Super Cat Technology · Hong Kong & London
Rent a top-1% data scientist. Or a team of them.
Senior AI engineers join your team, work in your repositories and take AI from pilot to production, then stay to run it or hand it over.
Cat Yung · Founder, leads every engagement
- Upwork Expert-Vetted
- Top 1%
- Job Success Score
- 100%
- Projects delivered
- 100+
- Two bases, two time zones
- HK · LDN
Backed by and trusted by
Who you work with
Hi, I'm Cat.
I am an AI engineer based in Hong Kong, and I lead every Super Cat engagement myself. My background is data science, and for the last few years my work has been taking AI out of the demo and into production for companies that cannot afford it to be wrong.
That has meant a compliance chatbot running on a Big 4 firm’s own GPUs, voice agents answering 200,000 calls a month, and more than 100 paid builds in between. When you book a call, you speak with me, and I stay on the work until it is running.
- 100% Job Success Score
- US$200K+ billed on Upwork
- 100+ paid builds
01 / Where AI gets stuck
Where enterprise AI gets stuck.
Most enterprise AI work stalls in the same few places. These are the problems we fix, each backed by systems running in production today.
Pilots that never reach production
Engineers who have shipped production AI join your team and take the pilot live, with evaluation, monitoring and a human fallback.
Forward-deployed engineers →02Knowledge locked in documents
Answers drawn from your own policies, contracts and reports, each one cited to its source, on your own infrastructure if required.
RAG case studies →03Customer contact that does not scale
Voice and chat agents that handle routine requests around the clock and hand over to your team with the context written down.
AI agent case studies →04Data only specialists can query
Business users ask questions in plain English and get exact, explainable answers from your databases and systems.
Data case studies →05Manual work between systems
Agents take over repetitive back-office steps, with approvals and an audit trail so they pass your security review.
Secure AI agents →06AI without an owner
A fractional Chief AI Officer sets priorities, decides what to build, buy or skip, and is accountable for the result.
Fractional CAIO →Something else? The discovery sprint starts there.
02 / Case studies
Production AI that shipped and stayed up.
Big 4 client · fully on-premise
Enterprise RAG Chatbot for a Big 4 Accounting Firm
Multimodal retrieval over a Big 4 firm’s full compliance library, tuned for citation instead of summary — every answer arrives with its source paragraph attached.
200k calls/month · 67% booking rate vs 77% human
24/7 AI Agent Call Center: 200k Calls a Month Across 60 US HVAC Brands
Voice agents orchestrating 200,000 calls a month across 60 US HVAC brands for a 5,000-person enterprise, booking 67% of calls against 77% for human agents — within 10 points of a trained team, around the clock. Built as a product, an AI front desk that answers, books and routes, not a pilot.
Same question, same SQL, same answer
Deterministic Natural-Language Search for a Private-Markets Data Platform
Deal teams ask “industrials in the southwest received this year with LTV under 70%” and get an exact, explainable table — the model plans the query, but never writes free-form SQL.
Ask in Slack, answered from Confluence
Slack Knowledge Assistant over Confluence for an EdTech Company
An assistant inside Slack that answers staff questions from the company’s Confluence — routed by an agent that decides, per question, whether to search the wiki, the web, or just answer.
03 / Engagement model
From discovery to production in three phases.
- 015 days
Discovery sprint
We map the workflow, the data and the constraints with your team, prototype the riskiest part, and score every candidate use case by value, effort and risk.
- 022–8 weeks · time & materials
Embedded delivery
Engineers join your stand-ups and ship to your repositories behind your security review, with an evaluation suite from the first week.
- 03Optional retainer
Run it, or own it
We operate and monitor the system on a monthly retainer, or hand over the code, the evaluation suite and the runbook to your team.
Example: what a discovery sprint produces
An opportunity map for a fictional asset manager. Every candidate is scored, and the ones that should wait are marked as clearly as the ones to build.
| Use case | Estimated value | Build | Risk | Recommendation |
|---|---|---|---|---|
| Cited search over research notes and policy | ~25 analyst-hrs/week | 4 weeks | Low | Build now |
| Plain-English questions on the deal database | ~15 hrs/week | 3 weeks | Low | Build now |
| Draft investor letters from portfolio data | ~3 days/quarter | 3 weeks | Medium | Next |
| Triage the shared investor inbox | ~8 hrs/week | 2 weeks | Low | Next |
| Fine-tune an in-house model | Unproven | 10+ weeks | High | Not yet |
04 / Client reviews
Verified reviews from Upwork.
Great and effective communication, extremely smart and attentive to the intrinsic details. If you're looking for professional level assessment and review, Cat's the one.
Upwork client
It's been great working with Cat! She has helped us develop some awesome Looker Studio reports and I am looking forward to working with her on future projects that we have.
Upwork client
Cat is incredible. She has a really impressive range of data science and ML skills, is able to respond to work requests on short notice, creates excellent time estimates and timelines, and has consistently delivered on time. I'm hiring her again for a new project. I would recommend her very highly to anyone looking for an expert data scientist.
Upwork client
Cat is a great NLP engineer and I would want to work with her again on various other NLP projects. She was diligent in her work, suggested ideas and carefully created a model that I could use as a MVP version.
Upwork client
05 / FAQ
Common questions
What does Super Cat Technology do?
Super Cat rents out vetted, top-1% AI and data scientists who embed in your enterprise on a time-and-materials basis — senior talent that ships production AI (NLP, LLM/RAG, agents, analytics), not fixed-scope MVPs.
How is this different from hiring full-time or using a freelance marketplace?
You get senior, production-proven people in days instead of months, with the accountability of a firm rather than a marketplace profile. The team embeds in your workflow like employees, and you scale the engagement up or down as the work demands.
What proof does the team have?
Super Cat founder Cat Yung is Expert-Vetted on Upwork, top 1% platform-wide, with a 100% Job Success Score (source: Upwork profile, October 2026). The team has shipped production AI at a 5,000-employee US enterprise and a Big 4 firm, and builds its own product, Super Chain, for family and investment offices.
How does an engagement start?
With a 5-day discovery sprint. We map the workflow, the data and the constraints with your team, prototype the riskiest part, and score every candidate use case by value, effort and risk, so you have a prioritised plan before any longer build starts.
How long does it take to get an AI system into production?
Typically 2 to 8 weeks of embedded delivery after the discovery sprint, depending on scope. Engineers ship in your repositories behind your security review, with an evaluation suite from the first week.
Can the AI run on our own infrastructure?
Yes. For a Big 4 firm we built a fully on-premise RAG system, fine-tuning Phi-3, Llama and Jina embedding models on the client’s own H100 GPU cluster, so confidential documents never left their environment.
Where does Super Cat operate?
Hong Kong and London, with remote embedding into enterprise teams worldwide.
Longer answers live in the guides.
First-Sprint Data Scientist
A vetted engineer embedded in your team, a shipped deliverable by end of sprint 1.
Pick a time below and you will speak with me directly. Bring the use case, the data and the constraints, and I will give you a straight view on feasibility, effort and the first thing to ship.
Book a callCat Yung
Hong Kong & London time zones
Book a call · 30 min with Cat Yung