Seven years turning game-publishing data into decisions: cost per install cut by 80%, monthly revenue up 30%. Now I build the AI systems that do the same job — answers grounded in your own documents, software that does real work without going off-script, and a measured number behind every claim. Everything below is running, and you can click into it.
Available now, full time. Remote from Ho Chi Minh City — a full working day with Asia and Australia, and the whole European morning. Scoped builds, fixed-price audits, ongoing by the month.
Seven years of it: pipelines over terabytes a day, dashboards people actually open, and churn / LTV / segmentation models pointed at a number someone is accountable for.
Answers grounded in your own documents, a gate that refuses any claim the source does not support, and a test harness that catches a bad prompt change before your customers do.
AI doing real tasks under rules you write: anything forbidden stops and asks a person, work happens on a copy, and nothing goes live until someone has read the change.
Run cost is a design constraint, not an afterthought. Everything here runs on a subscription you already pay for or on models running locally — not a per-token bill that grows with your success. That is a measurement habit rather than a preference: benchmark the free option against the paid one and pick on the result. 8.8% word error rate, chosen voice. 79% → 93% retrieval accuracy, measured.
How it usually starts. A fixed-price week: I look at what you have, and you get a written finding at the end of it — what is worth building, what it would take, and what I would not build. That document is yours whether or not we go further.
Data Analyst Specialist at VNG Corporation, publishing mobile games into South East Asia. A layered warehouse on AWS Athena behind 30+ live titles, fed from attribution and game servers and read by the dashboards, weekly briefings and executive reports the business ran on. Machine learning for churn, LTV and segmentation, pointed at spend decisions rather than at a slide.
The situation. A game launch was acquiring players at about $5 per install. At that price the launch budget buys an audience too small to judge the title on, and every downstream number — retention, revenue per user, payback — is read off a cohort that is not worth reading.
What I did. Ran it as an experiment with a budget split rather than a campaign with an opinion. Two weeks on 20% of a $10,000 budget to test creative, channel and audience against cost per install; two weeks on the remaining 80% to confirm the winner held at volume rather than at sample size.
The result. Cost per install settled between $0.80 and $1.00 for the launch phase, roughly a fifth of where it started — the same money buying about five times the players, on a figure that had already survived a confirmation round.
The situation. Evaluating a game for acquisition meant assembling competitor benchmarks by hand. The review cycle ran for months, which is longer than the window in which a title is available to bid on.
What I did. Built a watcher over the shared request file the product teams already used: it picks up each new request, pulls the benchmark set for that title from Sensor Tower, and writes a finished workbook back. Debounced against sync lag, locked to one machine, with per-row state so a failed row is retried and a done row is never rebuilt.
The result. The review cycle fell from months to one or two weeks, and the benchmark set stopped varying with whoever assembled it. Product teams requested reviews directly instead of queueing.
Open the Game Analytics Console → The real console — 18 screens, PUM, RFM, marketing, audit — on invented data.
Or the daily report it produces → One morning's publishing report: spend, installs, cost per install and what moved.
Or the executive briefing → Funnel by channel, the campaigns dragging the average, and what retention, LTV and ROAS do after install.
Channel diagnostics, run on generated data: TikTok → Facebook → Google Ads → Why a channel buys installs that never register, traced to the setting that causes it.
A local control plane that runs one person's whole engineering loop. It is not a research tool with an agent bolted on: research is step one of five. The console crawls and reads, routes each finding to the project it belongs to, turns a goal into an approved plan, then hosts the coding agents itself — streaming their work, holding any call the project's written constitution forbids, and reviewing the diff before it merges. Eight active projects run under it — the two below among them.
A seven-tier applied-AI curriculum authored once and rendered into narrated video, illustrated comics and speech, in English and Vietnamese. Nothing is filmed: HTML and SVG become video, so a re-render costs nothing and never falls behind the source.
Open the studio → Four tabs of the production tool — the pipeline, the roadmap, the library, and the node canvas one episode is built on.
A scheduled pipeline that collects Vietnamese immigration news into a medallion warehouse, scores each item for relevance, and writes posts grounded in the retrieved article body rather than the headline.
Open the product → The real interface, running with no server — open the calendar, read a draft and its seed comments. One market, sample stories.
Or the walkthrough → Seven markets, the editorial rules, and what one generated post contains.