Technical Architecture

Complete system design for Malaysia's sovereign government intelligence platform

Four-Layer Architecture

1

Data Extraction Layer

Distributed crawler system extracts documents from all government agencies with proper authorization and security protocols. Handles multiple document formats and stores raw data efficiently.

Distributed Crawler
Proven architecture handling 200,000+ documents per day with 300 concurrent workers. Checkpoint system prevents data loss during interruptions.
Python Async/Await 300 Workers
Document Extractors
Multi-format extraction supporting PDF, Excel, Word, HTML, and structured data. Maintains document metadata and relationships.
PyPDF2 openpyxl BeautifulSoup
Storage Layer
Object storage for raw documents with PostgreSQL for structured metadata. Enables efficient retrieval and audit trails.
MinIO/S3 PostgreSQL NVMe SSD
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2

Indexing & Search Layer

Multi-engine indexing strategy for different query patterns. Full-text search, inverted indexes, and optional semantic search provide comprehensive data access.

Elasticsearch
Industry-standard full-text search with powerful aggregations. Handles complex queries across billions of records with sub-second response times.
ES 8.x Aggregations Clustering
RocksDB Index
Fast inverted index for specific retrieval patterns. Proven with 4.9M+ documents indexed, optimized for high-throughput operations.
RocksDB BM25 Embedded
Vector Database
Optional semantic search for natural language queries. Enables similarity search and contextual retrieval beyond keyword matching.
Milvus pgvector HNSW
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3

AI Intelligence Layer

Fine-tuned Llama 3 70B model trained on Malaysian government data provides sovereign AI capability. Generates queries, interprets results, and provides fact-based insights without hallucination.

Query Parser
Natural language understanding converts user questions into optimized database queries. Handles multi-agency data requests and complex conditions.
NLP Intent Recognition Query Generation
Result Interpreter
Transforms aggregated database results into natural language summaries. Maintains factual accuracy by only working with actual data.
Llama 3 70B Fine-tuned GPU Inference
Pattern Detection
Identifies anomalies, trends, and correlations across government datasets. Suggests actionable insights for decision-makers.
Statistical Analysis Anomaly Detection Recommendations
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4

Presentation Layer

User-facing dashboard and reporting system for government analysts and leadership. Role-based access control ensures data security and compliance.

Intelligence Dashboard
Real-time analytics interface with natural language querying. Visualizes cross-agency data with charts, tables, and interactive reports.
React/Vue D3.js WebSockets
Export & Reports
Generate formatted reports for executive briefings and audits. Support for PDF, Excel, and custom formats with automated scheduling.
PDF Generation Excel Export Templates
Alert System
Automated notifications for anomalies and critical findings. Configurable thresholds and escalation workflows for timely response.
Real-time Alerts Email/SMS Webhooks

Query Execution Flow

1
User Query Input
Government analyst enters natural language question: "Show immigration patterns with anomaly detection for last 30 days"
2
AI Parses Intent
Llama 3 model identifies: data source (immigration), time range (30 days), analysis type (anomaly detection)
3
Generate Optimized Query
System generates Elasticsearch aggregation query targeting immigration database with statistical anomaly detection
4
Database Computes Results
Elasticsearch processes billions of records, returns aggregated data: 500 rows with daily statistics and anomaly scores
5
AI Interprets & Formats
AI receives aggregated results, generates natural language summary with specific findings and actionable suggestions
6
Present to User
Dashboard displays: AI summary, data table, visualization charts, and export options - all in under 2 seconds

Data Transformation Journey

Understanding how we transform raw government data into actionable intelligence - visualized through a simple analogy

INTEGRATE
1
Raw Data Collection
Like scattered LEGO pieces of different colors - government data comes from multiple agencies in various formats, unorganized and mixed together. Immigration records, finance data, health statistics all jumbled up.
2
Data Sorting
We separate the LEGO pieces by color - grouping similar data types together. Immigration data in one group, financial records in another, health data separately organized.
3
Data Arrangement
Organized LEGO pieces are now structured properly - data is cleaned, standardized, and arranged in databases ready for analysis. Each color group properly positioned.
UNDERSTAND
4
Visual Presentation
Building a bar chart from LEGO blocks - transforming organized data into visual dashboards, charts, and graphs that show patterns and trends at a glance.
5
Context & Storytelling
Creating a LEGO house tells a story - RexB AI explains what the data means in context. "Immigration increased 15% during holiday season" provides narrative understanding beyond just numbers.
BUILD
6
Actionable Intelligence
Functional LEGO creations ready to use - data becomes practical tools for decision-making. Policy recommendations, resource allocation plans, and strategic insights government leadership can act upon immediately.
💡 Key Insight
Just like building with LEGO - we take scattered pieces (raw data), organize them (integrate), create meaningful structures (understand), and build something useful (actionable intelligence). NexStellar transforms government data chaos into strategic clarity.

Technology Stack

Backend
Python 3.11+ Flask/FastAPI Celery Redis
Databases
PostgreSQL 16 Elasticsearch 8.x RocksDB MinIO/S3
AI/ML
Llama 3 70B PyTorch Transformers CUDA 12.x
Infrastructure
Kubernetes Docker Nginx Prometheus
Frontend
React/Vue.js D3.js WebSockets TailwindCSS
Security
OAuth 2.0 JWT RBAC Encryption

Proven Scalability

4.9M+ Documents
Currently indexed and searchable in production NestDaddy system
200K Items/Day
Crawler capacity with 300 concurrent workers, proven throughput
Sub-second Queries
Response time for complex aggregations across millions of records
Horizontal Scaling
Add more nodes to handle increased load without downtime
Checkpoint System
Prevents data loss with automatic recovery from interruptions
23 Agencies Ready
Architecture designed to connect all Malaysian government data sources