Hey everyone, I recently built an AI-powered lead qualification and routing system designed to automate how inbound leads are evaluated and distributed across a sales pipeline. Here’s what the system does: • Captures inbound leads (from Facebook Ads) via webhook • Normalizes and validates incoming data • Checks historical lead data to identify new vs. returning prospects • Uses AI to score intent and classify each lead • Logs AI decisions for transparency and auditability • Automatically routes leads to Sales, Nurture, or Discard • Updates HubSpot accordingly • Sends real-time Slack notifications for high-intent prospects How It Works Once a lead enters the system, it goes through a structured validation and enrichment layer before being passed into an AI decision engine. The AI evaluates intent and recommends the next best action. Based on that recommendation, the workflow automatically routes the lead to the appropriate pipeline while maintaining clean CRM data and logging all decisions. The architecture is modular and production-ready, meaning it can scale across different lead sources and CRM environments. Benefits • Eliminates manual lead qualification • Improves response time for high-intent leads • Keeps CRM clean and structured • Reduces sales team workload • Scales automatically as lead volume increases If anyone’s interested, here’s a complete overview of the workflow where I walk through the full system architecture: 👉 https://youtu.be/CknV7KcYVWA Happy to answer questions or discuss improvements.

What Actually Matters in Automation (Not the Tools) Most people think automation is about speed. It’s not. Automation is about reducing mistakes while scaling decisions. If you miss this, everything else falls apart. 1. Process clarity comes first:- >Before you automate anything, you should be able to answer this clearly: What starts this process? What information is required? What decisions are being made? What ends the process? If you can’t write this in plain language, automation will only hide the confusion — not solve it. Clear process → reliable automation. 2. Decision logic matters more than actions:- Sending messages, updating sheets, triggering APIs — that’s easy. >The hard part is deciding: when to act why to act when not to act Good automation is decision-driven, not action-driven. 3. Context is non-negotiable:- Automation without context behaves like spam. >Your system should know: what already happened who interacted last what stage the user is in what the last outcome was Context turns automation from “noise” into help. 4. Boundaries prevent damage:- >Every automation needs limits: maximum attempts clear stop conditions escalation rules If your system doesn’t know when to stop, it will eventually cause problems at scale. 5. Visibility is safety:- If automation fails silently, it’s dangerous. >You should always know: when something breaks what decision was made why it happened Logs and alerts matter more than fancy dashboards. 6. Consistency beats intelligence:- A predictable system is more valuable than a smart one. If the same input produces different outputs, trust disappears. Consistency is what allows automation to scale safely. 7. Human override is not optional:- The best automation still allows human control. Not because automation is weak — but because judgment, nuance, and accountability still matter. Automation should assist decisions, not escape responsibility. Final truth::-- Tools change. Models improve. Platforms come and go.