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Quote Follow-Up Automation for HVAC & Plumbing
Most home-service quotes don’t get rejected, they just go quiet. I built a system that follows up on them automatically.
What it does
Takes each quote through a simple form
Schedules up to 8 timed texts over 30 days
Stops instantly on a reply, a STOP, or a booked/lost update
Uses AI to draft replies to objections, with a human approving every one
Built with safety first
No duplicate texts, even when processes overlap
Only contractor-approved wording is sent
Texts go out only during local business hours
Opt-outs are honored immediately
Stack: n8n, Postgres, Groq, Docker, Twilio
Status: built and tested end to end, ready to pilot.
I build automation systems like this for service businesses. Message me if you’d like one for yours😊 .
I’d love to hear your suggestions. What would you add or change in a follow-up system like this? If you run an HVAC or plumbing business, what happens to your quotes today? Tell me where the follow-up breaks down.
Stopping the sequence on a booked/lost update is the part most follow-up setups skip, so good to see it built in. One thing I'd add: a quick way for the contractor to mark a quote closed from their phone, since plenty of these jobs get won over a call that never touches the...
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Which design should I choose for MorningAI — A or B?
Drop your choice in the comments! 👇
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Why AI Agent Demos Break in Production
An AI agent demo only has to work once, on a clean input, with you watching. In production it runs again and again on messy real inputs, and nobody's watching.
I've built AI into real products, from a repricing engine that runs 24/7 for a US Amazon seller to a voice automation platform for a company in Germany. These are the three things I put in place before any agent goes live:
1. Scoped tools, not open access
Give the agent a short list of actions it's allowed to take, each with its own limits, instead of raw access to your database or APIs. Anything that touches money, customers or deleting data needs a human to approve it.
2. A plan for when the model fails
Model calls are slow, they time out, and sometimes they return something that makes no sense. Run agent work in background jobs with timeouts and retries, check the output before acting on it, and hand the task to a person when it still can't be trusted.
3. A log of every decision
Store every input, tool call and output. When the agent does something strange (and at some point it will), you can see exactly why and fix it instead of guessing.
None of this shows up in a demo. All of it decides whether the agent is still running a month later.
Planning an AI agent for your product? I offer a free build audit: calendly.com/roshaanali-business
TITAN Nexus — Enterprise AI Automation Platform
One Intelligence. Infinite Possibilities.
TITAN Nexus is an AI-powered automation platform designed to help businesses streamline workflows, coordinate intelligent AI agents, manage approvals, and monitor task execution from one centralized dashboard.
What TITAN Nexus Can Do
AI Agent Orchestration: Coordinate specialized AI agents for different business tasks.
Workflow Automation: Build and manage multi-step processes to reduce repetitive manual work.
Smart Task Execution: Plan tasks, execute authorized tools, track results, and handle errors.
Approval & Security Controls: Manage permissions and require approval for sensitive operations.
Real-Time Monitoring: Track workflow status, execution history, and system activity.
Data & Backup Management: Support persistent task records, backup exports, and recovery workflows.
Integration-Ready Architecture: Designed to support external business tools and services as connections are configured.
Our Mission
To make business automation more accessible, organized, secure, and scalable by bringing AI agents, workflows, and operational visibility together in one platform.
Built by Shubham Tripathi
TITAN Nexus — Smarter Workflows. Greater Control. Intelligent Automation.
Great 👍🏻