Brand Voice Isn’t Enough. You Need a Brand Encyclopedia.

This post is written by a human. But not for lack of trying.

There are only so many times you can type “MAKE IT FUNNIER AND SMARTER!” before any self-respecting LLM gives up and tells you to go do it your own dang self.

That frustration is the same dead end brands are hitting with AI and “brand voice.”

The New Empty Promise

“We train it on your brand voice.”

That’s the AI vendor pitch today. It reminds me of the old “just one line of JavaScript on your site” claim. Sounds great. But meaningless in practice.

What this usually means is they’ve either asked the LLM to summarize your voice or shoved your brand guide into it. But brand voice isn’t captured in a style guide, a list of adjectives, or even a slick “tone of voice” deck from your agency.

Training a model on a brand voice summary is like handing a stranger a pamphlet about Ralph Lauren and saying: “Congrats, you’re the new spokesperson.” You’ll get words back. They might even sound right. But they’ll be shallow and insufficient for the dozens of contexts in which a brand needs to be authentically represented.

For example, all major LLMs describe the brand Theory with words like “sophisticated”, “confident”, “chic”, and “contemporary”. Sounds fine – until you realize the same words could describe 500 brands that are not Theory.

As my public service reminder: LLMs are trained on the internet’s version of your brand – scraps from product pages, social posts, and old press releases. Try asking AI about your own brand, look up the sources, and get ready to cringe.

From Brand Voice to Brand Encyclopedia

If you really want AI to work for brands – and not embarrass them – you need more than a vibe summary. You need a living, structured encyclopedia that AI can actually use.

That means:

1. Input way more: Your brand isn’t just three adjectives. It’s your terminology, aesthetics, key phrases, off-limit terms, signature features, suggested pairings, and the subtleties of your visual style. It’s product attributes that actually matter to shoppers – fabric, fit, silhouette – and the way you talk about them. It’s even how your models look and the attitude they project.

All of this has to be codified, and it has to be multi-modal: words, products, imagery, marketing assets. Without that level of input, AI will fall back on the internet’s generic version of you.

2. Use it selectively: Not every workflow needs the whole encyclopedia. PDP copy requires one set of inputs, campaign imagery another, an email subject line something else entirely. The system has to dynamically pull the right elements, fit-for-purpose, rather than dumping the whole brand manual into every prompt. That’s how you keep outputs specific, accurate, and relevant.
   
3. Account for context: Brands don’t sound or look the same everywhere. The way you style intimates is different from how you style kids’ graphic tees. The tone of a retargeting email is not the tone of a hero Instagram campaign. What plays in the UK may not play in the US. The encyclopedia has to flex across these contexts, while staying unmistakably “you.”
   
4. Tune frequently: Most brands adjust elements of their tone, merchandising focus, or styling every few months. A spring campaign doesn’t sound like a fall one. A back-to-school strategy doesn’t look like holiday. AI outputs have to reflect these shifts in near real time, otherwise you end up with stale copy and outdated styling cues.
   
5. Governance and guardrails: LLMs are probabilistic systems – which means sometimes they guess wrong. Without clear rules and oversight, they’ll drift off brand or generate things you’d never approve. That’s why you need governance: encoded do’s and don’ts, expert oversight to catch edge cases, and the ability for brand teams to review, override, and refine. It’s not just about output. It’s about control.

What the Heck Do You Know, Rohan?

This isn’t theory (pun intended). At Stylitics, we’ve been building Brand Encyclopedias for thousands of brands and plugging the right slice into AI workflows. Yes, it takes real investment – research, curation, expertise – but the payoff is massive: a step-change in output quality across outfitting, data enrichment, AI image generation, and quality control.

My prediction: brands that stop at “brand voice” in AI will sound generic. Brands that build Brand Encyclopedias (or tap into ours) will sound like themselves.

Now I need to go write an encyclopedia about myself for AI. Wish me luck. – Rohan

Authors

Rohan Deuskar

Founder & CEO

Rohan Deuskar is the Founder and CEO of Stylitics, the leading AI-powered outfitting and visual merchandising platform used by over 100 of the world’s top retailers. He writes about the intersection of AI, retail, and shopper inspiration.