Brand Naming Agent

From a one-line description, AI fills in the kind of name, category, home or global use, customer and values, then makes 10 names from ListeningMind search data (volume, journeys, related searches, Google AI Overviews, community posts) and a 9-language root library. It drops names already in use, look-alikes of existing brands in any field and names with bad meanings in other languages, runs a Gemini/ChatGPT first-impression test and domain checks, and gives each name a promise, story, purpose statement and manifesto. Used by brand managers and founders. Markets: Korea, Japan, US.

How a result is made

ask_llm: If only a description is given, extract 2-4 category keywords with keywordExtractionPrompt.
check_intel_cache: Check whether the data is cached (0 credits).
get_market_intel: Collect the search evidence; with no data the UI falls back to LLM-estimated journeys.
ask_llm: Build the strategy brief with briefPrompt citing evidence ids (plus translationPrompt for the foreign style).
ask_llm: Generate a candidate pool per naming route with poolPrompt.
check_name_usage: Check whether candidates are already in use by search volume and .com.
ask_llm: Critique similarity and distinctiveness with criticPrompt (prefer=openai); repeat pool, check, critic up to 3 rounds until the target count is met.
ask_llm: Explain and score the selected names in chunks of 4 with detailPrompt.
check_domains: Check domain status for each name across the market TLD list.
ask_llm: Optionally deep-check chosen names (competitors, trademark risk, SNS, SEO, verdict).

Agents: llms.txt · agents.md · MCP · OpenAPI. Every page also answers Accept: text/markdown.

Brand Naming Agent

좋은 브랜드 이름은 고객의 말 속에 있습니다

사람들이 검색하는 단어, 자주 묻는 질문, 후기에 남긴 표현에서 이름 후보를 찾습니다. 이미 쓰이는 이름은 거르고, 이름마다 무엇을 약속하는지와 어떤 이야기를 담는지 함께 정리합니다.

Brand Naming Agent at a glance

Key ideasSearch journey Search volume and 48-month trend AI Overview Entity

Works well withCEP Finder Brand Finder Pulse Finder

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