Privacy-Preserving AI (PPAI) Cloud Infrastructure
We provide a machine learning decision process cloud infrastructure foundation to turn your data into an actionable intelligence. Your decision process within your organization becomes measurable, viewable, tunable, and auditable with AI then a final human decision process at the end.
Cloud architecture
Data engineering, model training, and inference — built end-to-end on AWS or Google. Data streams in from thousands of sources, is transformed at scale, trains GPU models, and serves live, low-latency inference.
An autonomous ML decision system running in the hardest arena there is — adversarial, real-time, real money. See it live at luckma.io ↗
What you can try today: the decision process, the DPIA-alignment story, and a founder to talk to. Sold to any team making high-stakes calls — finance, credit, fraud, health.
The runtime · interactive
What am I looking at? This is the five-step decision process every LuckMa decision runs through — Observe → Ask → Analyze → Decide → Act. Click a stage to see what it does. Trading is our first adapter; the process itself is domain-agnostic.
Ingest and normalize any signal — prices, events, records — into a single timestamped view, with bad-input rejection built in. Garbage never reaches the decision.
The reach
A new industry is new Observe (their data) and Act (their systems) around the identical process.
| Stage | Trading built | Credit | Fraud | Clinical ops |
|---|---|---|---|---|
| Observe | market data | applicant + bureau | transaction stream | vitals + history |
| Ask | tradable moment? | complete file? | anomalous? | triage moment? |
| Analyze | P(win), E[R] | default probability | fraud score | deterioration risk |
| Decide | E[R] > hurdle | return clears risk | cost-weighted risk | risk × cost of miss |
| Act | place / hold | issue / decline | block / allow | escalate / monitor |
Compliance by construction
A Data Protection Impact Assessment (GDPR Article 35) is triggered by exactly what LuckMa enables — automated decision-making with significant effects. The engine emits the evidence your assessment needs, by construction.
What Sets Us Apart
Transparent Decisions
Every call is journaled with its reasoning. Audit by construction. No black boxes.
ML + Rules Hybrid
Start with rules. Swap in ML when it proves itself. Mix and match enablers.
Audit-by-Construction
Give reviewers full decision provenance before you go live. Nothing to reconstruct after the fact.
Easy Integration
REST API, local binary, or WebSocket. Integrate in hours, not weeks.
The Platform - decision process cloud infrastructure
Companies already have ML models. What they lack is decision process infrastructure as code (IaC) — to deploy them safely against incumbent rules, prove trust, and audit every call. Your code stays yours; we provide the cloud infrastructure underneath.
What deployment looks like
The numbers below are modeled illustrations of how the platform is used — not audited client results. Real, reproducible replay sessions are in the section.
Illustrative Scenario: Momentum Strategy Automation
Illustrative: a fund deploys momentum rules through LuckMa's decision abstraction layer — strict entry gates plus confidence scoring cut false signals, and every decision ships with the audit evidence a compliance review needs.
Illustrative Scenario: Credit Scoring Deployment
Illustrative: a credit union migrates from rule-based underwriting to an ML model via LuckMa's shadow → adoption gate → autonomous flow — every decision auditable, with FCRA/GDPR-grade explainability evidence for the compliance team.
Integration
Example: Get a decision
POST /v1/decisions
Content-Type: application/json
{
"observation": {
"open": 100.5,
"high": 102.3,
"low": 100.2,
"close": 101.8,
"volume": 1250000,
"timestamp": "2026-07-18T14:30:00Z"
},
"model": "momentum-hybrid-v2"
}
→ Response
{
"decision": "BUY",
"confidence": 0.87,
"rationale": "Momentum quality 0.82 (>0.20 threshold), 3-candle streak HH structure, SMA above",
"entry_target": 103.2,
"stop_price": 99.8,
"risk_reward": 3.4
}
Full REST + gRPC documentation available. SDKs: Python, Go, Node.js. See Architecture section for the integration stack.
