Open Engineering/Open Engineering
trıNetra
Independent research into consequential decision reasoning.
A research institution focused on structural accountability, reasoning preservation, and applied governance for AI-intensive systems.
Public Repository

Open Engineering

github.com/RocKing000/Portfolio  ·  Public  ·  triNetra Research

The triNetra Lifecycle
ResearchTheoryArchitectureEngineeringReference Implementation
About This Repository

This repository is the public engineering and implementation layer of the triNetra research ecosystem. Research is published, architectures are derived from it, and engineering prototypes demonstrate those architectures at production scale across multiple technology stacks.

The research studies how consequential decisions are reasoned, recorded, and governed. Architecture documents translate those findings into structural specifications. Engineering prototypes demonstrate that the architectural conclusions can be implemented at production scale.

The repository evolves as the research programme advances.

github.com/RocKing000/Portfolio ↗
Reference Implementations

Five production-scale engineering systems demonstrating the full technology range of the triNetra ecosystem. Each system is independently deployable and represents a complete vertical: from database schema to API layer to frontend application.

Implementation Detail
Simulation / Enterprise AI
DIRE-X
Geopolitical Intelligence Platform
View on GitHub ↗
ReactViteNode.jsPythonFastAPISupabase
  • 17 domain-specific simulation engines: economic, geopolitical, military, workforce, health, logistics, compliance, manufacturing, supply chain, and more
  • Real-world dataset integration via Kaggle: GDP (World Bank 2025), military expenditure, trade flows (34-year dataset), mineral production, FSI risk index, ESG emissions
  • Scoring engine with 6 calculation modules: adversarial, confidence, dependency, macro, risk, temporal
  • Multi-player simulation with branch management, leaderboard, and geospatial globe visualisation
  • Company creation and lifecycle tracking within simulated geopolitical scenarios
Computer Vision / Enterprise AI
Vision Framework
Computer Vision KYC Pipeline
View on GitHub ↗
PythonFastAPIAngularC#.NETOllamaOpenCV
  • Pluggable computer vision framework with KYC as the reference vertical implementation
  • Document detection, perspective correction, OCR extraction for Aadhaar, PAN, Passport, and Driving Licence formats
  • Face capture, liveness detection, and face comparison pipeline
  • Angular 17 frontend with camera integration, WebSocket streaming, and KYC flow orchestration
  • C# .NET wrapper for integration into enterprise backend systems
  • Synthetic training data generation with realistic document templates and augmentation pipelines
  • 6 multi-agent debug suite + stress testing framework
Research Context

Automated identity verification is precisely the high-consequence decision domain studied in the EAD Research Programme: systems that make or inform decisions about individuals where the reasoning behind those decisions must be recoverable.

Enterprise Engineering / Financial Systems
Loan Origination System
Financial Decision Support Platform
View on GitHub ↗
Pythonscikit-learnFastAPIAngular
  • ML-driven credit risk classification with model training pipeline
  • Decision support modules: alert pools, flags, predictions, and hierarchy views
  • Analytics dashboard with chart service and data export
  • Angular frontend with NgRx state management and full module routing
  • Configurable settings and reporting layer for institutional deployment
Research Context

Credit and underwriting decisions are among the five institutional contexts explicitly identified in the EAD Research Programme as environments where the judgment layer absence produces the most consequential information failures. The Judgment Layer (EAD-2026-02) was grounded in part on financial services cases.

Enterprise AI / Distributed Systems
AI SDLC Automation
Distributed Agentic Development Platform
View on GitHub ↗
PythonLangGraphC#.NETAngularDockergRPCRabbitMQRedisKeycloakPrometheusGrafanaELK
  • 4 LangGraph components covering the complete software development lifecycle: requirements gathering, design automation, code generation, and testing
  • Design automation splits into technical architecture design and UI/UX design as parallel agentic workflows
  • Code generation layer covers frontend, backend, database, AI/ML, and integration layers independently
  • Multi-LLM gateway with providers for OpenAI, Anthropic, Google, and Mistral, swappable at runtime
  • .NET Ocelot API gateway, gRPC inter-service communication, and RabbitMQ event bus
  • Full observability stack: Prometheus metrics, Grafana dashboards, ELK log aggregation
  • Keycloak authentication, MinIO document storage, SQL Server persistence
  • Angular frontend with NgRx state management, approval gate workflows, and session management
Research Context

The AI builder landscape studied in PaaF in the Field (EAD-2026-04) is the environment in which this system operates: independent AI builders constructing complex systems. The paper's field observations about the structural conditions of AI-augmented engineering are directly relevant to this domain.

