Llarod Hernaiz
AI + Automation consultant for growth systems.
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Built and optimized a high-volume Mailchimp automation funnel triggered by contact tags and entry conditions. The journey segments lead, send timed follow-ups (delays), and track performance to keep the sales pipeline moving with minimal manual work.
Highlights
17K+ emails sent with 40–48% open rates
Tag-based entry + conditions to control who enters the journey
Multi-step nurture flow with delays and performance monitoring
Designed for scalable lead follow-up and conversion
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Built an end-to-end lead capture and follow-up automation triggered by a Voiceflow chatbot. The workflow parses and formats user inputs, logs each lead to Google Sheets for tracking, sends an internal notification via Gmail, and syncs the contact into Mailchimp with custom fields for segmentation and automated nurturing. This system reduces manual data entry and ensures every lead gets captured, routed, and followed up consistently within minutes.
Highlights
Voiceflow trigger (Custom API Response) → workflow automation
Data parsing/formatting (split fields) for clean CRM-ready inputs
Lead logging to Google Sheets + internal email notification
Mailchimp contact creation with custom fields for tagging/segmentation
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Live demo of a bilingual website chatbot used for lead qualification and intake. The bot guides users through a simple flow, captures key details, and hands off the lead for automated follow-up (Voiceflow + automation stack).
(Spanish UI shown — English version available.)
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FastMind is a fasting tracker I built with product-grade logic — not a basic stopwatch. The app treats fasting as a structured workflow with multiple states (e.g., started, active, paused, ended), and persists time correctly across sessions. Users can log notes and mood states during a fast, creating a dataset that the AI layer can summarize into useful insights and patterns over time.
What makes this non-trivial
Reliable fasting time calculations + persistence (not just UI)
State-driven flow handling edge cases (pause/resume/end scenarios)
Notes + mood tracking tied to the fasting lifecycle
AI-generated summaries and insights based on user entries
Clean UX built on top of complex underlying logic
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