What a decision looks like
Mock data, shown for illustration — this is the shape of every journaled decision: inputs, call, confidence, the rationale it recorded, and the entry gates it cleared.
Cloud infrastructure
GPU-enabled AWS EC2 with Nvidia GPUs and deep-learning AMIs — engineered for scalability, redundancy, and security, with latency kept low.
GPU compute
The core operations of deep learning parallelize beautifully — and GPUs have far more cores than CPUs. On our benchmarks GPU training runs 4–5× faster than CPU. GPUs are accessible directly inside EC2 p/g instances via AWS deep-learning AMIs.
Docker · SageMaker · IaC
Every environment is code. SageMaker pulls a Docker image from AWS ECR, trains on EC2, and uploads model artifacts to S3 — the whole pipeline versioned and repeatable, never a hand-configured server.
CUDA · nvidia-docker
Nvidia CUDA provides the GPU-accelerated libraries deep-learning frameworks depend on — and TensorFlow pins a specific CUDA version, so the stack has to match exactly. Our Dockerized inference pipeline integrates nvidia-docker for GPU pass-through of the CUDA driver between host and container, so we train and serve on GPU inside containers that stay agnostic to the host OS.
Redundant, scalable EC2 fleets.
Inference co-located with the GPU.
Encrypted in transit and at rest, least-privilege access.
An asynchronous ingestion engine receives and processes data in parallel — nothing waits in line.
Batch processing in a multi-threaded context so training data is prepared at scale, not one row at a time.
Master/worker clusters and a PySpark context transform feature vectors — over 500% faster than the naïve path in our benchmarks.
Feature extraction that leans on vectorized operations — up to 100× over a Pandas apply in our tests.
GPU-enabled EC2 (p/g) with Nvidia GPUs and Pipe-mode streaming for robust, high-throughput training.
TensorFlow Serving GPU-compiled with Bazel, invoked through AWS Lambda over gRPC/REST for low-latency inference.
Data security
Before training data ever hits the wire, we apply proprietary encryption — so sensitive inputs are protected in transit and at rest, without giving up the ability to compute on them.
Founder · Visionary Software Architect
心 (Sim) - Mind, 自 (Ja) - Self, 魂 (Hon) - Soul
A decade architecting large-scale systems at Google, Salesforce, eBay, and Yahoo. Now, engineering a decision process system with cloud and AI
"The decision process isn't a metaphor for me — it's how I've navigated my whole life: act well when the information is incomplete and the cost of error is real."
Ja Sim came to the U.S. from South Korea at fifteen without a word of English. Through teachers, family, internships, and relentless self-teaching, he built a path through computer science, software architecture, and AI — shaped by a lifelong instinct for reading a situation with insufficient information and acting anyway. As a principal engineer in Silicon Valley he designed distributed systems at global scale; that same discipline now underpins LuckMa's decision platform.
A #1-ranked pro gamer's edge is the same as a trading system's: perceive fast, weigh the evidence, commit under pressure, adapt. At seven he beat Japanese RPGs he couldn't read — purely by inference and trial-and-error. That intuition for acting well without complete information is the thread from his biography to luckma.ai.
"Poker is live execution of decision process involving many different factors to make correct decisions during multiple stages of a hand - preflop, flop, turn and river while interacting with 7-8 opponents at the table."
Jae has been playing poker for over 15 years. Building his bankroll from $100 Pokerstars deposit back in 2008. He funded his own college tuition from his earnings. Got qualified to play the main event in 2008 but chose to play $2000 wsop side event where he met Daniel Negreanu, and paid his college tuition with his $12500 package win. Between 2023-2026, he has earned over $300000 in live cash games averaging about $150/hr.
Contact
It's founder-led — Ja Sim replies within one business day.
Machine learning
Deep neural networks trained on time-series big data — learning not just to predict, but to decide. Supervised where we have labels, self-supervised and reinforcement-learned where the market is the only teacher.
"When I saw move 37, I was proven wrong. AlphaGo is indeed capable of making creative decisions — an artistic move that resembles the beauty of Go."
