I built a node to stop chatbots from saving "Good morning" as the customer's name

[ricardoroberto]

Ever had a chatbot store “Good morning” as the customer’s name? Or “48 installments” parsed as a currency value?
That’s the bug that made me build NaLU AI.

What it does

NaLU AI is an API that extracts and validates structured data from natural language — the kind of messy, human replies
your chatbot actually receives.

Instead of regex or brittle keyword matching, it understands context:

Agent: “What’s your name?”
User: “Good morning! You can call me Ricardo”
→ { extracted_value: “Ricardo”, certain: true }

Available validators: name, full name, CPF (Brazilian ID), CNPJ, CEP (Brazilian postal), phone, email, postal code,
yes/no, birthdate, license plate, cancel intent, handoff intent, reply context analysis.

The n8n node

Install: n8n-nodes-nalu-ai

Starter workflow — detects if a user actually answered a question or just said “hello”:

Free tier: 3,000 credits/month, no credit card. Paid plans from $5.90/mo (billed in BRL).

Docs + playground: naluai.dev/en

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Happy to answer questions. Still early days — feedback from the n8n community would mean a lot.

[nguyenthieutoan]

This solves a real pain point - chatbots misinterpreting greetings as data is one of those subtle bugs that breaks user trust fast. The context-aware extraction approach (understanding “Good morning! You can call me Ricardo” vs just parsing for a name string) is the right way to handle this. Good to see Brazilian-specific validators like CPF, CNPJ, and CEP included too - that’s a detail most generic tools miss. Looking forward to seeing how this evolves!

[ricardoroberto]

Thanks so much!! I really appreciate you taking the time to dig into the details!

You nailed exactly why I built it. The “greeting-as-data” bug is sneaky because it doesn’t throw errors, it just quietly corrupts your records until someone notices a customer named “Good morning” three weeks later. Pattern matching can’t tell the difference; you need something that actually understands the intent behind the reply.

And yeah! The BR validators come from real pain. CPF/CNPJ/CEP show up in almost every Brazilian chatbot flow, and users type them in every format imaginable. I saw things like dots, without, and some answares mixed with chit-chat. Generic tools treat them as plain strings and miss the validation entirely.

A few things on the roadmap: more validators (and an easy way for people to request the ones they need), better multi-field extraction in a single call, and webhook-based async for bigger batches. If there’s a validator or use case you’d find useful, I’m genuinely collecting requests right now. Feedback like yours is shaping what comes next.

Thanks again for the encouragement!

[nguyenthieutoan]

The multi-field extraction in a single call will be a big one - right now making separate calls for each field adds up fast in higher-volume flows. For validator requests: an “intent confirmation” type that catches things like “maybe”, “I’ll think about it”, “perhaps” as soft negatives rather than treating them as yes/no would be genuinely useful in lead qualification flows.

[ricardoroberto]

That is a solid point about multi-field extraction. Reducing API calls per flow is a priority to keep it scalable and keep the overhead low.

Regarding the “maybe” or soft negative, the API actually anticipates this by design. When it detects an ambiguous answer, it flags it (like certain: false) and returns a suggestion_to_agent in the payload.

The idea is that your state machine or LLM evaluates this suggestion. It can either loop back to clarify the intent with the user or redirect the flow entirely, rather than forcing a hard yes/no and losing the lead.

By the way, you can test how it handles these edge cases in the playground on the site. The free tier gives 3,000 credits/month and requires no credit card to start.

Nalu ai - Playground

Thanks again for the feedback!