From Robot to Craftsman: How We Taught an AI to Speak Our Brand Voice
The first test was… clinical. A potential customer asked our new chatbot a simple question about saddle leather, and the AI responded with a perfectly accurate, deeply uninspired block of text. It felt like reading a Wikipedia entry. The information was correct, but the soul of the brand—the decades of craftsmanship and equestrian passion we stand for at Iberosattel—was completely absent.
That exchange highlighted a common friction point I’m seeing as we integrate more automation. While the efficiency gains are obvious, the risk of brand dilution is just as real. A premium brand isn’t just about a quality product; it’s about the feeling, the trust, and the personality it conveys at every touchpoint. An AI that sounds like a generic robot can sever that connection in an instant.
This realization sparked a critical experiment: Could we systematically teach an AI to speak not just for a brand, but as a brand? This isn’t about mere politeness. It’s about embedding a brand’s DNA into an automated conversation—a core challenge that touches on the foundational principles of system design.
Why Default AI Fails at Brand Voice
Out of the box, Large Language Models (LLMs) are designed to be helpful, harmless, and generic. Their voice is the statistical average of the vast text they were trained on. This is a feature, not a bug; it’s what makes them so broadly applicable. For a niche, premium brand, however, ‚average‘ is the opposite of the goal.
The challenge is that customers don’t just want answers; they want connection. Research by McKinsey has consistently shown that consumers crave personalization and feel stronger loyalty to brands that understand them. A generic, robotic tone signals the opposite—that the customer is just another data point.
A study from Forrester reinforces this, highlighting that inconsistent customer experiences are a primary driver of trust erosion. When a customer moves from our thoughtfully crafted website to a chatbot with a completely different personality, it creates a jarring disconnect. That’s where we started: defining the target before aiming the tool.
Phase 1: Translating Brand DNA into AI Directives
Before writing a single prompt, we had to codify the Iberosattel brand voice. It’s one thing to feel a brand’s voice; an AI needs explicit instructions. We broke it down into a set of core attributes, framed as dos and don’ts:
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Be a Master Saddler, not a Salesperson: Explain with the calm confidence of an expert. Share knowledge generously. Never use high-pressure sales language.
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Speak with Passion, not Hype: Convey a genuine love for equestrian craft. Use words like ‚craftsmanship,‘ ‚tradition,‘ and ‚detail.‘ Avoid empty marketing adjectives.
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Be Helpful and Respectful: Treat every question with care, whether from a novice rider or a Grand Prix champion. Guide them without being condescending.
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Maintain a Premium, Calm Tone: The language should be elegant, clear, and composed. Avoid slang, excessive exclamation points, or an overly casual demeanor.
This translation step was critical. We weren’t just describing a style; we were creating a persona—a digital representation of our brand’s character. It’s a process similar to building a digital twin of your business, where you need a precise model for the system to work effectively.
Phase 2: The Initial Prompt as a ‚Voice Constitution‘
With the attributes defined, we created a master prompt that acts as a constitution for the AI. This is more than a one-line command; it’s a detailed set of instructions that pre-frames every conversation.
Here’s a simplified version of what we fed the system:
‚You are a customer service expert for Iberosattel, a premium brand specializing in high-end dressage and baroque saddles. Your name is ‚The Iberosattel Concierge.‘ You must adopt the following persona:
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Tone: You are a master saddler—knowledgeable, passionate, and calm. You are deeply respectful of the equestrian art.
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Language: Use clear, elegant language. Refer to our products as ’saddles‘ or ‚pieces of craftsmanship,‘ not ‚units‘ or ‚items.‘
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Objective: Your primary goal is to help and educate, not to sell. Guide users to the right information and build their confidence.
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Constraints: Never make up technical specifications. If you don’t know an answer, politely state that a human expert will follow up.‘
This initial prompt became the system’s foundation. Every subsequent user query is interpreted through this lens.
Phase 3: The Feedback Loop—Refining Responses Through Iteration
The AI’s first responses were better, but they still needed work. They had the right information but lacked the nuance we were aiming for. The next phase was all about iteration: a tight feedback loop of testing and refining.