— Lee Sedol, Go World Champion (9 Dan)
In game two of the 2016 match, AlphaGo played Move 37 — a fifth-line shoulder hit its own training said a human would play with probability about 1 in 10,000. Commentators called it a mistake; fifty moves later it had quietly organized the whole board, and it won the game. Three games later Lee Sedol answered with Move 78 — a wedge so improbable it carried the same 1-in-10,000 odds, broke AlphaGo's evaluation, and won humanity its only game of the match. The pair of moves is the whole lesson: a well-calibrated machine can find value far outside human convention, and a human under pressure can still find the move the machine can't see. Neither is enough alone — which is why LuckMa pairs calibrated machine confidence with human oversight, and journals the rationale AlphaGo famously couldn't give.
We structure data nodes sequentially along a temporal series — recurrent networks that read the market the way it actually unfolds.
Agents take actions in an environment to maximize cumulative reward — the natural frame for a system that decides, not just forecasts.
Where outcomes exist, we learn from them directly; where they don't, the engine's own structure becomes a free, noise-tolerant label.
Managing many models on big data doesn't scale by hand. We automate hyperparameter search and apply orthogonal optimization — tune one axis at a time (fit training, then validation, then holdout, then the real world), so each knob has a clear job. Naïve early-stopping isn't orthogonal; it trades training fit for validation fit at once, and hides the cause.
Resources
ARTICLE
Why Black-Box Models Fail in Finance
The smartest models mean nothing without audit trails. Learn how opacity kills adoption and how LuckMa's transparent process builds trust.
Read more →ARTICLE
Decision Discipline: The Edge Over AI Alone
The Observe → Analyze → Decide → Act process is older than AI. We just made it auditable. Discipline beats model horsepower.
Read more →ARTICLE
Backtesting Realism: Friction & Slippage
Backtest metrics matter only if they match live execution. We measure the real cost: commission, slippage, intrabar ordering, and time zones.
Read more →ARTICLE
Integrating ML Without Losing Signal
Adding machine learning to a trading system is harder than it sounds. We avoid the six pitfalls that kill most ML projects.
Read more →Common Questions
Black boxes are unauditable by design. LuckMa's decision process is transparent at every stage: you see the observations, the analysis, the confidence score, and the rationale. Every decision is journaled, so your compliance team — and any reviewer — can inspect exactly how each automated decision was made.
Our engine is data-agnostic. If you have OHLCV data, we can run decision logic on it. It supports equities, futures, crypto, and forex data out of the box, and custom instruments and feeds can be added in days.
We're a decision abstraction layer, not a strategy. Performance depends on what logic you feed us — rules, ML models, or hybrids — so we don't advertise performance numbers. Instead we publish reproducible replay sessions in the Demo section: every trade, every decision, and the reasoning behind it, inspectable end to end.
We produce the evidence compliance requires. Every decision is journaled: rationale, confidence, data inputs, and outcomes. That audit trail maps directly to DPIA requirements — data lineage, GDPR Article 22 explainability, safeguards, and ongoing monitoring — so your data controller's assessment is defensible. Regulatory approval is always your process; LuckMa's job is making the evidence for it automatic.
Explainability, interpretability & transparency
The explanation is generated with the decision — rationale, calibrated confidence, exact inputs. Not forensics after the fact.
Every decision carries a human-readable reason — Article 22's "right to explanation," by construction.
Scores that actually rank outcomes — reviewers know when the system is unsure.
One model version per decision stamp — reproduce any past decision exactly.
Decision Process as a Service (DPaaS) Demo
Financial Technology AI, utilizes latest machine learning technologies to develop Fintech pattern classification software, algorithms to help you make better trading decisions.
Privacy-Preserving AI (PPAI) architecture
Train, infer, and decide without exposing PII. PPAI layers complementary privacy-enhancing technologies (PETs) across storage, compute, and outputs.
The four building blocks
Only model updates move — raw data never leaves its silo. Roadmap.
Proven ε-budget noise neutralizes re-identification. Roadmap.
