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Knak / 2024-2026

AI Brand Voice

Knak 2024-2026 AI/ML

Feature enabling customers to control AI-generated content alignment with brand guidelines. Achieved 25% increase in MAU/WAU for AI features.

AI/ML UX Design Product Strategy
AI Brand Voice
25%
MAU/WAU Increase
All Content Gen
AI Features Integrated
Manual → Automatic
Brand Context

Design Highlights

Overview

I designed AI Brand Voice as a foundational control layer for Knak's AI-generated content. It carries brand context across text generation, subject lines, and full email generation so teams get consistent output without repeating the same instructions.

Challenge

Before Brand Voice, users had to manually add brand context every time they prompted an AI generation feature. There was no persistent memory or shared context across AI interactions—no enforcement of brand standards and no consistency between generated outputs. This created duplicate effort for every generation request and meant that brand compliance depended entirely on the user remembering to include the right context each time. The result was inconsistent AI outputs that required manual rewriting to match brand guidelines.

Approach

I designed one system for two distinct needs: admins configuring brand standards and marketers generating content. Admins can define voice attributes and flexible rules for tone, personality, language, and syntax. For end users, I made the integration invisible so brand context is applied automatically without adding steps to the creative flow.

Solution

I built a configuration experience where admins define voice attributes and encode standards as flexible rules, from broad tone direction to precise language and formatting conventions. Once configured, every Knak AI feature pulls those attributes and rules into its context automatically.

Impact

Brand Voice contributed to a 25% increase in MAU/WAU for AI features. It removed repeated prompting, improved consistency across generated content, and gave admins centralized control while reducing manual review and rewriting.

Key Learnings

I learned that the best AI guardrails are often the ones end users never have to think about. Flexible rules worked better than rigid settings because every brand expresses standards differently, while separating admin configuration from everyday generation kept the experience fast.

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