ScriptGrain vs ContentIn

ScriptGrain measures the voice ContentIn trains on. ContentIn's ghostwriter learns from your posts; ScriptGrain turns your writing into 45 measured attributes, scores every draft against the measured profile, and hands the profile to Claude, ChatGPT or any MCP client on every plan.

The actual difference

ContentIn trains on your posts and lets you take its word that the draft sounds like you. ScriptGrain measures your voice as 45 attributes, scores every draft against the measurement, shows the features that moved, and works for sixteen content types rather than LinkedIn alone. A ghostwriter running several clients gets a measured profile per client and a score to show each of them.

ContentIn's AI drafting in your voice starts on Growth at $39 a month; the MCP server needs Pro at $59. ScriptGrain's Writer plan is £12 a month with the connector on every plan, and the free plan measures a voice with no card.

ScriptGrain is not a replacement for the assistant you already use. It measures your voice from writing you have published and hands that profile to ChatGPT, Claude, Muse or any MCP client through its connector, then scores every draft against it. ScriptGrain makes your AI better at being you.

Connect ScriptGrain to Claude

Every AI voice tool compared: prices, scores and API access, checked

ScriptGrainContentIn
Entry price£12/mo (Writer, 1 voice profile)$39/mo (Growth)
Free tierYes, See your own voice profile and try one draft. No card.None
Voices on entry plan1 (3 on Operator, £29/mo)The AI Ghostwriter on Growth and up is trained on your own past posts
How the voice is built45 measured style attributes, extracted from writing you have already publishedThe AI Ghostwriter learns from the posts you have already published so drafts read like you rather than a template with your company name in it. Its FAQ asks for at least 20 of your existing LinkedIn posts. There is no voice score and no published measurement; the plan prices above were read from the pricing block on ContentIn's home page.

ContentIn figures read from their pricing page on 30 September 2026.

What ContentIn is built for

ContentIn does one job and does it in one place: LinkedIn. Its own positioning line calls it "The LinkedIn AI ghostwriter that writes posts like you", and the audience it names is founders, solopreneurs, coaches and consultants who want LinkedIn to generate leads. That focus is the product.

Coaches sit squarely inside that audience, and they have a different problem: a model's cadence isn't theirs. We built a page for exactly that. ScriptGrain for coaches shows how we measure your voice from your real newsletters and posts, then write drafts that carry your rhythm instead of a generic one. You publish content that sounds like you did the writing. Because you did.

So the useful question isn't whether ContentIn is a good writing tool in the abstract. It's whether your writing life is mostly a LinkedIn feed. If it is, the tool covers the whole loop: ideas, drafts, scheduling and analysis, with publishing through LinkedIn's official API. You don't paste text between four apps. You draft, queue and look at what landed, all in one tab.

That's a real strength, and to be fair, it's one ScriptGrain doesn't try to match. ScriptGrain measures and enforces a voice; it doesn't run your LinkedIn calendar. Hold that distinction in your head for the rest of this piece, because most of the decision comes down to it.

Plans matter here too, since the voice feature sits behind them. As read from ContentIn's pricing block on 2026-09-20, there is no free tier, only a 7-day free trial. Growth covers one LinkedIn profile with unlimited drafts and scheduling. Pro adds company pages, lead analytics and the MCP server. Several profiles or team seats mean a custom Ghostwriter or Agency plan. The table above has the numbers; what's worth noting here is the shape. One profile per plan, and more people means a conversation with sales.

How ContentIn handles voice

Here's the mechanic, from ContentIn's own pages as read on 2026-09-20. It reads your LinkedIn profile and your past posts, and its FAQ asks for at least 20 existing LinkedIn posts. From those it builds what it calls a personal writing model: your hooks, your rhythm, your topics. Then it keeps learning. Every edit you make and every post you publish retrains it.

That's a sensible design. Your published posts are the best evidence of how you write on LinkedIn, and a model that updates from your edits will drift toward your preferences over time. For someone who posts often, the feedback loop is tight and the tool gets better without you doing anything deliberate.

