ScriptGrain vs Bloomberry

ScriptGrain measures one person's voice across 45 attributes and holds every draft to it, across sixteen content types. Bloomberry solves the adjacent problem, getting a whole team publishing on LinkedIn in their own voices, with approvals.

The actual difference

Bloomberry is built around LinkedIn distribution for a team: briefs in, approved posts out, under many names. ScriptGrain measures one person's voice across 45 attributes and holds every draft to that profile across sixteen content types, blog, email, newsletter, long-form, wherever the writing goes.

Bloomberry's Pro tier is $49/mo for an individual, with team plans from $750/mo; a free tier covers 5 posts a month. Their site prices by region, so a UK visitor is shown a local-currency figure rather than the dollar one. ScriptGrain is £12/mo for one profile and £29/mo for three.

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.

How the connector works

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

ScriptGrainBloomberry
Entry price£12/mo (Writer, 1 voice profile)$49/mo (Pro)
Free tierYes, See your own voice profile and try one draft. No card.Free tier: 5 posts/month
Voices on entry plan1 (3 on Operator, £29/mo)One person's Voice Memory on Pro
How the voice is built45 measured style attributes, extracted from writing you have already published"Voice Memory", a per-person writing model trained on that person's past posts, which they say gets more accurate with every post generated. Output is measured by a "Voice Fidelity Score", their metric for how closely a draft matches that person's authentic style.

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

What Bloomberry is built for

Bloomberry is an employee advocacy platform. That's the category, and it tells you most of what you need to know. The pitch on its own site is that one company brief becomes original, approved posts for LinkedIn and X, written for founders, executives, sales leads and subject experts, each in their own voice, governed and approved before anything publishes (read 2026-09-30).

So the unit of work is a campaign. Someone at the company writes a brief, the platform fans it out into posts under several names, and an approval step sits in the middle. A "Company Brain" holds approved company knowledge and feeds each brief, which keeps the facts consistent across people who would otherwise each remember the product slightly differently.

That's a distribution problem, not a writing-quality problem. The hard part for a B2B team isn't that one person can't draft a post. It's that eight people each have a day job, nobody posts on schedule, and the founder's LinkedIn goes quiet for a month. Bloomberry is aimed squarely at that. It's a workflow tool with a writing engine inside it.

And to be fair, that focus is a real strength. If your goal is many named people publishing on LinkedIn with sign-off before anything goes out, a product designed around exactly that has fewer awkward seams than a general tool bent into shape.

One status note, because it affects anyone planning around it. On 2026-09-30, the Bloomberry home page banner reads "Bloomberry is going open source", and the self-serve sign-up buttons have been replaced by a waitlist and a demo request. Check what's available to you before building a process on it.

How Bloomberry handles voice

Voice sits inside a feature called Voice Memory. It's a per-person model trained on that person's past posts, and Bloomberry says it improves with every post generated. Each team member gets their own, which is the sensible design for the job: the CEO and the head of sales shouldn't sound alike.

The learning loop is the interesting bit. The more a person posts, approves and edits through the platform, the more material the model has. That suits a tool whose whole business is repeat publishing.

Output is measured by a Voice Fidelity Score, described as how closely a draft matches the person's style. Bloomberry doesn't publish a formula or a method for it. That's not an accusation; plenty of products keep scoring internals private. It just means that when a draft comes back with a number, you can see that the tool thinks it's close, but you can't see which features it measured or what moved.

There's also a scope question. Voice Memory learns from past posts, so it's shaped by social writing: short, public, performative. That's fine if LinkedIn is where the voice will live. It's a narrower base if you want the same person to sound like themselves in a newsletter, a long article or a sales email.

On price, Pro was listed from $49 a month for one person's Voice Memory, and Teams from $750 a month with one Voice Memory per member, both read on Bloomberry's pricing page on 2026-09-30. Their site prices by region, so a UK visitor may see a local-currency figure instead. Treat those as a snapshot, not a quote.

How ScriptGrain handles voice

ScriptGrain starts from measurement rather than a learning loop. You give it writing the person or brand has already published, and it builds a profile: 45 attributes in 8 layers, covering lexical choices, syntax, tone and register, rhetorical moves, punctuation and format, function words, content patterns, and quirks and cadence.

What does that look like in practice? Average sentence length and how much it varies. Contraction rate. Comma density. Semicolon and dash habits. Formality on a 0 to 10 scale. Confidence versus hedging. How the writer opens, whether they lead with evidence or the conclusion, which words start their paragraphs, how they split first and second person. The full list is in the writing voice attributes reference, and you can try the countable part yourself with the free writing style analysis tool.

A profile needs at least one sample of 50 or more words. Three or more pieces and around 3,000 words is the recommendation. Extraction usually takes under a minute; the median was 39 seconds when measured on 2026-09-28.

Then every draft gets scored from 0 to 1 against that profile, which is 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 comes in under 0.45. The score arrives with per-feature deltas, draft value against profile value, so you can see that the draft runs long on sentences and light on contractions rather than just being told it's "off". The method is published in the voice measurement framework.

Polish then 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. It reaches ChatGPT, Claude, Gemini, Cursor, Meta Muse and any MCP client through the MCP server and the REST API, read live every time. It's never a pasted prompt. Both the API and MCP server are on every plan including Free; the details are on the API and MCP page.

