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One of the most valuable components of this project was the AI Qualification & CRM Synchronization Engine.
Instead of pushing every incoming lead directly into the CRM, the system first evaluates and enriches lead data using AI.
The workflow automatically:
• Captures incoming leads
• Generates AI qualification summaries
• Assigns lead scores based on available data
• Identifies lead intent and priority
• Filters and routes qualified opportunities
• Synchronizes records into HubSpot CRM
• Creates associated contacts, companies, and deals
This ensures sales teams work with enriched, actionable opportunities rather than raw lead data.
The result is a cleaner CRM, faster lead prioritization, reduced manual review, and more efficient pipeline management.
Tech Stack: HubSpot CRM • Make.com • Airtable • Groq AI
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
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Building smarter backend systems for businesses using GoHighLevel.
Recently worked on setting up workflow automations, pipeline tracking, lead management, and business growth systems designed to help businesses operate more efficiently behind the scenes.
I’m becoming increasingly interested in the intersection between:
• CRM systems
• automations
• customer journeys
• AI workflows
• modern frontend experiences
A lot of businesses focus only on the website frontend, but the real growth often happens through the backend systems powering follow-ups, lead nurturing, and operations.
Currently exploring more advanced workflows involving automations, onboarding systems, customer segmentation, and AI-assisted processes.Just built this for a cleaning business
How We Map Repeated Business Work Before Building Automation
A service agency came to us with a familiar problem: their team was manually chasing clients for updates, following up on internal tasks, and losing hours every week to the same repetitive loop.
Most people jump straight to "automate it." We don't.
Step 1: Map the mess first.
We shadowed the actual workflow. Who sends what, when, and why. Turns out, 60% of the follow-ups existed because there was no shared visibility into task status. People were pinging each other just to figure out where things stood.
Step 2: Fix the process before adding tech.
We restructured the handoff points. Clear ownership at each stage. One source of truth for task status. No more "just checking in" messages clogging up Slack.
Step 3: Layer automation on top of a clean process.
Only after the workflow made sense on paper did we wire up automated status updates, reminders, and escalation triggers. The automation didn't replace the process. It enforced it.
The result:
Fewer manual follow-ups. Faster handoffs. The team stopped chasing and started executing. The client said it felt like they "hired two extra people" without adding headcount.
The takeaway: Automation built on a broken process just breaks faster. Map it first. Clean it up. Then automate what's left.
AI Voice Calling Agent — Vapi + Make.com + Google Sheets Built an AI-powered inbound voice agent named Shreeja for KyrosAura, a wealth advisory firm. The agent answers calls from the website, qualifies leads by confirming a minimum 50 Lakh investment mandate, collects caller name, phone number, and preferred meeting time, then automatically logs the booking to Google Sheets via Make.com. Supports Hindi and English detection automatically. Built on Vapi, Claude Haiku, ElevenLabs, and Deepgram.
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