Marketing Operations, CRM & Project Delivery Consultant
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Marketing Operations, CRM & Project Delivery Consultant
Illustration, Brand / Graphic Design
Creative Director at Serafinne
Creative Director at Serafinne
This campaign targets students heading back to school by showcasing the tablets in a fun and engaging way, highlighting its practical benefits for the classroom.
Esta campaña se dirige a los estudiantes que regresan a clases, presentando los Tablets de manera divertoda destacando su utilidad en el aula.
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Awera was created for a surfing resort on the remote tropical island of Mentawai, Indonesia. The brand applications reflect a tranquil, cozy environment for solo travelers.
Esta identidad es para un resort de surf en la remota isla tropical de Mentawai, Indonesia. Refleja un ambiente tranquilo y acogedor, ideal para viajeros solitarios.
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Turning content into Pinterest traffic for coaches & podcast
New to Contra
Turning content into Pinterest traffic for coaches & podcast
Hoy hice clic en cada pin publicado. Uno no llevaba a ningún lado.
Parte de mi rutina semanal como Pinterest Manager es revisar los enlaces: abro cada pin publicado y hago clic.
Hoy, 7 de 8 pines estaban perfectos. Abrían el artículo correcto, tenían el tracking y la página cargaba rápido.
Pero uno se había publicado sin enlace de destino. Se veía bien y estaba recibiendo vistas, pero quien hacía clic no llegaba a ningún lado. Todo ese interés, cero tráfico.
Pasa más seguido de lo que crees: se queda un campo vacío al programar, el pin sale publicado y nadie lo nota porque se ve bien.
Si usas Pinterest para tu podcast o tu negocio, haz esto una vez al mes:
✅ Haz clic en cada pin de los últimos 30 días
✅ Revisa que abra la página correcta
✅ Busca el tracking en la URL (utm_source=pinterest)
Un pin sin enlace es solo una imagen bonita. 📌
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Lo que 20 años en atención al cliente me enseñaron y que
ningún curso de marketing enseña:
Que un cliente no quiere solo resultados. Quiere saber qué
está pasando sin tener que preguntar.
Por eso trabajo 100% asíncrono, con actualizaciones claras y
por escrito. Tú grabas tu podcast; yo me encargo de que tus episodios sigan
trayendo oyentes desde Pinterest, sin reuniones ni seguimientos de tu parte.
Empezar como freelance a los 50 no significó empezar de
cero. Significó usar todo lo que ya sabía de otra manera.
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"A los 50 ya es tarde para empezar algo nuevo."
Lo escuché más de una vez. Y este año decidí ignorarlo.
Tengo 50 años, más de 20 en atención al cliente, y hoy
trabajo como freelance ayudando a podcasters a convertir sus episodios en
tráfico desde Pinterest.
No empecé sabiendo todo. Empecé sabiendo lo más importante:
cómo escuchar a un cliente, cumplir lo que prometo y comunicarme con claridad.
Eso no lo da la edad. Pero tampoco te lo quita.
Si tienes un podcast y sientes que cada episodio desaparece
a la semana de publicarlo, hablemos. Ese es justo el problema que resuelvo.
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Audited my own Upwork profile today instead of someone else's for once.
Found a title that didn't fit the character limit, a portfolio piece missing its best-matching tags, and a service promise that didn't match my real numbers.
None of it was obvious. All of it mattered.
Worth doing this more than once a year.
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Founder & AI Systems Architect
Project: Dragon Vision — Agent-Built AR Ingredient Scanning Effect
Instagram Reel:
https://www.instagram.com/reel/DeYMiNXsRBz/
invideo Project Link:
https://invideo.io/share/Export-10-Oct-25thLT4QVMM
Primary Effect: Custom AR-style scanning effect using AI-generated visual layers, scanning highlights, and animated information overlays.
Process Note:
I created Dragon Vision using invideo Editor's AI editing agents. Starting with AI-generated source footage, I directed the agent to build an AR-style ingredient scanning effect with animated overlays, scanning lines, and information panels. I refined the timing, opacity, and animation of the layers through the editing workflow. My screen recording demonstrates the agent workflow, editable layers, refinements, and my voice commentary.
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Enterprise Multimodal AI Platforms (NDA)
Designed and developed two production-grade enterprise AI platforms under NDA, taking ownership of the complete AI lifecycle from research and system architecture to production deployment and long-term platform support. The platforms integrated LLMs, RAG, Computer Vision, OCR, intelligent document processing, AI agents, custom neural networks, benchmarking, model evaluation, dataset engineering, synthetic data generation, model training, fine-tuning, optimization, and scalable production inference pipelines.
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Horse Health AI is a Golden Dragon AI DeepTech project for portable, non-invasive, multimodal animal health screening and longitudinal health intelligence.
I designed the system around a compact multimodal sensor architecture combining RGB Computer Vision, multi-wavelength NIR, radiometric thermal imaging, structured/polarized optical sensing, and motion and geometry data.
Rather than analyzing each signal independently, the AI architecture is designed to fuse complementary modalities and evaluate visible anatomy, gait and behavior, thermal patterns, vascular and perfusion-related features, tissue-related optical signals, movement, and individual history.
A key part of the architecture is the Individual Digital Baseline. Each animal can become its own longitudinal reference, allowing new scans to be compared not only with validated population-level knowledge but also with that animal's historical normal patterns.
The software architecture includes synchronized acquisition, calibration, multimodal registration, per-modality AI models, longitudinal baseline modeling, multimodal embeddings and fusion, veterinary knowledge/RAG, explainable anomaly localization, confidence and uncertainty assessment, and mobile and research interfaces.
The project is designed as a screening and decision-support system, not as a replacement for veterinary diagnosis. Veterinary expertise, clinically confirmed data and validation are explicit parts of the development roadmap.
The initial platform is focused on horses, with potential expansion to other animal species following species-specific validation.
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Food as Healing AI is a personal AI nutrition and food intelligence platform designed around the individual.
The platform builds a persistent Personal Nutrition Profile from the user's goals, food habits, lifestyle, preferences, restrictions, ingredient policies, practical constraints, feedback, and optional user-provided laboratory context.
Its food intelligence pipeline uses Vision/OCR, ingredient extraction and normalization, an ingredient and additive knowledge base, RAG, and structured product analysis to help users understand what is actually represented in a food product.
A Personal Matching Engine is designed to connect structured product information with each user's individual food policy — including hard exclusions, preferences, dietary patterns, nutrition goals, clean-label requirements, and ingredient rules.
The broader architecture is modular and multi-agent. Specialized agents are designed for personal profiles, questionnaires, nutrition, ingredients, products, matching, shopping, meal planning, knowledge retrieval, and continuous feedback.
The current technical foundation includes a Google Cloud backend, Supabase, RAG, prompt/AI orchestration, external AI model integration, an ingredient/additive knowledge base, and an image-based ingredient analysis prototype.
The goal is not to provide another generic diet app or diagnostic system, but to create a personal AI food consultant that learns how an individual wants to eat and turns that profile into explainable everyday food decisions.
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Professional Video Producer and Editor
Professional Video Producer and Editor