Compute on encrypted data — highest privacy, highest cost. Roadmap.
Hardware enclaves, <5% overhead. Nearest-term on our AWS/GCP deploys.
Encryption in transit/at rest, region-selectable residency, zero-trust posture, full journaling.
Edge ingestion → FL + DP-SGD training → TEE aggregation → encrypted inference.
| Technology | Privacy level | Overhead | Accuracy impact |
|---|---|---|---|
| Federated Learning | Medium (gradient leaks if unencrypted) | Low–Medium | Minimal |
| Differential Privacy | High (proven ε-budget) | Low | Moderate |
| Homomorphic Encryption | Extremely high | High (10×–1000×) | None (exact) |
| Confidential Computing | High (hardware-dependent) | <5% | None |
The vocabulary
The cryptographic and statistical techniques that let systems use personal data without exposing it: federated learning, differential privacy, homomorphic encryption / secure multi-party computation, and hardware enclaves (TEEs). Each protects a different moment in the data's life — none protects all of them alone.
No single PET covers storage, compute, and outputs at once, so they're stacked: storage encryption and key management protect data at rest, FL + DP protect it during training, HE and TEE enclaves protect it in use — while it's being computed on — and encrypted channels protect it at inference. A gap in one layer is caught by the next.
Shipping means in production now (encryption in transit/at rest, region residency, journaling). Nearest-term means it runs on infrastructure we already deploy to and is being productized (TEEs). Roadmap means the architecture is designed for it and it ships behind the same shadow → adoption gate every capability passes (FL, DP, HE). Labels move only when the capability actually does.
Regulatory compliance & governance
The process journals what a privacy risk assessment must prove — lineage, Article 22 explainability, safeguards, monitoring. Auditing AI decisions becomes reading a log.
The mapping
The DPIA (GDPR Art. 35) is yours, the data controller's. We produce the evidence each section needs. ✅ shipping today · 🔜 roadmap.
| DPIA section | What it demands | Evidence | Status |
|---|---|---|---|
| §1 Screening | Identify high-risk automated decision-making | Decision-mode registry: every model/version's autonomy level is explicit | ✅ |
| §2 Data flow | Map inputs, sources, lineage | Observe normalizes and timestamps every input; each decision journals its exact inputs | ✅ |
| §3 Legal basis & Art. 22 | Explain automated decisions | Per-decision rationale + calibrated confidence, human-readable | ✅ |
| §4 Risk & mitigation | Concrete safeguards | Safety gates, kill-switch, drift-breach revert, shadow-before-adopt | ✅ |
| §5 Vendor due diligence | Processor terms, residency | Region-selectable AWS/GCP; encryption in transit and at rest; DPA/SCC paperwork | 🔜 |
| §6 Sign-off | Inspectable evidence for the DPO | Decision journal with per-decision drill-down (audit UI) | 🔜 |
| §7 Ongoing monitoring | Continuous post-launch review | Drift alerts + reproducible replay by model version and input hash | ✅ |
Decision process governance & human review
Autonomy is earned on an explicit trust ladder — and a human can review, override, or halt the decision process at every rung. The ML pipeline improves precision underneath; the human owns the high-stakes call.
The controls
Approve, overturn, or pause any decision path — the override itself is journaled.
Safety gates, hurdles, kill-switch, drift-revert — above whatever model runs inside.
Inputs, outputs, versions, decision paths — reproducible by stamp, ready for discovery.
High-stakes, regulated sectors
Human makes the final high stakes decision, not AI
Top 5 industries where LuckMa fits
Reproducible replay sessions, journaled decisions, full risk gates — see the Demo.
Default probability with per-decision rationale — the adverse-action evidence FCRA/GDPR demand.
Human-in-the-process screening with journaled reasoning — the corpus a bias review needs.
Shadow-mode underwriting against incumbent rules before any autonomous decision.
HIPAA-conscious triage support with human sign-off and complete provenance.
Transparency, contestability, and audit trails as first-class requirements.