{"slug": "why-is-your-chatbot-saving-good-morning-as-the-customer-s-name", "title": "Why is Your Chatbot Saving \"Good Morning\" as the Customer's Name? 🤖🤦‍♂️", "summary": "A developer built NaLU AI, a lightweight API and MCP Server that uses semantic NLU to clean, structure, and validate conversational data in real-time. The tool automatically filters out greetings and irrelevant text from user inputs, such as preventing a chatbot from saving \"Good morning\" as a customer's name. NaLU AI offers 13 built-in semantic validators for names, documents, postal codes, and conversational context, integrating with no-code tools like n8n and Make.", "body_md": "The classic struggle of chatbot data extraction, why your complex regex is failing, and how to fix it in 30 seconds using semantic NLU.\n\ntags: webdev, ai, chatbots, javascript\n\nWe've all been there. You spend days building a sleek WhatsApp chatbot or a customer service agent. You write what you think is the perfect prompt or input validation.\n\nThen, your bot asks:\n\n\"Hi! What is your full name?\"\n\nAnd the user replies:\n\n\"Good morning! I'm John Doe.\"\n\nYour traditional validation or naive regex captures the input, and boom—your CRM database now has a new client officially named **\"Good morning!\"** or **\"Good morning! I'm John Doe.\"**\n\nEven worse, you ask for a Brazilian postal code (CEP) and the user types: *\"It is 01310-100\"*. Your strict regex fails because it's not strictly digits, or your lazy regex fails to extract it.\n\nTraditional chatbot validation is broken. Relying on complex regex patterns is a maintenance nightmare, and raw LLM prompts are slow, expensive, and prone to hallucinations.\n\nI got tired of these universal chatbot struggles, so I built ** NaLU AI**. It’s a lightweight API and MCP Server designed specifically to clean, structure, and validate conversational data in real-time.\n\nNaLU combines a fast deterministic layer with semantic LLM validation in multiple languages (English, Portuguese, Spanish). Instead of processing raw strings, it understands **context**.\n\nLet's see it in action.\n\nHere is how you can easily validate and extract a clean full name from a conversation using a simple JavaScript `fetch`\n\n(or standard cURL):\n\n``` js\nconst response = await fetch('https://api.naluai.dev/v1/extract/name', {\n  method: 'POST',\n  headers: {\n    'Authorization': 'Bearer YOUR_TOKEN',\n    'Content-Type': 'application/json'\n  },\n  body: JSON.stringify({\n    agent_input: 'Good morning! Whats your name?',\n    user_input: 'Good morning!',\n    language: 'pt-BR'\n  })\n});\n\nconst result = await response.json();\nconsole.log(result);\n```\n\nThe JSON Response:\n\n```\n{\n  \"obtained\": false,\n  \"extracted_value\": \"\",\n  \"confidence\": \"high\",\n  \"certain\": false,\n  \"reasoning\": false,\n  \"suggestion_to_agent\": \"Good morning again! Could you please tell me your name?\",\n  \"validator_used\": \"validate_name\",\n  \"engine\": \"llm\"\n}\n```\n\nNotice that:\n\nIt automatically filtered out the greeting (\"Good morning isn´t a valid name\").\n\nIt suggest a \"try-again-message\".\n\n13 Built-in Semantic Validators\n\nNaLU AI comes with 13 ready-to-use validators:\n\nvalidate_name (extracts clean proper names, ignores titles and greetings)\n\nvalidate_cpf / validate_cnpj (validates Brazilian documents using mod 11 check)\n\nvalidate_cep (extracts postal codes and returns enriched address data)\n\nvalidate_handoff (detects if the user wants to speak to a human, measuring urgency from 1 to 3)\n\nvalidate_reply (analyzes conversational context like counter-proposals or indirect answers)\n\nBuilt for n8n, Make, Cursor & Claude Code\n\nSince it is a standard REST API, it integrates out of the box with no-code tools like n8n and Make.\n\nEven cooler, it exposes itself as an MCP Server, allowing you to add it directly to Cursor or Claude Code to perform semantic tasks locally!\n\nGive it a try! 🚀\n\nNaLU AI is free to start. The free tier gives you 3,000 free credits per month (no credit card required), and paid plans start at less than a fraction of a cent per validation (R0,0058/ 0.001 USD).\n\nStop losing clients to bad chatbot regex. Clean your database and make your agents truly smart in 30 seconds.\n\n👉 Test it out in the playground: naluai.dev\n\nLet me know in the comments how you are currently handling user data extraction in your chatbot webhooks!", "url": "https://wpnews.pro/news/why-is-your-chatbot-saving-good-morning-as-the-customer-s-name", "canonical_source": "https://dev.to/ricardo_carneiro_eb9caf27/why-is-your-chatbot-saving-good-morning-as-the-customers-name-2dc4", "published_at": "2026-05-26 11:25:39+00:00", "updated_at": "2026-05-26 11:33:35.789855+00:00", "lang": "en", "topics": ["natural-language-processing", "ai-tools", "ai-products", "artificial-intelligence"], "entities": ["NaLU AI", "NaLU"], "alternates": {"html": "https://wpnews.pro/news/why-is-your-chatbot-saving-good-morning-as-the-customer-s-name", "markdown": "https://wpnews.pro/news/why-is-your-chatbot-saving-good-morning-as-the-customer-s-name.md", "text": "https://wpnews.pro/news/why-is-your-chatbot-saving-good-morning-as-the-customer-s-name.txt", "jsonld": "https://wpnews.pro/news/why-is-your-chatbot-saving-good-morning-as-the-customer-s-name.jsonld"}}