Technology for a safe financial AI assistant
NoahAI Labs designs for safety, control, and reproducibility first—not short-term performance. We build an operable financial AI pipeline: judgment → execution support → logging → replay → improvement.
Details are in the system architecture doc: input, judgment, risk, execution, logging, and feedback layers.
Operable
Real-world stack
Safety
Guardrails, halt conditions, conservative decisions
Logging / replay
Full logs, reports, reproducibility
Verification
Multi-model comparison, replay, evaluation
Feedback
Anonymized pattern learning, risk signals
Multi-asset
Securities, real estate, and more
Current technology status
Operating version
As of Jan 2026 we run v3.8.x 24/7 in production; stabilizing and improving.
Key progress
- • Exchange order/execution stability and exception handling
- • TP/SL and risk guardrail logic improvements
- • ETF/equities: UI and broker API integration complete (internal test/stabilization)
Exchange support
Six exchanges in concurrent operation
• Binance (standalone), Bybit, OKX, Bitget
• Upbit, Bithumb (CCXT)
• Domestic broker API integrated (ETF/equities; pre-commercial internal test)
Financial vertical AI operating stack
We build a financial AI stack where judgment, risk, logging, and verification work in production.
Input Layer
Real-time interpretation
We interpret market data, account/position state, and user goals together.
Decision Layer
Safety controls
Guardrails, halt conditions, and conservative decisions control risk.
Risk Layer
Risk / guardrails
Safety rules and controls limit excessive risk.
Execution Layer
Execution
Within user settings and guardrails, we support automated judgment organization and repetitive task execution.
Logging Layer
Logging / replay
Full process is logged and reported for reproducibility.
Feedback Layer
Feedback / improvement
Learning at anonymized pattern level detects risk signals faster.
Guardrails
In finance, controllability matters more than speed. NoahAI designs with these principles as default.
Guardrails
Safety rules (max risk, halt conditions, etc.) are applied first.
Transparency
Judgment rationale and outcomes are logged in verifiable form.
Verification
Multi-model verification and comparison reduce bias.
Logging / replay
Every judgment and outcome is logged and reported; we look back and feed results into the next policy.
Logging
- • Judgment rationale and execution results are logged
- • Standardized format for replay
- • Learnable, auditable, traceable structure
Replay
- • Reports for replay to improve next decisions
- • Review success/failure patterns and improve
- • Verifiable in reproducible form
Verification
We provide evaluation, leaderboard, and replay systems that compare and verify models/strategies under the same conditions.
Multi-model comparison
Compare multiple models with same prompt and data
Operating metrics
Operating metrics including guardrails (stability, consistency, resilience)
Replay
Reproducible test scenarios
Extension structure
NoahAI Labs technology extends on the same judgment–logging–verification structure; it is not limited to a single asset or function.
Multi-asset
We aim to extend from initial operating experience to securities/ETF, real estate analysis, and more.
Everyday finance + voice
For users who find mobile/PC difficult, we support understanding via voice and extend to repetitive tasks (transfers, checks) and fraud/phishing risk response.
Long-term extension
NoahAI aims to provide an explainable financial assistant experience beyond smartphone/web—via voice (STT/TTS) and across devices. Our financial AI assistant technology is designed with future physical agents (robots) in mind. External integration expands step by step within clear regulation, security, and responsibility.
This describes technical extension possibility; we do not currently offer commercial robot-integrated services.
Technical documentation
This page is based on the technology we currently operate. New features are verified and released in stages.
System architecture
Core, Engine, Analyzer, Storage and extension design →
AI optimization loop
Record → Review → Policy → Risk → Feedback → XAI →
XAI (explainable AI)
Decision rationale, traceable logs, reports →
Data structure
Schema and storage for judgment/result/context →
Technical proof
Operating pipeline and log examples →
Whitepaper
Architecture, loop, safety, verification, data, roadmap →