The same job in each tool

Take one job. A founder wants to publish an announcement about a product change, and wants it to sound like her, not like a press release.

In Bloomberry, she (or a marketing lead) writes a brief. The Company Brain supplies the approved facts. Voice Memory drafts a LinkedIn post in her style, drawn from her earlier posts. The draft comes back with a Voice Fidelity Score. A colleague approves it, it's scheduled, and the same brief quietly produces versions for the head of sales and a solutions engineer. The whole thing is built to move from brief to approved post across several people with very little friction. For that job, it's hard to beat.

In ScriptGrain, the same announcement starts from her profile, built earlier from her published writing. Say her profile shows short sentences, conclusion-first structure, dry asides and almost no hedging. The draft is written against those constraints and scored. Suppose it comes back in the partial-match band, and the deltas show the draft hedges more than she does and runs a longer average sentence. Polish pulls it toward the 0.9 target, and she sees what changed and why.

Now widen the job. She also needs a launch email, a newsletter paragraph and a blog post. In Bloomberry, that's outside what the tool is built around. In ScriptGrain, the same profile applies across all of them, read live by whatever tool she's drafting in. You can run a finished piece through the brand voice consistency checker to see whether it held, and voice drift is exactly what that check is there to catch.

Different jobs. Same announcement.

Which one fits which situation

Pick Bloomberry when the problem is coordination. You have several executives or experts who should publish on LinkedIn regularly, a marketing team that wants approval before anything goes out, and a campaign rhythm rather than a single author's voice.

Pick ScriptGrain when the problem is sounding like yourself, or like one brand, wherever the writing lands. That includes solo founders, consultants, writers with an established style, and small teams with one house voice who publish across several formats.

A few situations, plainly:

If you're weighing more than these two, the tools matrix lays the wider field side by side, and /vs collects the other comparisons. Prices for our side are on the pricing page.

Using ScriptGrain alongside Bloomberry

Using both is a reasonable setup, not a hedge. They don't compete for the same step.

The split is simple. Bloomberry runs the LinkedIn programme: briefs, per-person Voice Memory, approvals, scheduling. ScriptGrain holds the measured profile for everything else, and acts as a second opinion on the social drafts too. Paste a Bloomberry draft into the checker and you get a voice match score with feature deltas, which is a different lens from a fidelity score whose method isn't published. If the two disagree, that's information, not a problem.

A few practical notes. Build the ScriptGrain profile from long-form writing, not just posts; that's what makes the long-form side hold up. Keep one profile per person or brand, and if a team needs several, the Studio and Agency plans carry 10 and unlimited profiles respectively.

Don't expect the two to share state. Bloomberry's Voice Memory lives in Bloomberry; the ScriptGrain profile lives in ScriptGrain and travels by API and MCP. Neither imports the other, so if you're moving over, plan to rebuild the profile from source writing rather than converting anything. That takes under a minute per profile, which is the easy part.

The harder part is process. Decide who owns the profile, where approved drafts get checked, and what score counts as good enough. Then write it down. A number you don't act on is decoration.

And given Bloomberry's 2026-09-30 banner about going open source and its waitlist, keep the ScriptGrain profile independent of it. If the platform's availability changes, the voice stays yours.

Where Bloomberry is stronger

An employee-advocacy platform for B2B teams: one company brief becomes approved LinkedIn posts from founders, executives and subject-matter experts, each in their own voice. As of 30 September 2026 its site says it is going open source, with sign-up by waitlist or demo request. Bloomberry spreads one company brief across several people's LinkedIn feeds. We start from the other end: one person, one voice. Our executive page shows how we measure your real writing across 45 attributes, score every draft against it, and skip the ghostwriter's monthly retainer. If you'd rather your posts sound like you, read ScriptGrain for executives.

If your problem is that twelve executives should be posting on LinkedIn and none of them will write it themselves, that is a distribution and governance product and Bloomberry is built for it. ScriptGrain has no approval workflow and no publishing pipeline.

Questions

Is ScriptGrain better than Bloomberry?

They solve the problem differently. Bloomberry is built around LinkedIn distribution for a team: briefs in, approved posts out, under many names. ScriptGrain measures one person's voice across 45 attributes and holds every draft to that profile across sixteen content types, blog, email, newsletter, long-form, wherever the writing goes. If your problem is that twelve executives should be posting on LinkedIn and none of them will write it themselves, that is a distribution and governance product and Bloomberry is built for it. ScriptGrain has no approval workflow and no publishing pipeline.

How much does Bloomberry cost compared with ScriptGrain?

Bloomberry's entry plan is $49/mo (Pro), with this free tier: Free tier: 5 posts/month. ScriptGrain starts at £12/month (Writer, 1 voice profile) with a free plan: See your own voice profile and try one draft. No card. Bloomberry figures read from their pricing page on 30 September 2026.

Does Bloomberry learn my writing voice?

"Voice Memory", a per-person writing model trained on that person's past posts, which they say gets more accurate with every post generated. Output is measured by a "Voice Fidelity Score", their metric for how closely a draft matches that person's authentic style. ScriptGrain instead measures 45 style attributes from writing you have already published, and scores every draft against that measured profile.

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