Built a RAG-powered intake chatbot for PI law firms. Here's the full breakdown 👇

What it does for the firm:

Visitor lands on the site, describes their injury, bot qualifies them, sends them a confirmation email + SMS, pings the attorney on Slack, and logs the lead to Google Sheets. Zero human involvement until the attorney shows up for the call.

How it's built (n8n):

Workflow 1 — Ingestion

Schedule Trigger → DELETE old vectors → scrape website → extract HTML → chunk with Character Text Splitter → embed with Mistral → store in Supabase

Workflow 2 — Chat

Chat Webhook → AI Agent → Respond to Webhook

Agent tools: Supabase Knowledge Base, Google Sheets, Gmail, SMS, Slack

Memory: Window Buffer Memory (keeps conversation context)

A fix worth mentioning:

First version was stacking duplicate vectors every time the ingestion ran. Added a DELETE call before re-ingestion — wipes the old data, rebuilds clean every time.

Stack:

n8n · Mistral · Supabase · Google Sheets · Gmail · Twilio · Slack

This is a demo but the whole thing is deployable as-is. Would love feedback — especially if anyone's built something similar for legal.

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Built a RAG-powered intake chatbot for PI law firms. Here's the full breakdown 👇