Voice fidelity is measurable. Bloomberry tracks how closely every generated post matches the behavioral patterns captured in each person's Voice Memory Layer — and improves that score automatically with every post, every edit, every approval.
From raw idea to publish-ready content in under a minute.
Voice Fidelity Score is a measurement of how closely an AI-generated post matches the behavioral writing patterns of a specific person — captured from post history, edit behavior, and approval decisions. A high Voice Fidelity Score means the output sounds unmistakably like that person. A low score means the output sounds like AI filling in a style description.. It is used to generate content that sounds like the actual person — not an approximation — and track improvement over time as Voice Memory accumulates.
Real examples of what Bloomberry generates.
Most employee advocacy programs fail before they ship. Not because of budget. Not because of tools. Because they ask people to post content that doesn't sound like them. No one shares something that makes them sound like a corporate newsletter. The fix isn't a better tool. It's building a content system that remembers how each person actually writes.
I've watched 3 advocacy programs fail from the inside. Every time, the pattern was the same: • Marketing sends templates • Sales team posts them once • Engagement is low • Everyone moves on The problem was never motivation. It was that nobody could find themselves in the content. When the post sounds like you, you share it. When it doesn't, you don't.
What is a Voice Fidelity Score?
A Voice Fidelity Score measures how closely an AI-generated post matches the behavioral writing patterns of a specific person. It is not a subjective rating — it is calculated from measurable signals: sentence rhythm match, vocabulary frequency alignment, hook pattern consistency, and edit rate on similar drafts.
How is voice fidelity different from voice matching?
Voice matching is the goal. Voice Fidelity Score is the measurement of how well that goal was achieved. Bloomberry uses the score to track improvement over time — showing whether each person's Voice Memory is accumulating enough signal to produce consistently high-fidelity output.
Why does voice fidelity improve over time?
Every post published, every edit made, and every approval granted adds behavioral evidence to the Voice Memory Layer. More evidence means better pattern recognition. Better pattern recognition means higher fidelity output. Bloomberry's Voice Fidelity Score tracks this improvement — the score for each person should rise as their Voice Memory accumulates.
Can two people in the same company have the same Voice Fidelity Score?
They can have the same numeric score, but that score is calculated against their own individual Voice Memory — not a shared baseline. A 90% Voice Fidelity Score for a founder means the post matches how that founder writes. A 90% score for a recruiter means the post matches how that recruiter writes. The metric is always person-relative.
Does Voice Fidelity Score apply to both LinkedIn and X posts?
Yes. Bloomberry calculates Voice Fidelity Score for each platform separately — because how a person writes a LinkedIn post differs from how they write an X post in format, but their vocabulary and sentence rhythm should be consistent. High voice fidelity on both platforms means the person sounds like themselves across all public content.
Generate posts that match your tone instead of generic AI output.
Every post published adds to Voice Memory. Voice Fidelity Score improves automatically — no additional setup required.