DeAIze vs Scribbr: Where a Flag Points You Next

DeAIze vs Scribbr is not a contest over which tool is more accurate. It is a question about output shape. Scribbr’s free AI detector returns a document-level figure. DeAIze returns a document score plus a per-sentence AI probability, so you can see which lines carry the signal and edit those first.

What Scribbr’s detector actually gives you

Scribbr is best known for proofreading and citation services, and its free AI detector sits alongside that. You paste text, it returns a percentage-style reading of how much of the document looks machine-generated, and it flags at the document level.

That is a legitimate design. If you are a student checking a paragraph before submission, or an editor deciding whether a freelancer’s draft needs a second look, a single number is often enough to start a conversation.

The limitation is what happens next. A document-level score tells you the draft is a problem. It does not tell you which paragraph caused it. If you have a 3,000-word chapter and the tool says the text reads machine-made, you are left guessing: is it the literature review, the methods paragraph, or the transitions between sections?

Scribbr’s score is also not a probability that a machine wrote your text. It is an instrument reading. Different detectors use different definitions, thresholds and training data. Two tools can look at the same paragraph and return different numbers without either being broken. That is normal, and it is why comparing raw percentages across tools is a weak exercise.

What DeAIze reports instead

DeAIze (https://deaize.com) is a web app with two halves: an AI detector and an AI humanizer. The detector is sentence level. You get a whole-document score and each sentence is highlighted by AI probability, so you can see which lines carry the signal.

That changes the workflow. Instead of asking “is this draft flagged?” you ask “which four sentences are doing the damage?” For a writer, that is the only question that leads to an edit.

The humanizer half suggests revised wording for your own draft. It is an editing aid, not a laundering service. You compare the suggestion with the original and verify claims, quantities, citations, negations and qualifiers. Semantic similarity is a review aid, not proof that every meaning was preserved. If a suggested rewrite softens a “must not” into a “should not,” you are the one who catches it.

If you want to see how a sentence-level reading changes your editing order, the rewrite trial is 200 words after email confirmation, no credit card, and the pricing page shows what each plan covers. Detection has separate configurable limits shown in the current interface.

How the two outputs differ in practice

Here is the comparison that matters. Not which company is better, but what each output lets you do next.

Output	Scribbr’s free AI detector	DeAIze
Document-level score	Yes	Yes
Sentence-level highlighting	Not the core output	Yes, each sentence scored
What it tells you first	Whether the draft reads machine-made	Which lines carry the signal
Editing next step	Re-read and guess	Rewrite the flagged sentences
Rewriting suggestions	Separate service	Built into the same app
Best for	A quick check before you decide	Knowing where to start editing
Languages	English-first	English and 9 other languages
Upload formats	Paste-oriented	.txt, .docx and .pdf

Neither column is a verdict. If your job is triage across many submissions, a document score may be all you need. If your job is fixing your own draft, a document score leaves the hard part undone.

Why a single number cannot tell you what to fix

Consider a real editing scenario. You have a 1,000-word report. The document reads high. You suspect the introduction because you wrote it quickly with a model’s help, but the body was drafted by hand over two days.

With a document score, you have no way to confirm that. You either rewrite everything, which wastes the two days of hand work, or you rewrite nothing and hope.

With sentence-level output, the pattern is visible. The flagged sentences cluster in the introduction and in the transitional sentences between sections. The body paragraphs, where you wrote about your own data, come back low. You rewrite the introduction and the transitions. Done.

That is the whole argument. Detection that does not localise the signal pushes the work back onto you without giving you a map.

What our own detector measurement shows

We publish our own numbers because otherwise you have no way to judge what a score means. On 2026-10-04 we scored two corpora with the DeAIze detector (local ensemble, lite tier) at a 30% flag threshold.

• Reference sample: n = 1504 passages from DeAIze guides not marked generated (metadata selection only; human authorship not independently verified). AI score mean 27%, median 20%, range 1-100%. Scores at or above the 30% threshold: 487 of 1504.
• Unedited model output: n = 12 passages generated from fixed prompts and not edited. AI score mean 52%, median 49%, range 11-91%.

Read those two rows carefully. The reference sample is not a clean human baseline. It is passages from our own guides, selected by metadata, and human authorship was not independently verified. The 487 of 1504 figure is a passage count at a threshold, not a count of independent documents and not a verified false-positive rate for human writers. Passages from the same guide are not independent samples.

What the measurement does show is separation. Unedited model output scored higher on average than the reference sample, with a wide range in both. That is what an editing tool should look like: useful signal, not a verdict.

The objection you are probably holding

You may be thinking: fine, sentence-level sounds better, but I do not want to spend money to find out, and I do not trust any detector anyway.

Both parts of that are reasonable. On trust: no detector output proves authorship. DeAIze does not provide an official Turnitin readout or a Turnitin score estimator, and we do not guarantee any particular score on another detector. If your institution runs Turnitin, what you need is an understanding of what that report measures, which is a separate question from what our app shows you.

