01
NOBODY NAMES THE TASK
The team cannot say which piece of work should change first, or who owns the change. Ambition stays abstract, so nothing ships.
AI ADOPTION measured in REAL WORK
We help Hong Kong teams put AI into one workflow they already run, measure the difference, and keep what works. With a private, on‑premise path for data that cannot leave the building.
BOOK A WORKING SESSIONWORKING WITH TEAMS ACROSS HONG KONG
PILOTS THAT STALL
Proofs of concept impress in the demo, then never reach the daily work
/ THE PROBLEM
YOU BOUGHT THE TOOLS. adoption didn’t follow.
Most teams do not need another license or another training day. They need one task done a better way, clear rules for data, and proof that the new way beats the old one. Four things usually stand in the way.
02
NOBODY MEASURED THE OLD WAY
No one wrote down how long the work took before. So no one can show what got better, and the budget dies at the next review.
03
THE DATA RULES ARE UNCLEAR
Nobody says what may go into which tool. People paste too much into public models, or avoid AI completely. Both cost you.
04
THE PILOT NEVER LANDS
A demo works in a sandbox and stalls before production. The gap between a pilot and daily use is where most AI budgets go to die.
That is why we start small: one task, the team that owns it, and a number to beat.
/ THE APPROACH
ONE TASK at a time.
Every engagement runs the same way, from a two‑week first deployment to a private setup. Plain steps, a fixed price, and a written verdict at the end.
DAYS 1–2
PICK THE TASK
Choose one task where a better way matters. Agree who uses AI, what data is off limits, and the number to beat.
DAYS 3–8
RUN IT WITH THE TEAM
Set up the tools and the rules, then work on live cases together until the new way holds without us in the room.
DAYS 9–10
MEASURE AND DECIDE
Put the new numbers next to the old ones and agree what happens next: expand, revise, or stop. If the case is weak, we will recommend stopping.
You keep the working setup, the rules, the training, and a written recommendation. THE BASELINE DECIDES, NOT THE DEMO.
/ PRIVATE AI
FOR WORK THAT cannot leave THE BUILDING
Most teams only need approved accounts and clear rules. When the data demands more, we add a gateway you control, or a model on hardware you own. Use the level of control the work actually requires.
FOR TEAMS THAT NEED APPROVED ACCESS FAST
Approved tools
Accounts in your name, written rules for safe use, staff training, and a plan for daily work.
FOR TEAMS THAT NEED MASKING AND LOGS
A gateway you control
Your team reaches external models through one gate in your cloud account. It masks sensitive data, logs use, and caps spend.
FOR WORK THAT MUST STAY ON‑SITE
A model on your hardware
A private model on machines you own, tested on your real workload, with a written runbook for your team.
You keep the accounts, the keys, the logs, and the hardware. You can remove our access at any time.
/ PRICING
FIXED PRICES, published here.
Almost nobody in this market prints a real price. We do. The fee is agreed before work begins, in writing. Software, cloud services, and hardware are billed separately unless the proposal says otherwise.
MOST TEAMS START HERE
First Deployment
From HK$120,000
TWO WEEKS · ONE TEAM, ONE TASK
- Baseline measured before anything changes
- Tools and data rules set up with the team
- Live cases run together until the new way holds
- Written verdict: expand, revise, or stop
AFTER PROOF
Embedded Adoption
From HK$320,000
8–12 WEEKS · ONE TEAM
- We stay until the new way is routine
- Continuous improvement on live work
- Training for the people who do the task
- Full handover, documented
PRIVATE PATH
Private AI
Setup: fixed HK$98,000
4–5 WEEKS · GATEWAY FROM HK$19,500/MO
- Approved accounts and written data rules
- Optional gateway: masking, logs, spend caps
- On‑premise model, quoted for your site
- You hold the keys; our access is revocable
/ ABOUT
THE PERSON YOU MEET is the person who delivers.
No software to sell. No quota to hit. No handoff to a junior team after the pitch. The practice is led by Johannes Janousek: previously a data scientist at Massar Capital, an award‑winning New York macro hedge fund, with an MSc in Data Science from King’s College London and startup experience in Berlin, New York, and Hong Kong.
We work directly with your team, write everything down, and set up only what makes sense for your business. We run our own work the same way we advise clients to run theirs.
- MASSAR CAPITAL, NEW YORK
- Data scientist at a macro hedge fund
- KING’S COLLEGE LONDON
- MSc Data Science
- BERLIN · NEW YORK · HONG KONG
- Startup and buy‑side experience
- DAILY TOOLS
- Claude, Codex, and Python
/ FAQ
ASKED FIRST, answered plainly.
Who is this for?
Teams whose work is analysis, documents, operations, code, or client service. Not only engineers, and not only large companies.
How do we know it works before we spend more?
Start with the First Deployment. It is a small test on your own work, measured against your old numbers. If the results are weak, we will say stop. The first deployment is the decision instrument, not the commitment.
How do you handle data and security?
We agree the rules in writing before we start: which tools are approved, what may go into a model, and who reviews the output. If your policy asks for more, the private AI options add masking, logs, and on‑site models.
What happens after the first deployment?
If the numbers hold, most teams expand to a second task or move to Embedded Adoption, where we stay until the new way is routine and then hand everything over. Launching is not the finish line; sticking is.
Do you work in person or remotely?
Both. We are based in Hong Kong and work on site with local teams. For teams elsewhere, we do the same work remotely.
/ CONTACT
TELL US ABOUT the team and the task.
A short note is enough: what the team does and what you want to change. Kept confidential; no sensitive data is needed at this stage.