Brand-Voice-Aware AI
You’ve probably noticed that AI can draft just about any information you ask for, and can turn that draft around faster than any human could.
You’ve probably also noticed that stock AI gets you in the ballpark, but if every blurb needs a rewrite you’re not saving any time. Generic AI doesn’t know your product, your customers, or how your team talks about features.
That’s why we built Changebot around three pillars: accuracy, tone, and distribution.
Accurate content that sounds wrong still needs a rewrite. Perfect tone with wrong details is worse. And neither matters if it doesn’t reach the right people. Here’s how we solve the tone piece.
How Changebot learns your voice
1. Writing samples during onboarding
When you set up Changebot, we ask for examples of your best release notes, changelog entries, blog posts, and support snippets. These samples teach us the phrases you use, the level of detail you prefer, and how you frame benefits versus features.
We look at patterns: Do you lead with customer impact or technical detail? Short and punchy or thorough and explanatory? Casual or buttoned-up? These samples become the foundation.
2. Company and product context
We work with your team to build a context layer that understands your product, your customers, and how you talk about both. Changebot knows that “the new alerting workflow” helps enterprise accounts reduce mean time to detect. We surface the customer impact that matters to your users.
This context prevents the generic AI problem where every feature sounds the same. Your updates reflect what actually matters to your users.
3. Your entire code history
Changebot scans your complete commit and PR history to build a timeline of your product’s evolution. We know what shipped when, what features connect to what, and how your product has grown.
This means when a new PR lands, we understand it in context. A small fix might be the final piece of a larger improvement your customers have been waiting for. A refactor might signal a shift in how a feature works. History gives us accuracy; accuracy makes the tone believable.
4. Hands-on tuning with your team
During implementation, we work directly with your PMM and product teams to tune the output. We collaborate on refinement until the updates feel right.
We adjust for audience (internal vs external), verbosity, CTA style, and the specific phrases your team loves (or hates). The goal: updates that need light edits, not rewrites.
5. Continuous learning from your edits
Here’s where it gets good. Every time you edit an update before publishing, Changebot learns from it. Your corrections teach the system what you prefer.
Over time, your brand voice gets sharper. The first month might need more tweaks. By month three, most updates ship with minimal changes. By month six, your team wonders why they ever wrote this stuff manually.
What “good” looks like
- Benefit-first framing: “Fewer failed payouts” beats “Updated payment retry logic.”
- Audience-aware language: Support teams, customers, and investors each get the version that resonates.
- Consistent structure: Short scannable summaries with optional depth for those who want it.
- Your phrases, not ours: If your team says “setup” not “onboarding,” Changebot says setup.
The three pillars working together
Tone doesn’t exist in isolation. An update that sounds perfect but gets the details wrong erodes trust. An accurate update that sounds robotic gets ignored.
Accuracy comes from scanning your code history and understanding what actually changed. Tone comes from learning your voice and improving with every edit. Distribution ensures the right version reaches the right audience in the right channel.
When all three work together, your team just reviews drafts instead of rewriting them. Your customers get updates that sound like they came from the team that built the product.