On cost: the rewrite trial is 200 words after email confirmation, no credit card. That is not unlimited scanning and it is not a free tier that quietly resets. It is enough to paste one problematic passage and see whether the sentence-level output helps you edit. If it does not, you have lost nothing but the time to check.

There is also a harder version of the objection. If you are facing an academic integrity allegation, no detector comparison page solves that. Preserve your original drafts, your version history and your notes. For how panels actually assess a case, read Academic Integrity Hearing AI: What Panels Assess. For what your institution’s rules likely permit, Academic Integrity Policy AI: What Your University Actually Allows is the companion piece.

A worked example, clearly labelled as an illustration

This is a constructed example, not a measurement. It shows the editing decision, not a score.

Original sentence (flagged): “In today’s rapidly evolving digital landscape, effective communication plays a crucial role in organisational success.”

What is wrong with it: stacked hedges in one sentence, an abstract noun doing the work of a concrete one, and a phrase that could open any document on any subject. This is the pattern a sentence-level detector tends to mark.

Revised sentence: “Teams that write short status updates ship decisions faster, because nobody has to read twice to find the ask.”

The revision names a subject (teams), an action (ship decisions), and a cause (nobody reads twice). It could not open a different document. That is the test.

Now the part most people skip. Compare the revision with the original and check whether anything changed meaning. The original claimed communication is important to success. The revision claims short updates speed up decisions. Those are not the same claim. If the original claim is what you meant, keep it and cut the hedges instead: “Clear communication matters more than most teams admit, and the cost of unclear updates shows up in delayed decisions.”

The point of the example is that the edit has to preserve your claim, not just read better. A tool that suggests wording cannot know which of your claims are load-bearing.

Where each tool fits in a real workflow

Think in stages rather than in tool loyalty.

1. Draft. Write your own argument, data and conclusions. No tool substitutes for this.
2. Triage. Run a document-level check to see whether the draft reads machine-made at all. A free detector is fine here.
3. Localise. If it flags, switch to a sentence-level reading so you know which lines carry the signal. This is where the AI detector earns its place in your routine.
4. Edit. Rewrite the flagged sentences in your own voice. Keep your claims, cut the hedges.
5. Verify. Re-read the edited passage against the original for quantities, citations, negations and qualifiers. This step is not optional.
6. Record. Save the before and after, and note what you changed. If anyone asks later, you have a trail.

Step 6 is the one people skip and regret. If a dispute ever arises, an editing trail is worth more than any detector screenshot.

What DeAIze does not claim

We are not going to tell you we beat Scribbr on accuracy, because we do not publish a comparative accuracy figure and neither should anyone else’s marketing page convince you without a dated, reproducible run.

We also will not tell you that rewriting your draft lowers a score on someone else’s tool. It might. It might not. Different instruments, different definitions. What we can tell you is what our own detector reports and where it points you.

A few other boundaries worth stating plainly:

• Text you paste is processed for the current task only, never used for training and never resold.
• Detection and rewriting cover English and 9 other languages; the interface ships in 10 languages. English is the strongest.
• Uploads accept .txt, .docx and .pdf, and rewriting a Word document preserves fonts, styles and tables.
• Pricing is pay-as-you-go. Credits never expire and there is no subscription: Starter $20.99 for 1,000 words, Standard $44.99 for 3,000 words, Pro $99.99 for 10,000 words, Max $199.99 for 30,000 words.

Those are the facts you need to decide. Everything else is preference.

A note on the outside pressure driving these searches

The reason this comparison gets typed into search bars is not curiosity about two brands. It is that AI detection has moved from a novelty into institutional process. Universities publish policies. Journals ask for disclosure. Employers ask whether a cover letter was drafted with assistance. When a process exists, people look for tools that tell them where they stand.

That context matters because it changes what you should want from a detector. You do not want a verdict, because a verdict is someone else’s to give. You want a map. You want to know which sentences read machine-made so you can decide whether that is accurate or not, and fix it if it is not.

Scribbr’s free detector gives you a starting signal. DeAIze gives you the starting signal plus the locations. If you only need the first, use the first. If you need the second, that is what the sentence-level view is for. For a broader look at how these readings differ across instruments, AI Detector Comparison: What Each Tool Actually Reports walks through the definitions.

Ready to see which sentences read machine-made?

Paste one problematic passage and look at the sentence-level output. That single action tells you more than any comparison table, including this one.

• Start with the AI detector and read the per-sentence breakdown.
• If you want rewriting suggestions on the same draft, the draft editor keeps the original and the revision side by side.
• Check pricing to see which word count matches your workload, or create an account and use the 200-word rewrite trial after email confirmation. No credit card.

If you never buy anything, the free reading still tells you which lines to look at. That is the part we would want if we were in your position.