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This example shows a CRM pipeline designed to track leads through every stage of the sales process. Paired with automated email sequences, it ensures leads are nurtured, tracked, and followed up with efficiently—all crucial for scaling service-based businesses.
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
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Maybe not the most talented in the room…
But quitting has never been an option.
One project at a time. One improvement at a time. Keep building. 💻📈
12:40am at the desk with "Normal is Boring" on the wall. That's a pretty clear statement on its own. Consistency compounds.
I joined this year's Config Makeathon and decided to build something inspired by a problem I've repeatedly encountered while working in CRM and lifecycle marketing.
CRM platforms are built around flows, campaigns, and automations. Customers are not.
When trying to understand what a customer actually experienced, I often found myself jumping between multiple journeys, templates, campaigns, and reports just to answer a simple question:
What happened, why did it happen, and what happens next?
So I built Trace.
Trace is a lifecycle debugging tool that helps teams:
• Replay customer journeys in a single timeline
• Understand why communications were triggered
• Simulate future customer experiences before launch
• Review every message in one place
• Identify journey conflicts and lifecycle issues before they impact customers
The idea came from real CRM consulting work. To bring it to life, I used ChatGPT to refine the concept, challenge assumptions, and iterate on the product structure, then used Figma Make to rapidly turn those ideas into a working prototype. After several rounds of prompting, testing, and revisions, the concept evolved into the MVP shown here.
The project was designed and prototyped entirely in Figma Make.
Would love to hear your thoughts!
🎥 Try the demo: https://tracedebug.figma.site/dashboard
🧩 Figma Community: https://www.figma.com/community/file/1647280144583934618
Project Title: AI-Driven Sales Intelligence & Workflow Automation
The Business Challenge:
Manual lead qualification and inconsistent follow-up processes created massive operational bottlenecks, leading to lost conversion opportunities and stalled revenue growth.
The Architectural Solution:
I architected a closed-loop automation framework to standardize the sales lifecycle. This solution features:
Real-time Intelligence: An AI-processing layer that dynamically extracts customer data and matches insurance policy profiles during live interactions.
Automated Conversion Pathways: A logic-driven branching model that differentiates between "Ready to Buy" signals and "Needs Time" triggers, ensuring zero lost leads through automated callbacks and personalized SMS sequences.
Systemic Optimization: A feedback loop that feeds interaction data back into the performance analytics engine to continuously refine recommendation accuracy and time efficiency.
The Impact:
Transformed fragmented sales interactions into a high-visibility, data-backed system, directly improving lead recovery rates and operational throughput.
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