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InvisiWrite

We build tools for people who write with AI and want it to still sound like them.

InvisiWrite started with an argument about a number. A colleague's report came back from a detector at 82% AI — a report she had written herself, at a kitchen table, over two evenings. The number was wrong, and there was no way to see why it was wrong.

2024Founded
11People
ManchesterWhere we are
0Documents used for training

What we are trying to fix

AI detection arrived faster than anyone's ability to interpret it. Universities bought it, employers bought it, and both started making decisions on a single percentage with no working shown.

A score without reasoning is not evidence

Every figure we produce comes with the sentences that drove it and the reason each was flagged. If you cannot argue with a number, it should not be used to judge you.

False positives land on the same people

Formal, careful, non-idiomatic English gets flagged hardest — which describes almost everyone writing in a second language. We say so in the product rather than hiding it in a support article.

Rewriting is an editing tool

Making a stiff paragraph readable is ordinary editorial work. We build for that, and not for concealing where something came from.

Your text is yours

Never used for training, never sold, deleted on the schedule you pick. Business accounts get stricter retention, not looser.

Responsible use

We would rather lose a sale than end up in someone's misconduct hearing, so here is the position in plain terms.

What this is for. Checking how your own writing reads, editing drafts you produced, and understanding a score before someone else acts on it.

What we will not build. Watermark removal, keystroke replay into document editors, or anything else designed to fabricate a record of how work was produced.

Where institutional rules apply. Universities and employers set their own policies on AI assistance, and they differ. Those rules take precedence over anything we say. Read them.

What we will not claim. That any rewrite is guaranteed to pass any detector. Nobody can promise that honestly, and the ones who do are selling certainty they cannot deliver.

The team

Small, and intending to stay that way for a while.

Name one
Founder and product
Name two
Engineering lead
Name three
Machine learning
Name four
Support and operations

Replace these with real names and photographs before launch. Placeholder team sections are the fastest way to lose a business customer's trust.

Try it on something you wrote

Not on a sample. Paste in your own work and see whether the reasoning holds up.

Open the detector