Systems Architecture: Scaling Knowledge for Millions

Introduction

Aevum Encyclopedia serves over 2.4 million articles across 140+ languages to researchers, students, and developers worldwide. Supporting this scale requires an architecture that balances low-latency retrieval, massive concurrent reads, continuous AI-driven content generation, and strict data integrity.

This document outlines the core architectural principles, data flow, storage topology, and scaling strategies that keep the platform reliable, accurate, and fast.

Core Design Principles

• Semantic-First: Every component treats knowledge as interconnected nodes, not isolated documents.
• Edge-Optimized: Content is cached and served from 120+ edge locations to ensure sub-50ms TTFB globally.
• Fault-Tolerant: Multi-AZ deployment with automatic failover; no single point of failure in the read path.
• Verifiable by Design: Immutable audit trails for every content mutation, aligned with academic integrity standards.

High-Level System Topology

The platform follows a layered event-driven microservices architecture. Requests flow through a global edge network into a unified API gateway, which routes traffic to domain-specific services.

Data Ingestion & Processing Pipeline

New and updated content enters through a multi-stage verification pipeline designed to prevent hallucination and ensure academic rigor.

1. Submission: Contributors submit via web interface, API, or automated crawlers. Payloads are schema-validated against our OpenAPI spec.
2. Preprocessing: Text is normalized, OCR is applied to images, and multilingual NLP models extract entities, claims, and citations.
3. Fact-Verification: Claims are cross-referenced against trusted primary sources using our AI auditor. Discrepancies trigger human review queues.
4. Graph Construction: Validated entities become nodes; relationships become edges. Versions are appended to an immutable ledger.
5. Embedding & Indexing: Text blocks are vectorized (768-dim) and indexed. BM25 indices are updated in parallel.

POST /v1/articles/ingest
{
  "title": "Quantum Error Correction",
  "language": "en",
  "body": "...",
  "sources": ["arxiv:2304.xxxx", "doi:10.1038/nature..."],
  "metadata": { "tags": ["physics", "quantum-computing"] }
}

Hybrid Search & Retrieval

Aevum uses a dual-vector + keyword retrieval system to balance precision and recall. The search engine supports:

• Semantic Matching: Dense vector similarity via Milvus (HNSW index)
• Lexical Matching: BM25 scoring via Elasticsearch
• Reranking: Cross-encoder model (MiniLM) scores top-100 candidates for final ranking
• Contextual Filters: Language, domain, recency, and contributor verification level

Query latency is kept under 80ms p99 through result caching, connection pooling, and async index updates.

AI & Inference Engine

AI is integrated as an augmentation layer, not a replacement for verified knowledge. Key components include:

• Routing Layer: Directs queries to specialized models (summarization, translation, fact-checking, graph expansion)
• RAG Pipeline: Retrieves relevant graph subgraphs and documents, injects them into the context window, and enforces source grounding
• Guardrails: Output is filtered against hallucination patterns, citation requirements, and policy constraints before serving

Security & Observability

Trust is the foundation of an encyclopedia. We enforce:

• Encryption: AES-256 at rest, TLS 1.3 in transit, customer-managed KMS keys
• Access Control: RBAC + ABAC for contributors, editors, and admins. All mutations require cryptographic signatures.
• Compliance: GDPR, CCPA, and academic publishing standards (COPE guidelines)
• Observability: OpenTelemetry tracing, Prometheus metrics, Grafana dashboards. SLOs enforced via error budgets.

Scaling & Performance

The system is designed to handle 10x traffic spikes without degradation:

• CDN Caching: 94% of article views are served from edge; stale-while-revalidate pattern handles updates
• Database Sharding: PostgreSQL shards by language/domain; graph partitions by community detection
• Auto-Scaling: Kubernetes HPA/VPA scales stateless services; stateful components use managed autoscaling
• Async Processing: Kafka handles ingestion, indexing, and AI queueing with exactly-once semantics

Future Roadmap (2025–2026)

• Decentralized storage layer using IPFS + Filecoin for contributor-submitted media
• Real-time collaborative editing with CRDTs and conflict-free graph updates
• Multimodal knowledge graphs (text, audio, 3D models, datasets)
• Quantum-resistant cryptographic signatures for audit trails

This architecture is continuously evolving. For API specifications, SDKs, or infrastructure contribution guidelines, visit the Developer Portal.