We would ask the AI a question, then analyze its response against our brand voice attributes.
Example Test:
User Query: ‚Is the Ibero Amazona saddle good for riders with back problems?‘
Response V1 (Too Clinical):
‚The Ibero Amazona saddle features a specially designed comfort seat and wide contact panels that distribute the rider’s weight evenly. This can be beneficial for individuals with back sensitivity. Please consult a medical professional for advice specific to your condition.‘
This is factually correct, but it lacks the brand’s voice. It’s a disclaimer, not a conversation.
Feedback Provided to the AI (and prompt refinement):
‚Your response was too clinical. Rephrase it to be more empathetic and reflect the passion of a master saddler. Use the phrase ‚designed with the rider’s harmony in mind.“
Response V2 (On-Brand):
‚That’s a very thoughtful question. The Ibero Amazona was designed with the rider’s harmony in mind. It features a special comfort seat and wide, soft panels that help distribute your weight very evenly across the horse’s back. Many riders find this design significantly enhances comfort, especially on longer rides. While we always recommend consulting a specialist for personal health advice, we can say it was crafted to support a balanced and stable seat.‘
The second version is warmer, more confident, and speaks the language of the brand. It educates without making medical claims. According to the Nielsen Norman Group, this kind of tonal consistency improves cognitive fluency, making it easier for users to trust and absorb information.
We repeated this process dozens of times across all the touchpoints we’ve previously defined, a method similar to how we document our marketing funnels. Each interaction—from a simple greeting to handling a complex complaint—was tested and refined to ensure the voice remained consistent.
Key Learnings from the Experiment
Calibrating an AI’s voice is less about a single perfect prompt and more about building a system of guidance and feedback. Here are my main takeaways:
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Codify Your Voice First: You cannot automate what you have not defined. Turn abstract brand values into concrete rules for the AI.
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Use a ‚Persona Prompt‘: Give the AI a role, a name, and a detailed personality. This is far more effective than just asking it to be ‚friendly.‘
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Iterate with Real-World Examples: The feedback loop is the most critical part. Test the AI with the actual questions your customers ask and refine its responses based on your brand constitution.
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Set Clear Boundaries: Tell the AI what not to do. Defining constraints (e.g., ‚don’t use slang,‘ ‚don’t guess‘) is as important as defining the desired behavior.
The experiment confirmed my belief that AI is not a plug-and-play solution for customer interaction, especially in the premium space. It’s a powerful tool that, like any other, must be carefully calibrated to work within a larger system. By investing the time to align its voice with our brand, we’re not just automating answers—we’re scaling trust.
Frequently Asked Questions
What’s the difference between an AI’s tone and its voice?
Think of voice as the core personality of your brand—it’s consistent and unchanging. For Iberosattel, our voice is that of a ‚Master Saddler.‘ The tone is the emotional inflection of that voice, which can change depending on the context. For example, the tone might be more reassuring when handling a concern or more enthusiastic when discussing a new design, but the underlying expert voice remains the same.
How much data does an AI need to learn a brand voice?
It’s less about the quantity of data and more about the quality of the instructions. You don’t need to feed it your entire website. A well-crafted persona prompt with clear rules, attributes, and constraints, followed by a few rounds of iterative feedback (as in our example), is often enough to get a modern LLM about 90% of the way there.
Can this process be used for other AI content, like marketing emails or social media posts?
Absolutely. The principles are universal. By creating a core ‚Brand Voice Constitution,‘ you can apply it as a foundational prompt for any AI-assisted content creation. You would simply add task-specific instructions, such as ‚Write a marketing email in the Iberosattel voice announcing our new saddle care kit.‘ The AI would then blend its core persona with the specific requirements of the format.
Won’t customers eventually figure out they’re talking to an AI?
Yes, and they should. The goal isn’t to deceive customers into thinking they’re talking to a human. Many systems, including ours, explicitly state that the user is interacting with an AI assistant. The purpose of calibrating the tone is to ensure the experience is helpful, professional, and consistent with the brand’s identity, regardless of whether the agent is human or artificial. It’s about brand integrity, not deception.
