-spoke content | Grow a pillar piece into linked supporting articles. |
| Published previews & exports | See the piece as each channel renders it; export HTML, DOCX, or JSON. |
| Workflow & accountability | |
| Editor's read | An independent review of every draft with scored findings and one-click fixes, each applied with a receipt of what changed. |
| Issues log per draft | What the fact-checker flagged, what the reviewer changed, and why — a record, not a summary. |
| Client review links | Private, expiring preview links where clients approve or request changes; every decision is logged and feeds the writer's learning. |
| Clients, projects & folders | Agency-shaped organization with per-client voices, knowledge bases, and reporting. |
| Knowledge base grounding | Upload brand guidelines, product facts, and past work; drafts pull the most relevant passages and cite them. |
| Transparent costs | Every piece shows its credit cost before you commit and its true generation cost after. |
| Performance tracking | Optional per-project reports: search rankings, AI Overview citations, and what ChatGPT, Perplexity, and Claude say about the brand. |
| Trust & data | |
| Never used for AI training | Your content is excluded from model training — our policy and our providers' contractual terms. |
| Tenant isolation | Row-level security in the database itself; no cross-workspace access, no human review in normal operation. |
| Retention controls | Law-firm workspaces set per-status retention from 24 hours to a year, enforced by an automated hard-delete shredder with a provable purge log. |
One writer's room, every kind of work
The same pipeline drafts a researched SEO article, a newsletter issue, an executive's op-ed, and a law firm's client alert — each inside its own format discipline. Find your work:
Coming from another tool? See how Ghosts compares to ChatGPT, Jasper, Copy.ai, and the rest on the compare page.
Common questions
What is an AI ghostwriter?
An AI ghostwriter produces publishable writing under someone else's name, which sets a higher bar than an AI writing assistant: the facts have to be right because a real person answers for them, and the voice has to be theirs, not a model's house style. Ghosts meets that bar with a multi-agent pipeline — a planner researches and structures the piece, a writer trained on real writing samples drafts it, an independent agent fact-checks every claim against fetched sources, and editors remove the patterns that make AI writing recognizable.
How is Ghosts different from ChatGPT or single-model writing tools?
One model asked to plan, draft, fact-check, and hold a voice at the same time trades those jobs off against each other. Ghosts assigns each job to a specialist agent and links them with checks: verification is done by an agent that did not write the draft, verified numbers are locked against later edits, and the writer persona is trained on voice, not topic knowledge. See how Ghosts compares to ChatGPT, Jasper, and other tools on the compare page.
Are you Ghost, the publishing platform?
No. Ghost (ghost.org) is an open-source publishing platform; Ghosts (ghosts.app) is an AI writing team. Plenty of our users publish on Ghost — there's a dedicated page about using Ghosts to write for Ghost blogs.
Does AI-ghostwritten content hurt SEO or E-E-A-T?
Generic AI content does, because it demonstrates no experience, cites nothing, and reads like everything else. Ghosts is built the other way: drafts cite real fetched sources, run under named authors with schema-ready bylines, draw on your own first-hand material through a per-project knowledge base, and carry an audit trail of what was verified. That is the E-E-A-T checklist — experience, expertise, authoritativeness, and trust — expressed as product mechanics.
Will the writing sound like me?
The writer persona is trained on your published writing — rhythm, diction, how you open and how you land — and it keeps learning from every draft you approve, with a visible ledger you can edit or revert. The tenth piece sounds more like you than the first.
Can it handle YMYL topics like health, finance, and legal content?
Yes, with escalated care. Ghosts detects your-money-or-your-life subject matter and holds those drafts to stricter sourcing; law-firm workspaces additionally run a claims scrub on every draft that attributes, softens, or removes any assertion the sources don't support — and never invents a citation, case name, or statute.
Does it support affiliate content?
Yes. Each project keeps an approved affiliate-link library, and review and roundup formats place those links with honest pros-and-cons structure and disclosure discipline. Drafts can only link to your approved list or to real fetched sources — invented URLs are removed before you ever see the draft.
What does an AI ghostwriter cost?
Ghosts plans start at $29/mo (Individual), with Studio at $59, Agency at $99 per seat, and Law Firms at $150 per seat. Every plan includes monthly drafting credits, you see each piece's cost before it writes, and the $29 plan starts with a 7-day free trial — write your first article on us.
Publishing on Ghost, the CMS? There's a dedicated page for Ghost publishers. More background on who built this and why.
Brief a ghostwriter. Keep the byline.
Plans from $29/mo. Write your first article on us — the $29 plan starts with a 7-day free trial.
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