*This is a submission for Weekend Challenge: Generosity Edition
What I Built
Small online social enterprises and cause-driven brands often lose significant revenue from cart abandonment due to a lack of automated, personalized follow-up infrastructure. Manual outreach is too slow, while basic automated emails lack context and reasoning.
I built an Agentic E-Commerce Workflow System that runs an autonomous agent loop to detect abandoned carts, segment high-value customer interactions, analyze past campaign performance from memory, and execute context-aware recovery messaging.
The intended goal is to give small businesses and ethical brands enterprise-level recovery tools without high manual overhead.
Demo
Code
View the code and system repository on GitHub:
https://github.com/rcortez056-spec/ramon-ai-systems
How I Built It
The system is powered by a 4-part agentic feedback loop displayed live in the interface:
Planner (Task Decomposition): Scans cart state, identifies high-value priority tiers (e.g., carts > $150), and builds execution plans.
Tools (Execution Layer): Runs data segmentation dynamically across user records and top product categories.
Memory (Context Store): Recalls historical campaign performance metrics (e.g., prior 22% recapture rates with specific discount playbooks) to inform current actions.
Output (Result Delivery): Generates and routes personalized recovery messaging to target customers automatically.
By leveraging low-code agentic design, cause-driven merchants can automatically convert lost intent into completed support with zero manual triage.
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