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Webhook recieves lead gemini scores each lead with the help of the json code node and uses split node to send emails back to the sender for further advancement or a nurture email
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A production-ready LangGraph agent that discovers, audits, scores, enriches, and qualifies B2B prospects automatically.
🎬 Built a Conversational AI Cinema Booking Agent with n8n
I built this as a hands-on project to explore how AI agents, tool calling, and workflow automation can work together in a real-world booking process.
The agent can:
• Browse movies, showtimes, prices, and trailers
• Check seat availability
• Validate customer emails
• Generate unique Booking IDs
• Update movie inventory in Google Sheets
• Store booking records
• Send confirmation emails
• Retrieve bookings using Booking IDs
Tech stack: n8n, Google Gemini, Google Sheets, email automation, and an email verification API.
It’s still a demo with some limitations, but getting the entire process working end-to-end was the biggest takeaway.
Projects like this are helping me move beyond simply learning automation tools and focus on building practical AI-powered workflows that can solve real business problems.
Currently exploring more ways to combine AI agents with automation, APIs, and business workflows.
#n8n #AIAgents #AIAutomation #Automation #GoogleGemini #WorkflowAutomation #Contra #BuildInPublic
The idea
After my first build (the initial "Lead Follow-Up Automation," which was really just me learning the ropes), I wanted to prove I could build something that actually solved a real, painful problem for a real type of business not just a toy demo. Personal injury law firms are notorious for this: someone fills out a contact form after a car accident or injury, and if the firm doesn't get back to them within minutes, they just call the next firm on Google. Speed-to-lead is everything in that industry, and most small firms are still doing it manually, an intake person checking a shared inbox a few times a day. So I built Langford & Reyes as a mock client to show exactly how AI automation solves that gap.
The build
The pipeline was: a prospect fills out a Tally form → Make.com picks it up and routes it → Groq (running llama-3.3-70b-versatile) reads the intake details and qualifies the lead. Is this a real case, what's the injury, how urgent → Airtable logs everything as a structured record → Gmail fires off a response, formatted properly, to both the lead and the firm.
The whole point was compressing what used to take hours (or a full day) down to under 5 minutes from form submission to a qualified, routed lead sitting in the firm's inbox.
The challenges
This is where it got real.
Tally field mapping getting the form fields to actually map cleanly into Make.com without breaking when someone left a field blank or answered in an unexpected way.
Airtable 422 errors, classic "your data doesn't match what the API expects" pain, which meant going back and forth debugging exactly what Airtable's schema wanted versus what Make was sending.
Invalid JSON from multi-line prompts. Groq would sometimes return malformed JSON when the AI's response included line breaks or multi-paragraph reasoning, which broke the next step in the chain. That's a subtle one a lot of people don't anticipate until it happens to them.
Gmail formatting, getting emails to actually look like emails (not a wall of unformatted text) by manually inserting HTML <br> tags since Gmail doesn't auto-convert line breaks in API-sent messages.
The lesson
None of these are exotic problems they're the unglamorous plumbing issues that separate "I watched a YouTube tutorial and made a demo" from "I built something a real business could actually run on." That's really the value of Langford & Reyes as a case study: it's proof I've hit the annoying edge cases and know how to debug an AI pipeline end-to-end, not just wire together a happy path.
