WritingTwelve seconds a round
Spot the default
Two replies to the same email, both written by AI. One had a voice profile and one had none. You have twelve seconds to say which.
What does your email writing say about you?
Paste two emails you have actually sent. You get your writing fingerprint, measured from the text rather than guessed at, and one new email written in your voice.
- Free
- No account
- Numbers appear instantly
- Two emails is enough
How it works
Three steps, no sign-up
Paste two emails you have sent
Real ones, not samples. Two is the minimum because one email shows a mood and two start to show a habit. You can add up to four, and each is read up to 3,000 characters.
See the measurements at once
Sentence length, rhythm, hedging, greetings and sign-offs are counted in your browser the moment you press the button. There is no network call, so this part cannot fail.
Read the interpretation
A few seconds later an AI model adds the habits behind those numbers, each with a line from your own email as evidence, and writes an email in your voice.
What you get
What you get, and where each part comes from
The numbers are counted in code before any model sees your text. The model is handed them and told not to contradict them.
| Part | Where it comes from | What it tells you |
|---|---|---|
| Sentence length and rhythm | Counted in code | Average, shortest and longest, and where your sentences land: 1 to 5 words, 6 to 10, 11 to 15 and so on. Rhythm is the most recognisable thing about how someone writes, and an average hides it. |
| Questions and exclamations | Counted in code | How often you ask rather than assert. |
| Hedging | Counted in code | Counts of 20 softeners such as “just”, “maybe”, “I think” and “sort of”. |
| Autopilot phrases | Counted in code | 17 stock phrases such as “circling back” and “per my last email”, listed when you used them. |
| Greeting and sign-off | Counted in code | Whether you greet by name and close with a sign-off: never, sometimes or always. |
| Formality and directness | Counted in code | Two scores out of 100, composed from the measured counts on the page. They describe your writing; they do not grade it. |
| Consistency across emails | Counted in code | Whether you write the same way to everyone, shown email by email. |
| Habits, tells and the sample email | Model judgement | Interpretation of the numbers, with a line from your own writing as evidence for each habit. A reading that comes back generic is rejected instead of shown. |
Background
Why it works this way
How do you find out your own email writing style?
Take two or more emails you have actually sent, and measure them rather than judging them: average sentence length, how often you hedge, whether you greet by name, how you sign off, and which words repeat. Those patterns are your voice. A single email shows a mood, not a habit, which is why one is never enough.
The tool above does exactly that. It counts first, in code, and only then interprets what the numbers mean and quotes the lines that prove it.
Why most writing analysis is not worth much
Ask a chatbot to describe your writing and it will tell you that you are clear, professional and friendly. That is true of almost everyone who writes competent email, which makes it useless. It is not a description of you, it is a description of the genre.
It will also give you numbers: average sentence length, reading level, formality score. A language model does not count words, it predicts what a plausible answer looks like, so the figure will be in a believable range and it will not be your figure.
So this works in the other order. Your text is measured in code first, and the model is handed those measurements and told not to contradict them. It is allowed to interpret and to quote you, and nothing else.
Limits
What it cannot tell you
Every tool here is a first pass with edges. These are this one's.
- Two emails is a small sample. It describes those emails. A third or fourth makes the habits more trustworthy.
- It is built around English. The counting expects English words and English hedges and autopilot phrases.
- Formality and directness are descriptions, not grades. A high or low number is not good or bad. It is how your writing reads.
- The written reading can be missing. If the model is busy, or you have run it ten times in an hour, you still get every measurement.
- The sample email is an illustration. It is written to show the profile working. It is not meant to be sent.
Your data
What happens to what you give it
Read off the code that does it, not written from memory.
- In your browser
- The measurements: sentence lengths, hedging, greetings and sign-offs. They appear instantly, with no network call.
- Sent to our server
- The emails, so a model can write the reading. Each is cut to 3,000 characters.
- Sent on to a model
- Yes, for the written reading only: a small open model (Gemma) through OpenRouter.
- What we keep
- Nothing you paste. A request counter tied to your IP address (10 readings an hour) holds no content.
Questions
Questions people ask, answered plainly
13 answers, grouped. Each one is written against what the tool actually does.
Using it
Which emails should I paste?
Ones you actually sent, written without help from an AI, to real people. Different recipients help, because the results show whether you write the same way to everyone. Remove anything you would not want to send to a third-party service.
Why does it need two emails?
One email tells you about one email. Two is the minimum for separating a habit from a one-off, which is the entire point of a voice profile. Three is better, and the analysis will use up to four if you add them.
How long should the emails be?
Around seventy words in total is the least it will work with, and each email is read up to 3,000 characters. Very short emails give the analysis too little to work with, and the page tells you when that is the case.
How it works
How does this analyse my writing voice?
It measures your text in code first: average sentence length, how often you ask questions, how much you hedge, whether you greet by name, how you close, and which words you lean on. Only then does a language model interpret those numbers and quote your own lines back to you. That order matters, because a language model cannot count, so anything it tells you about your sentence length without measurement is a guess.
Is this different from asking ChatGPT to analyse my writing?
In one specific way. A chat model will happily tell you your average sentence length and be wrong, because it is estimating rather than counting. Every number here is computed from your actual text before the model sees it, and the model is told not to contradict them. A reading that comes back as generic description is rejected before you see it.
What are autopilot phrases?
Stock lines people write when they are not paying attention, such as “I hope this email finds you well”, “just checking in”, “circling back”, “per my last email” and “at your earliest convenience”. The tool checks for 17 of them and lists the ones you used.
What do formality and directness mean?
Each is a score out of 100 composed from measured counts on the page. Formality leans toward casual at the low end and formal at the high end. Directness runs from heavily hedged to very direct. They describe how your writing reads and are not a grade.
Is this a tone checker?
No, and the difference matters. A tone checker looks at one email and tells you whether it reads as rude or unclear before you send it. This looks across several emails you have already sent and describes the patterns that repeat, which is a different question: not whether this email is good, but what your writing is actually like.
Your data
Do you store the emails I paste?
No. The measurements happen in your browser. For the written reading, the emails go to our server, which measures them again so the model cannot be fed made-up numbers, and passes them to an AI model to write the interpretation. Nothing is written to a database. We keep a request counter tied to your IP address, limited to ten readings an hour, which holds no content.
Which AI reads my emails?
Gemma, a small open model from Google, reached through OpenRouter. We do not control how those providers handle data, so if an email contains something sensitive, edit it before you paste.
Why did the written reading not appear?
It depends on a model responding in time. If it is slow, unavailable, or you have run the tool ten times this hour, the reading is skipped and the page says so. The measurements are computed in your browser and are always complete.
Mailient
What can I do with the profile?
Most people use it to notice something they did not know they did, usually the hedging or the autopilot phrases. If you want it applied rather than just observed, that is what Mailient does: it builds the same profile from your real sent mail and uses it to draft the replies you owe.
Is it free, and do I need an account?
Free, and no account. Paste two emails, get the profile. There is no trial, no card and no sign-up wall, because a tool you have to register for is not a tool you would try out of curiosity.
More free tools
Try another one
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Paste the subject lines in your inbox. Get them sorted into what needs you, what can wait and what a machine sent, with the reason for each.
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Meeting commitment finderWhat did you just agree to?
Paste a meeting transcript. Get every promise made in the room, yours and theirs, with the ones most likely to be forgotten flagged first.
Now imagine it doing that to your whole inbox.
Mailient builds this profile from your real sent mail, then uses it to draft every reply you owe, on a schedule, waiting in your Gmail drafts for approval.