We trained a humanizer on 2,590 real long-form essays.

The corpus

The research corpus contains 2,590 long-form English essays, over 5 million words of real writing, collected with permission and indexed for retrieval. When you paste an AI draft into the humanizer, we condition the rewrite on style examples drawn from this corpus. The model doesn't guess what natural writing looks like; it tracks the cadence of writers who were actually doing the work.

We publish the corpus methodology because nobody else in this niche does. QuillBot trains on "tens of thousands of human texts" without disclosure. WriteHuman ships a closed model. We tell you exactly what shaped our humanizer because that transparency is the moat.

How we evaluate output

We use dated internal evaluation sets across varied domains to track meaning preservation, readability, and detector variance. Results can differ by passage and by third-party detector, so we do not promise a particular score. Methodology, sample outputs, and historical detector results are on the May 2026 model update and the vs WriteHuman head-to-head.

Every /detect request goes through the same trained model we run ourselves, not a thinner public version of it. The score on screen is the score the engine produced.

Who built this

HumanizeMyAI is built and operated by Fırat Mıhcı, an ESL writing researcher. Earlier published work on second- language writing patterns shaped the corpus selection criteria: every essay in the training set was chosen because it exhibits the specific structural patterns detectors look for in human prose. The product is a research project that became a product, not a product retrofitted with a research narrative.

There is no marketing team, no editorial board of fake personas, no "AI-powered customer success" chatbot. The address on the contact page reaches Fırat directly.

Why the free tier exists

Opening a free account gets you four humanizations of 250 words each, and no card, which isn't a growth-hack funnel. It's the clearest way you can verify the methodology claim before paying. Paste your own AI draft, run it through, then run our output through any review tools used in your workflow. Pay only if the result is what you needed. We're confident enough in the output to make the test free.

What we don't do

• We don't take affiliate revenue from any competitor we review. The QuillBot and Duey reviews are commissionless.
• We don't train our model on your inputs. The text you paste passes through and the response returns; nothing is retained for training.
• We don't use fake editorial personas. Fırat is the byline; future contributors will be real, LinkedIn-verifiable people, not stock-photo writers.
• We don't ship features that compete with academic integrity policies. The product is for AI-assisted writing improvement, not for misrepresenting authorship.

Is HumanizeMyAI any good?

People search this and land on nine different pages of ours, every one of them a review of somebody else. So here is the answer in the one place where the conflict of interest is obvious rather than buried: we build it, we cannot review it, and you should not take our word for any of it.

What you can check without trusting us. The corpus is 2,590 real student essays and it is published, so the training claim is inspectable rather than asserted. The free tier asks for an account but never a card, which means you can test the output on your own writing before you pay us anything. Our detector figures are self-reported measurements, labelled that way everywhere they appear, and the honest way to treat a vendor’s numbers about itself is as a starting point for your own check, not as a result.

What we will not tell you is that we are the best humanizer. We publish research on how detection works, including findings that make our own category look worse, and a company doing that cannot coherently crown itself in the same breath. Run a paragraph of your own through the tool and then through a detector nobody here controls. That answer is worth more than this page.

The research lab and the press record

The measurement habit didn't stay inside the product. The same lab publishes open computational-linguistics research (on AI writing style, detector behavior, and second-language writing) with the method, the code, and a DOI attached to each study, in the Computational Linguistics Lab. One of those studies, a 12,222-review analysis for the Alanya City Council, ran in local newspapers; independent interviews with the founder and coverage of the lab's work are collected (with links out to each original piece) on the Press & Media page. Everything is published under one verifiable identity: Google Scholar, ORCID and ResearchGate.

The roadmap

Next: open the eval set as a downloadable CSV, ship the detector matrix as a public benchmark page, and onboard one or two real freelance researchers as bylined contributors on non-technical pieces. The goal is transparency at every layer: methodology, data, code, authorship.

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