4. Technical Architecture
A deep dive into the distributed, AI-native infrastructure powering the world's most comprehensive knowledge graph. Built for scale, accuracy, and real-time intelligence.
System Overview
Aevum Encyclopedia operates on a microservices architecture designed for horizontal scalability, fault tolerance, and multi-tenant isolation. The platform processes over 2.4 million articles across 140+ languages, serving millions of read operations daily with sub-50ms latency.
At its core, the system combines graph databases for semantic relationships, vector stores for AI-powered similarity search, and a real-time event pipeline for content ingestion and verification.
High-Level Architecture
The following diagram illustrates the end-to-end flow of data through the Aevum platform, from ingestion to delivery.
Core Components
The platform is composed of specialized microservices, each responsible for a distinct domain of functionality.
Orchestrates retrieval-augmented generation (RAG), entity extraction, sentiment analysis, and hallucination guardrails using an ensemble of specialized LLMs.
Stores entities and relationships in a property graph model. Supports complex traversals for discovering implicit connections between topics across disciplines.
High-dimensional embeddings enable semantic similarity search, clustering, and personalized recommendation across the entire corpus.
Hybrid search combining keyword matching, vector similarity, and graph traversal. Supports faceted filtering, autocomplete, and multi-language query expansion.
Multi-layer fact-checking system that cross-references claims against primary sources, tracks citation provenance, and flags potential inaccuracies for expert review.
Asynchronous message broker powering real-time updates, content indexing, audit logging, and inter-service communication with guaranteed delivery semantics.
Data Ingestion Pipeline
New content enters the system through multiple channels, each processed through a standardized pipeline ensuring quality and consistency.
AI & Machine Learning Stack
Aevum employs a specialized ensemble of models tailored for knowledge work, prioritizing accuracy and interpretability over raw generation speed.
| Function | Model Architecture | Latency | GPU |
|---|---|---|---|
| Semantic Search | E5-mistral-7b-instruct | <20ms | A10G |
| Entity Extraction | Custom BERT + CRF | <15ms | T4 |
| Content Summarization | Llama-3-8b-Aevum-RLHF | <100ms | A100 |
| Fact Verification | DeBERTa-v3 + Rule Engine | <50ms | T4 |
| Translation | NLLB-200-distilled-600M | <30ms | A10G |
| Query Understanding | Ensemble (FastText + Transformer) | <10ms | CPU |
Security & Compliance
Trust is foundational to knowledge. Our infrastructure implements defense-in-depth strategies with compliance certifications across data privacy, integrity, and availability.
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Encryption: AES-256 at rest, TLS 1.3 in transit, end-to-end encryption for contributor data.
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Authentication: Multi-factor authentication, OAuth 2.0 / OIDC, role-based access control (RBAC).
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Compliance: GDPR, CCPA, SOC 2 Type II, ISO 27001 certified.
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Auditing: Immutable audit logs, content versioning with cryptographic hashing, provenance tracking.
Scalability & Performance
The architecture is designed to scale elastically across global regions, ensuring consistent performance regardless of traffic spikes or geographic distribution.
Deployed across 30+ AWS regions with active-active replication. Automatic failover and geo-routing ensure sub-100ms response times worldwide.
Kubernetes-based orchestration with HPA/VPA. Predictive scaling models anticipate traffic patterns from academic calendars and breaking news events.
API & Integrations
Build on Aevum's knowledge infrastructure with our comprehensive API suite. All endpoints support pagination, filtering, and field selection.