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An AI voice that learns your brand from every edit

Stop re-fixing the same drafts. GrowthMeteor turns every approve, edit and skip into a durable, readable rule and applies it to the next reply, so the agent sounds more like you each time you click.

Voice that learns learning
you editedr/SaaS draft · 2m ago

Meter is the best usage-based billing platform. Want a demo?

distilled into a rule
new voice rule

Open with the shared problem before naming Meter, never lead with the pitch.

Applied to every new draft3 rules
voice

Open with the shared problem before naming Meter, never lead with the pitch.

+9pp
channel

Keep replies in r/SaaS short; concise drafts get approved more often here.

+14pp
tone

A peer, founder-to-founder tone lands best on usage-based billing threads.

+11pp
rules that stop earning approvals fade out on their own

You keep making the same edit on every single draft.

Most AI reply tools never learn. You soften the pitch, cut the jargon, fix the tone, then watch the exact same mistake show up on the next draft, and the next. The voice never settles because nothing remembers what you corrected. GrowthMeteor closes that loop: your edits teach the agent, so the corrections actually stick.

  • ×Re-fixing the same tone and phrasing on draft after draft
  • ×A generic AI voice that never sounds like your brand
  • ×No memory of what you approved, edited or skipped last time
How it works

How GrowthMeteor learns your voice over time

Step 1

Every edit becomes a durable rule

When you change a draft before it posts, the agent compares your before and after and distills the consistent corrections into plain-language voice rules. Those rules are stored as readable text, not a black-box model, so the same fix never has to be made twice.

Edit distilled to a rule live
your draft · before

I totally get the frustration with RivalBill’s mis-billing — super common. Check out our metered invoicing!!

your edit · after

Metered billing reconciles usage nightly, so the invoice matches what was consumed.

stored voice rulebrand_voice

“Drop hype and exclamation marks. Lead with how metered billing works, then offer to help.”

source: you·plain text, not a modelnever re-corrected
Step 2

Rules steer the next draft automatically

Before writing each new reply, the agent recalls the rules most relevant to the mention and injects them into the draft. It learns where shorter wins, where a formal tone lands, and which topics and communities earn approvals, then tunes accordingly.

Rules steer the next draft live
incoming · r/SaaS · metered billing
recalled & injected
Shorter replies get approved more in r/SaaSlength
Formal tone lands for LinkedIn finance threadstone
Lead with proration on metered-billing topicstopic
Skip hype, no exclamation marksvoice
drafted with your voice1 of 4 rules applied

Usage reconciles nightly, so the invoice matches actual consumption. Proration is handled automatically on plan changes.

Step 3

Rules that stop working fade out

GrowthMeteor measures whether each learned rule actually lifts your approval rate. Rules that help are promoted, stale ones decay, and agent-learned rules that consistently hurt are pruned. Your own hand-written rules are never auto-removed.

Rules measured by approval lift live
baseline approval 50%promote ≥ +5pp · prune ≤ −5pp
Lead with proration on metered topicspromoted
+32ppagent
Drop hype, no exclamation markskept
+21ppyou
Formal tone for LinkedIn financedecaying
+4ppagent
Always open with a questionpruned
-11ppagent
your hand-written rules are never auto-removed
Who it’s for

Built for B2B SaaS teams who care how they sound

If your replies on Reddit and X are part of how you build trust, the voice cannot read like a bot. GrowthMeteor learns the way your team actually talks, so distribution scales without losing the voice that makes it land.

Founders

Teach the agent your voice once through normal edits, then trust it to draft in that voice as you scale.

Growth teams

Get drafts that improve with every review instead of re-correcting the same tone and length forever.

Brand & content

Keep an inspectable, human-readable record of the voice rules shaping every reply, not a model you cannot audit.

FAQ

Voice that learns questions, answered

How does the agent actually learn my brand voice?
It learns from your feedback, not a fixed prompt. When you edit a draft before it posts, GrowthMeteor compares your before and after and distills the consistent corrections into durable voice rules. Approvals and skips also feed the loops that learn which tone, length, topic and community earn the most approvals.
Is this fine-tuning a model on my data?
No. The learning is retrieval-based, your corrections become readable rules that are recalled and applied to each new draft. Nothing is used to train a shared model, and you can read exactly which rules are shaping a reply rather than trusting a black box.
How many edits before it improves?
It starts shaping drafts as soon as it sees a consistent pattern across a few recent edits, and the A/B length and per-community preferences kick in once there is enough signal to beat your baseline. The more you correct, the sharper the voice gets. Until then nothing is forced, every outcome is logged so you can see what it has learned.
What if a learned rule is wrong or stops working?
GrowthMeteor measures whether each rule lifts your approval rate. Rules that keep earning approvals are promoted, ones that go stale decay, and agent-learned rules that consistently hurt are pruned automatically. Rules you write yourself are never auto-removed and take priority.

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