The limit is what you can see. ContentIn publishes no voice-match score. "Sounds like you" is asserted, not scored. You read the draft, you decide whether it sounds right, and you edit. There's no number that tells you a draft has slipped, no list of which habits moved, and no way to compare this month's drafts with last month's except by feel.

Is that a problem? Depends who's asking. If you're the author, your ear is a decent judge of your own voice, at least on a good day. If you're an editor checking a client's drafts, or a team trying to keep five people sounding like one brand, "trust the model" is a thin answer. You can't hand a client a feeling.

There's also the matter of reach. The model is trained on LinkedIn posts and serves LinkedIn. Your newsletter voice, your sales emails and your long-form writing aren't part of what it learns from or what it writes. That's scope, and you should price it in.

How ScriptGrain handles voice

ScriptGrain starts from a different place: measurement. It takes writing you've already published and turns it into a profile of 45 attributes across 8 layers: lexical, syntactic, tone and register, rhetorical, punctuation and format, function words, content patterns, and quirks and cadence. The attributes are concrete. Average sentence length and its variance. Contraction rate. Comma density. Semicolon and dash habits. Formality on a 0 to 10 scale. Confidence versus hedging. Opening style, argument structure (evidence-first, conclusion-first or narrative), paragraph openers, pronoun mix. The full list lives in the writing voice attributes reference.

A profile needs at least one sample of 50+ words. The recommendation is three or more pieces and about 3,000 words, because more writing gives steadier numbers. Extraction usually takes under a minute; the median was 39 seconds when measured on 2026-09-28.

Then the part that matters. Every draft is scored from 0 to 1 against the profile; that's voice match. On-voice drafts typically land between 0.85 and 0.95. Partial matches sit from 0.5 to 0.8. A clearly different voice falls under 0.45. The score arrives with per-feature deltas: the draft's value against the profile's value, feature by feature. So you don't just learn that a draft scored 0.71. You learn that the sentences ran long, the contractions dried up and the hedging crept in. The method is published in the voice measurement framework, so you can check the working.

Polish revises a draft toward a target score, 0.9 by default. Drafts are written with the profile as a constraint, and the app learns from the edits you save.

The profile also travels. Through the MCP server and the REST API, it reaches ChatGPT, Claude, Gemini, Cursor, Meta Muse and any MCP client, read live each time. It's never a pasted prompt. Both the server and the API are on every plan, Free included. You can see how that looks inside Claude or ChatGPT, and the wider picture is on the API and MCP page.

The same job in each tool

Take one job: a consultant wants a LinkedIn post on why most discovery calls run too long. Same person, same topic, same week.

In ContentIn, she opens the ghostwriter, which has learned from her published posts. She gives it the idea. It drafts a post with a hook and a rhythm it believes are hers. She reads it, cuts a line that feels too salesy, adds a sentence of her own, and schedules it for the morning. Her edits retrain the model. The whole thing takes a few minutes and never leaves the tool. If the draft is close, she's done; if it's off, she rewrites by hand and the model absorbs the correction.

In ScriptGrain, the job starts earlier. Her profile already exists, built from her published writing. She asks Claude or ChatGPT for the post, and the assistant reads her profile live through the connector, so the draft is written against her measured habits. Then it's scored. Say it lands in the partial-match band, somewhere between 0.5 and 0.8. The deltas show why: her real posts average short sentences, and this draft strung together long ones; she never opens with a question, and this one does. Polish pulls the draft toward the 0.9 target, she makes her own final edits, and she saves them so the profile keeps learning.

Notice what each path gives her. ContentIn hands her a finished loop: draft, queue, publish, analyse. ScriptGrain hands her evidence about the draft, and a profile she can carry into any tool. One is a workflow. The other is a measurement. Neither replaces the other's strength; the question is which gap hurts you more.

Which one fits which situation

Some situations are easy to call.

