Naming 6-Gates Protocol

Overview

Brand naming in 2026 is architectural engineering, not creative brainstorming. A name must be:

1. Machine-compatible - tokenized efficiently by LLMs, citeable by AI engines
2. Legally defensible - clear of trademark conflicts across jurisdictions
3. Voice-ready - pronounceable by assistants, invocable without friction
4. Narratively loaded - capable of carrying a brand story

Core principle: Optimize for AEO (Answer Engine Optimization) first, aesthetics second.

When to Use

• Creating a new brand, product, or company name
• Replacing a name due to trademark conflict
• Expanding internationally (multilingual requirements)
• Launching a voice-activated product or skill
• Naming for AI-first discovery (ChatGPT, Perplexity, Gemini recommendations)

When NOT to Use

• Internal project codenames (no legal/AEO requirements)
• Temporary campaign names
• Personal projects with no commercial intent

The 6-Gate Naming Process

digraph naming_process {
    rankdir=TB;
    node [shape=box];

    brief [label="1. BRIEF\nDefine constraints"];
    generate [label="2. GENERATE\nAI-assisted creation"];
    filter [label="3. LINGUISTIC FILTER\nMultilingual stress test"];
    legal [label="4. LEGAL FILTER\nTrademark screening"];
    aeo [label="5. AEO FILTER\nEntity strength test"];
    story [label="6. STORY FILTER\nNarrative potential"];

    brief -> generate -> filter -> legal -> aeo -> story;

    filter -> generate [label="fails" style=dashed];
    legal -> generate [label="conflicts" style=dashed];
    aeo -> generate [label="weak entity" style=dashed];
    story -> generate [label="no narrative" style=dashed];
}

Gate 1: BRIEF (Constraints Definition)

Define hard constraints BEFORE generating:

Output format:

brief:
  max_letters: 8
  max_syllables: 3
  target_markets: [FR, ES, US, UK]
  nice_classes: [9, 35, 42]
  avoid_phonemes: [ʒ, θ, ð]  # Hard for non-native speakers
  competitor_names: [Notion, Figma, Linear]  # Style reference
  forbidden_associations: [medical, financial, religious]

Gate 2: GENERATE (AI-Assisted Creation)

Use structured prompting with 5 typologies:

Typology 1: Analogical (Real-Word Transfer)

Words from unrelated domains applied to new context.

• Examples: Apple (tech), Amazon (retail), Jaguar (auto)
• Prompt: "Find words from [nature/mythology/geography] that evoke [core attribute]"

Typology 2: Metaphorical (Abstract Concept)

Abstract concepts made concrete.

• Examples: Slack (workspace tension release), Notion (idea capture)
• Prompt: "What single word captures the transformation from [pain] to [outcome]?"

Typology 3: Neologism (Invented Word)

New words with phonetic engineering.

• Examples: Kodak, Xerox, Häagen-Dazs
• Prompt: "Create a 2-syllable word with [plosive start] + [open vowel] + [soft ending]"

Typology 4: Portmanteau (Fusion)

Blend of two meaningful words.

• Examples: Instagram (instant + telegram), Pinterest (pin + interest)
• Prompt: "Blend [word1] + [word2] keeping recognition of both roots"

Typology 5: Empty Vessel

Meaningless but phonetically pleasing.

• Examples: Rolex, Vimeo, Skype
• Prompt: "Generate 5-letter combinations: CVCVC pattern, no existing meaning"

Generation Contract:

CONSTRAINTS:
- Max 8 letters
- Pronounceable in French, Spanish, English
- No existing trademark in Class 42
- Distinct from: [competitor list]

OUTPUT FORMAT per candidate:
- Name: [word]
- Typology: [1-5]
- Phonetic: /IPA transcription/
- Syllables: [count]
- Rationale: [1 sentence]

Gate 3: LINGUISTIC FILTER (Multilingual Stress Test)

Test each candidate across target markets:

Pronunciation Test

Run through TTS engines in 15+ languages:

• Google TTS, Amazon Polly, Azure Speech
• Listen for: mispronunciation, confusion, awkwardness

Cultural Connotation Check

Verify no negative meanings in:

• Target market slang (TikTok, Douyin, RedNote trends)
• Historical associations
• Religious/political sensitivities
• Homophones in local languages

Phonetic Hazards to Avoid

Kill criterion: If TTS mispronounces in >2 target markets, reject.

Gate 4: LEGAL FILTER (Trademark Screening)

Phase 1: Quick Disqualification

• Google: "[name] company" / "[name] brand" / "[name] trademark"
• Domain check: .com, .co, .io, .fr, .eu
• Social handles: Namechk scan all platforms

Phase 2: Formal Trademark Search

Phase 3: AI-Assisted Similarity Detection

EUIPO "Early TM Screening" detects:

• Phonetic similarity (sounds-like)
• Visual similarity (looks-like)
• Conceptual similarity (means-like)

Kill criterion: Any conflict in target Nice classes → reject.

Gate 5: AEO FILTER (Entity Strength Test)

A name must be recognizable as a distinct entity by AI systems.

Entity Strength Checklist

• Unique token: Does the name tokenize as a single unit or get split?
• No collision: Search "[name]" - does it return your intended context or noise?
• Semantic boundaries: Can AI distinguish "[name] the company" from generic usage?

Testing AEO Strength

Prompt to test:
"What is [NAME]?"

GOOD response: "I don't have information about [NAME]" (clean slate)
BAD response: "[NAME] is a common word meaning..." (collision)
BAD response: "[NAME] could refer to several things..." (ambiguity)

Keyword Difficulty Check

Use Semrush/Ahrefs:

• Keyword difficulty < 30 = good
• Keyword difficulty > 60 = buried by generics

Kill criterion: If name collides with high-volume generic term, reject.

Gate 6: STORY FILTER (Narrative Potential)

A name must be "tell-able" - capable of carrying a brand story.

The 5 Narrative Pillars Test

For each candidate, verify you can build:

Empty Vessel vs. Loaded Name

• Empty vessel (Kodak, Google): Brand fills meaning over time
• Loaded name (Instagram, Airbnb): Meaning is built-in

Both work. But you must choose consciously and build accordingly.

Vocal Signature Test

Say the name aloud 10 times. Ask:

• Does it feel good in the mouth?
• Would customers enjoy saying it?
• Does it have a "sticky" quality?

Kill criterion: If you can't tell a compelling origin story, reject.

Voice Assistant Constraints (2026)