Enterprise Engineering / Analytics
Multi-Tenant Analytics Platform
Enterprise Signal Intelligence and Analytics
View on GitHub ↗
C#.NET 8PythonFastAPIAngularSQL ServerRedisSignalR
  • Multi-tenant isolation architecture with tenant-level data partitioning and access control
  • Drag-and-drop dashboard builder with 5 widget types: bar chart, line chart, pie chart, metric card, and data table
  • AI-augmented search with input classifier, query processor, search strategy selector, and typo correction
  • Real-time signal intelligence with SignalR WebSocket broadcasting and signal lifecycle management
  • Role-based access control with guard-protected routes and admin hierarchy management
  • Encryption middleware, JWT authentication, and request logging across all API surfaces
  • Multi-language support: English and Kannada (kn) i18n JSON bundles
  • Cypress E2E test suite: accessibility, responsive design, browse, and search tests
  • Structured architecture documentation: backend, database, and frontend architecture specifications
Research Context

Enterprise decision support systems are the primary domain of the Judgment Layer research (EAD-2026-02). The audit logging, role separation, and decision traceability patterns in this system are concrete engineering instances of the structural accountability architecture the Eagle Framework is designed to assess.

Technology Reference

Derived from the five prototype implementations. These are not aspirational; every technology listed is present in the repository.

Frontend
React · Vite · Angular 17 · NgRx · Tailwind CSS · SCSS · WebSocket · SignalR
Backend
Node.js · Python FastAPI · C# .NET 8 · gRPC · RabbitMQ · Redis · SQL Server · Supabase
AI and ML
LangGraph · OpenAI · Anthropic · Google Gemini · Mistral · Ollama · scikit-learn · OpenCV
Infrastructure
Docker · Keycloak · MinIO · Prometheus · Grafana · ELK Stack · Ocelot API Gateway
Testing
Cypress E2E · pytest · multi-agent stress testing · synthetic data generation
Research Documentation Layer

The triNetra/ folder contains the architecture, research, and platform documentation layer of the ecosystem. These are working documents that support the published research and platform development. The published papers are available on SSRN.

Architecture (6 files)
Design principles, insight cycle, platform registry, research lifecycle, and system overview.
Platform Documentation (18 files)
Eagle Framework specification, alignment intelligence, organisation intelligence, portfolio intelligence, and Eagle scoring engine specification.
Research Philosophy (5 files)
First principles, research vs. assessment distinction, structured reasoning rationale, and insight vs. consulting positioning.
Ecosystem (6 files)
Sector-specific applications: defence, energy, infrastructure, manufacturing, and ecosystem overview.
Methodology (3 files)
PaaF four-phase methodology and triNetra pattern research standard.
Case Studies (3 files)
Evolution-to-assured and structural change case study documents.
RiskOpsBench (3 files)
Corpus documentation and sample outputs for the RiskOpsBench benchmark programme.
Roadmap (3 files)
2026 and 2027 development roadmap documents.
Browse documentation layer ↗
Authored Works
Research Foundation

The engineering layer does not exist independently. The published research provides the conceptual foundation; the architecture documents translate that into structural specifications; the implementations demonstrate that those specifications can be realised at production scale.

EAD Research Programme
Four published working papers: external AI dependence, the judgment layer, the infrastructure loop, and PaaF field analysis.
Eagle Framework
35-pattern, 7-dimension deterministic structural assessment framework derived from the EAD Research Programme.
PaaF Methodology
Four-phase recursive research methodology used to produce both the working papers and the architectural documentation layer.
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