NoahAI Technical Whitepaper
This whitepaper is not a list of tech specs but a technical hub so that team, investors, and partners share the same picture.
The core goal is "moving the subject of financial judgment to AI"—focus is on 'operable safety', not short-term return competition.
Full whitepaper is available below.
Table of contents
- 1
Overview
NoahAI's core goal is "moving the subject of financial judgment to AI"—focusing on operable safety, not short-term return competition. This whitepaper describes the technical design and principles to achieve that goal.
- 2
Technical philosophy (design principles)
Four principles: Safety First, Record → Review → Improve, Collective Learning, Explain & Verify. The most important KPI in financial AI is minimizing failure, not short-term returns; every judgment is recorded, replayed, and reflected in improvement.
- 3
System architecture
High-level architecture in 7 layers: Market Data, Account State, Decision, Risk & Guardrails, Execution, Logging & Report, Feedback Loop. Each layer's role and interaction are described.
- 4
AI optimization loop
The 6-step loop Record → Review → Policy → Risk → Feedback → XAI is explained from an operational view. We aim for an "experience-accumulating judgment structure," not "fixed automation"; each step's role and improvement mechanism are detailed.
- 5
Learning data structure
Data structures in a CareLog-like schema. DecisionLog, MarketSnapshot, AccountSnapshot, RiskEvent, ExecutionResult, XAITrace and their fields and purpose; standardized record enables replay, learning, and audit.
- 6
XAI (explainable AI)
XAI value from the perspective of explainability = trust / audit / reproducibility. Four use cases: trust, audit and trace, reproducibility, improvement and learning; log structure and version tracking are covered.
- 7
Multi-model benchmark
Mechanism for comparing multiple AI engines to learn an optimal judgment structure. Same data and prompts, engine-level performance comparison; verification process to reduce bias and illusion.
- 8
Security and compliance
Security and regulatory compliance for financial services: data encryption, access control, audit logs, privacy, anonymized pattern learning.
- 9
Enterprise adoption
Adoption path and requirements for enterprise: RBAC, SSO, on-prem/VPC options, SLA, customization, integration API.
- 10
Future plans
Asset and channel expansion proceed step by step on top of the validated judgment·record·guardrail structure. This section does not imply immediate commercialization; it describes the technical roadmap and design principles for extending the same operational structure to other high-risk verticals.
Summary
NoahAI is financial AI operations infrastructure designed so that repeated judgment in finance and assets can be performed safely by AI. The technical core is not short-term returns or auto-trading performance but a structure where judgment, risk control, record, replay, and verification are possible.
This whitepaper is not a commercial service brochure or investment pitch. It is a reference document for explaining executability, reproducibility, and accountability in government R&D, public projects, and institutional adoption review.
The whitepaper describes NoahAI's overall technical structure, AI optimization loop, learning data structure, multi-model benchmark results, security and compliance, enterprise adoption, and future development plans.
All design is based on financial AI operation principles that reduce failure and clarify responsibility, not short-term performance.
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