I'm Ellison Yeung. I make things across strategy, content, and tech. Some of them are campaigns. Some are AI tools. Some are just me seeing if I can automate something before my attention span runs out. The good news is, a surprising amount of it actually works.
I grew up in the UK, went to the University of Leeds, and spent a few years working across digital learning, content production, and SEO at a consumer electronics company. That's where I started getting into how automation and AI could actually make marketing work better. Not in theory, but in practice.
In 2025 I moved to Hong Kong and joined Publicis Groupe as a management trainee. It's a rotational programme, so I'm getting hands-on time across media planning, client servicing, and campaign execution. On the side, I've been building tools that help people work faster and skip the repetitive stuff.
Management trainee at Publicis Groupe in Hong Kong. Rotating across departments, learning how media, creative, and strategy come together in practice. Building AI tools on the side.
Tracking what competitors are doing is useful strategic intel. But doing it manually means hours of Googling and copying data into spreadsheets.
A multi-agent automation workflow using n8n, Perplexity AI, OpenAI, and web scraping. It queries competitor events, scrapes relevant pages, uses AI to parse and structure the data into clean JSON, and outputs everything to Google Sheets.
What used to be a manual research task is now a single trigger. The workflow handles multiple brands in parallel.
Understanding how competitors run ads is genuinely useful for media planning. But pulling that information manually from Meta Ad Library is tedious.
An n8n workflow that connects to the Meta Ad Library API, scrapes ad creatives and targeting data, then pipes everything through OpenAI to summarize trends. Also built a chat agent layer for real-time queries.
Tested with a major brand and pulling full demographic and creative data. Designed to extend to TikTok, Google, and LinkedIn ad libraries.
Knowing what competitors post on LinkedIn, and how people respond, is useful for understanding positioning and audience sentiment.
A pipeline using Apify for LinkedIn scraping, n8n for orchestration, and OpenAI for summarization. Built a validation layer that normalizes data and falls back gracefully when parsing fails.
A repeatable system that turns scattered LinkedIn activity into organized competitive intel.
A five-agent framework where each agent has a specific role: competitor intelligence, content repurposing, client onboarding, insight dashboards, and lead research. Designed to be built in order.
Practical build order, realistic pricing, what to automate vs. keep human, and how the agents actually talk to each other.
A two-tab dashboard using Lovable, powered by Google Sheets. One tab analyzes competitor LinkedIn content; the other tracks competitor event participation. Both tabs have filters for date, competitor, content type, and full-text search.
Every AI insight references the specific posts or events that support it. Those references are clickable, opening a detail drawer with the full data.
Designed and implemented an SEO strategy across their website and YouTube. Keyword research, metadata optimization, content structuring, and internal linking.
Website traffic increased around 50%, video engagement went up significantly, and the content system gave them a repeatable process.
I like building interactive things — games, XR experiences, 3D scans, generative visuals. It's how I learn new tech. My university final project was an immersive XR memory experience built in Unity, and I'm always experimenting with what's possible in the browser.
I don't think of myself as just a "media person" or just a "creative person." I'm interested in the whole picture. How an idea starts, how it gets shaped, how it reaches people, and whether it actually lands.
What I keep coming back to is this: the best work happens when you close the gap between thinking and doing.
I'm less interested in ideas that sound good and more interested in ideas that work. Can this become a tool? A system someone actually uses?
I use AI every day. Not because it's trendy, but because it genuinely makes my work better.
Good strategy starts with paying attention. What do people actually care about?
I'd rather build something that works ten times than do the same thing manually ten times.
Before I build anything, I run through: what triggers it, how does the data move, what actions need to happen, where do we need AI or APIs, and what integrations are involved.
I'm always open to interesting conversations, whether it's about work, ideas, or something you're building.