Pick ContentIn if:

Pick ScriptGrain if:

Use both when the jobs split cleanly. A founder might keep ContentIn as the LinkedIn engine, because the scheduling and analytics are genuinely good, while using ScriptGrain to measure the voice and keep it steady across everything else. And here's a check worth running: score a ContentIn draft against your profile (voice-match scoring is free on every plan). If it lands in the 0.85 to 0.95 band, the ghostwriter has you. If it doesn't, you've learned something no feeling could tell you.

Cost is part of the call, and the plans are on the pricing page. The short version: ScriptGrain's connector is on every plan, while ContentIn's MCP server needs Pro.

Using ScriptGrain alongside ContentIn

Running both is simpler than it sounds, because they don't compete for the same step.

Start with the measurement. Build your profile from your best published writing, ideally three or more pieces and around 3,000 words, across more than one channel if you have it. That gives the profile a fuller picture than LinkedIn posts alone. Then keep ContentIn doing what it does: ideas, drafts, scheduling.

The checking step is where the two meet. Before a ContentIn draft goes out, score it. The brand voice consistency checker is built for exactly this, and it surfaces the features that moved. Watch for voice drift: the slow slide that can happen when a model keeps retraining and nobody measures where it's heading. A model that learns from every edit will follow you, including when you're tired and sloppy. A score catches that.

If you're a ghostwriter or agency, the split is even more useful. ContentIn's custom Ghostwriter or Agency plans handle the multi-profile LinkedIn workflow. ScriptGrain holds a measured profile per client, so the evidence sits alongside the drafts. Studio gives you 10 profiles and 3 seats; Agency gives unlimited profiles and 10 seats.

One practical note on moving over, in case you're tempted. You don't migrate anything. There's no export to convert, because ScriptGrain builds its profile from the writing itself, not from ContentIn's model. Your published posts are the raw material, and they're yours already. If you ever want a cleaner read on what a generic assistant produces, run the output through the AI cliché checker too.

And if a whole brand is involved, brand mimic can build a profile from a public website. It needs explicit consent that you own or may use the site, which is the right rule. For the wider field, the tools matrix and the /vs index lay out the rest.

So the honest summary is short. ContentIn runs your LinkedIn. ScriptGrain tells you whether the writing still sounds like you. At the end of the day, that's a job worth having someone check.

Where ContentIn is stronger

A LinkedIn-first content tool: an AI ghostwriter trained on your past posts, a calendar, scheduled comments, analytics, and on Pro a LinkedIn MCP server for Claude and ChatGPT. Founder-authored comparison pages with dates.

If LinkedIn is the whole job and you want scheduling, comments and analytics beside the drafting, ContentIn is built for exactly that and ScriptGrain is not; ScriptGrain measures and writes, and leaves the posting to you.

Questions

Is ScriptGrain better than ContentIn?

They solve the problem differently. ContentIn trains on your posts and lets you take its word that the draft sounds like you. ScriptGrain measures your voice as 45 attributes, scores every draft against the measurement, shows the features that moved, and works for sixteen content types rather than LinkedIn alone. A ghostwriter running several clients gets a measured profile per client and a score to show each of them. If LinkedIn is the whole job and you want scheduling, comments and analytics beside the drafting, ContentIn is built for exactly that and ScriptGrain is not; ScriptGrain measures and writes, and leaves the posting to you.

How much does ContentIn cost compared with ScriptGrain?

ContentIn's entry plan is $39/mo (Growth) (Growth $31/mo billed annually; Pro $59/mo ($48 annually)), with no free tier. ScriptGrain starts at £12/month (Writer, 1 voice profile) with a free plan: See your own voice profile and try one draft. No card. ContentIn figures read from their pricing page on 30 September 2026.

Does ContentIn learn my writing voice?

The AI Ghostwriter learns from the posts you have already published so drafts read like you rather than a template with your company name in it. Its FAQ asks for at least 20 of your existing LinkedIn posts. There is no voice score and no published measurement; the plan prices above were read from the pricing block on ContentIn's home page. ScriptGrain instead measures 45 style attributes from writing you have already published, and scores every draft against that measured profile.

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