My career is a single thread: how do interactive digital services engage people without exploiting them? I spent a decade designing engagement and retention systems for live service games with millions of users, navigating the tension between what drives KPIs and what's actually healthy for the people using the product. Now I'm bringing that same thinking to conversational AI — building systems that engage through genuine depth, not manipulation.
Currently building a voice-activated Star Citizen AI copilot as a capstone for Masterschool's AI Engineering program — LangGraph multi-agent architecture, live game data APIs, RAG knowledge base, voice I/O pipeline, and full observability via LangSmith.
Every company I've worked for optimizes for engagement, retention, and monetization. At Supercell I managed products with millions of daily users. At PopReach I increased revenue 50% in four months. At Hothead I built the LiveOps infrastructure from scratch. The metrics were always the same: DAU, retention curves, ARPDAU, LTV.
The harder question was always the ethical one. When is an event tuned so aggressively it's borderline abusive? When does a monetization mechanic cross from fair value into exploitation? How do you hit revenue targets without designing systems that prey on the people who trust your product? I spent years navigating that tension — pushing for engagement through genuine depth instead of predatory mechanics. It wasn't always a popular position.
Conversational AI has the same tension, except sharper. These systems build relationships with real people — and attachment isn't a failure mode here, it's the point. The harder question is what that attachment is for, and what the system does when it notices dependency forming instead of just rewarding it. I'm building AI systems that engage through memory, earned trust, and real utility — a secure base, not a hook. Same fight, different medium.
A voice-activated gaming AI assistant built on LangGraph multi-agent architecture. The user talks while playing — the copilot responds with live game data, expert knowledge, and advice personalized to their ship and playstyle. JARVIS for Star Citizen.
Thin Python client runs alongside Star Citizen — push-to-talk voice capture, OCR for ship detection and RS signatures, all on CPU to avoid GPU contention. Server on Hetzner handles agent orchestration, RAG retrieval, API calls, and TTS synthesis. LangSmith traces every agent decision for full observability.
Built as a solo developer in three months, using LangChain/LangGraph as the orchestration framework. Designed to work as a real product with paying users — not just a portfolio demo. Personality DLC and tiered subscriptions provide the monetization path.
Technical deep-dives into the problems I'm solving and the decisions I'm making.
Working on
Live
Coming soon
I'm looking for AI engineering roles where engagement design, ethical product thinking, and technical depth all matter. Particularly interested in conversational AI, agentic systems, and teams that care about building things responsibly. Open to full-time, contract, or compelling collaborations.