The Problem with Generic AI Content

You've probably tried ChatGPT for marketing content. The output is decent — but it sounds like everyone else. That's because general-purpose AI models don't know your brand, your audience, or the way you naturally communicate.

Meg's Brand Voice Engine was built to solve exactly this problem.

Step 1: Website Analysis

When you first sign up, Meg scrapes your website to understand your brand's existing messaging, tone, and terminology. She analyses your "About" page, service descriptions, blog posts, and any other public content to build an initial voice profile.

Step 2: Onboarding Questions

During setup, you answer a short set of questions about your audience, industry, and communication style. Do you prefer formal or casual? Are you data-driven or story-driven? These preferences are layered on top of the website analysis.

Step 3: Tone Selection

You choose from three core tone profiles — Professional, Friendly, or Bold — and can fine-tune from there. This sets the baseline emotional register for all generated content.

Step 4: Continuous Learning

Every time you edit, approve, or reject a piece of content, Meg learns. If you consistently change "utilise" to "use" or remove exclamation marks, she adjusts. Over time, the content gets closer and closer to how you'd write it yourself.

Step 5: Per-Platform Adaptation

Your voice might be slightly different on LinkedIn versus X. Meg understands this. She maintains your core voice while adjusting formality, length, and structure for each platform's norms.

The Result

After a few weeks of use, most Meg users report that the generated content requires minimal editing — some publish it as-is. That's the goal: content that sounds like you wrote it on your best day, every single time.