AI humanizer for agencies
that scales without losing the voice
Your clients are paying for writing that sounds like their brand, not like everyone else's model. Run the whole queue through one editing pass and deliver work that reads human.
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See how our bulk processing and API can save your agency 100+ hours per week.
Built for high-volume content teams
The editing layer between your drafting workflow and the client's inbox.
Bulk Processing
Run hundreds of articles through the editing pass at once. Upload a CSV and get drafts back that read like people wrote them.
Team Seats
Manage your entire writing team under one billing account. Share credits and collaborate on projects.
Client-Ready Reports
Share a readability and voice report with each deliverable, so clients can see the editing work rather than take it on faith.
NDA & Privacy
We sign NDAs for enterprise clients. Your client data is processed securely and never trained on.
Consistent Voice at Volume
When a dozen writers draft with a dozen different models, everything drifts off-brand. One pass brings the whole queue back to a single voice.
Dedicated Support
Get a dedicated account manager to help with integration, prompt engineering, and custom workflows.
Why agencies need a bulk AI humanizer
Most content teams now draft with AI somewhere in the process, whether or not the client has been told. That is a production decision, and defensible. What is not defensible is shipping the artefacts of it.
Two things leave a trace. The invisible layer arrives with every paste: zero-width spaces inside words, non-breaking spaces, byte-order marks, em dashes in place of commas. It survives Google Docs, it survives the CMS, and it is trivially detectable by anyone who thinks to look. A client who runs your deliverable through a Unicode inspector finds it in seconds.
The stylistic layer is worse, because the client's own audience sees it. Stacked abstractions, connective phrasing at the head of every paragraph, three-item lists throughout, hedged claims with no specific detail. The client may not diagnose it, but they will say the copy "doesn't sound like us", and that conversation costs more than the article was worth.
A bulk AI humanizer is the editing pass between production and delivery. Its job is to make the deliverable indefensible to criticise, not to hide how it was made.
Running it across a queue
Repeat work is free and instant
Results are cached by a hash of the text. When a writer, an editor and an account manager each paste the same draft, the second and third get the identical result immediately and are not charged again. On a team touching the same deliverable several times, that is most of your volume.
A cleaned re-paste still matches
If someone strips the invisible characters themselves and pastes the result back, it is recognised as the same document rather than re-scanned from scratch. Ordinary editorial handling does not cost you a second charge.
Editors check, rather than trust
Every flag names the phrase and gives a plain-English reason. An editor can accept or overrule each one on the evidence. That matters at volume, because the failure mode of any automated pass is a team that stops reading the output.
Holding a client's voice at volume
Brand voice at scale is a governance problem before it is a tooling one. Five writers produce five registers, and the drift is invisible until a client reads three pieces in a row.
Two rules make the tooling part reliable. Set the mode per client, not per writer, so the register is a property of the account rather than whoever picked up the ticket. And run one piece through before committing a queue, checking the result against the client's style guide. If it fights the guide, you want to know on one article and not on forty.
Everything client-specific survives untouched: product names, figures, dates, quotations, disclaimers and links. The rewrite removes the model register rather than substituting ours. What it cannot do is invent a voice the client has never defined, and if a client has no style guide that is the real deliverable to sell them first.
What to tell a client about AI detection
Never promise a detector score
Detector outputs are probabilities, they disagree with each other, and they change when the vendor updates a model. A client who pastes your deliverable into a different tool than the one you quoted will get a different number, and you will own the difference. Promise what is verifiable instead.
Promise the things that are checkable
That no invisible characters remain, which anyone can confirm. That catalogued AI phrasing has been rewritten, with a list of what was found and why. That figures, names and quotations are unchanged from the brief. All three survive contact with a sceptical client.
Decide your disclosure position early
Some clients require disclosure of AI in the production process, some contracts forbid third-party processing of their material entirely, and a few sectors have regulatory views. Settle this at the contract stage. A tool cannot resolve a disclosure question, and discovering the answer mid-engagement is expensive.
Putting it in front of a team
The tooling is the easy part. What decides whether this works across an agency is where you put it in the process and what you ask people to do with the output.
Scan before the edit, not after
Teams instinctively run it last, as a final polish. That wastes it. Scanning first tells the editor whether they are looking at a light pass or a rewrite, and that decision governs how they spend the next hour. Running it after the edit only confirms work already done.
One person owns the mode per client
If each writer picks the register themselves you have reintroduced the drift you were trying to remove. Set it on the account, record it wherever the client's brief lives, and treat a change to it as a change to the brief.
Overruling a flag is a real answer
Some flagged phrasing is correct for the client. A legal page needs hedging. A technical brief needs the formal vocabulary. An editor who reads the reason and decides to keep the sentence has used the tool properly. A team that accepts every suggestion without reading has stopped editing, and their work will get flatter over a quarter in a way nobody notices until a client says so.
Keep one sample of raw output per client
Before-and-after on a real deliverable is the most persuasive thing you can put in a quarterly review, and it is impossible to reconstruct later. Save one from each account while you have it.
Where the margin actually comes from
The saving is not in drafting faster. Most teams already draft fast. It is in the editing pass, which is where senior time goes and where the cost sits.
An editor reading a raw model draft is hunting for problems with no map: reading everything at the same level of attention, catching the obvious phrasing, missing the invisible characters entirely because they are invisible. Handing that same editor a marked-up draft changes the task from search to judgement, which is both faster and the thing you are paying them for.
The second saving is rework. A deliverable returned by a client for tone is expensive in a way that rarely shows up in a spreadsheet: it consumes an account manager, a writer and an editor a second time, and it spends credibility you cannot rebuy. Catching the register problem before delivery is worth more than the minutes saved on the first pass.
Neither saving is dramatic on one article. Both compound across a quarter, which is the only timescale on which content margin is actually visible.
The honest limit: this does not replace an editor, and an agency that uses it to remove the editing step will ship worse work than one that uses it to focus the step. The tool finds candidates. Deciding what a sentence should say is still a person's job.
Related reading: humanizer versus paraphraser, how AI detectors actually work and why AI writing sounds robotic.
Questions agencies ask
How we make AI-assisted writing sound like a person wrote it.