01 — System Overview

Platform Architecture

Aevum Encyclopedia runs on a distributed microservices architecture spanning multiple availability zones, designed for high availability, fault tolerance, and horizontal scalability.

▸ Platform Architecture Layers
Edge / CDN
Global
🌐

CloudFront CDN

240+ edge locations, sub-50ms static asset delivery

DNS
🔀

Route 53 + GeoDNS

Intelligent traffic routing & failover

WAF
🛡️

DDoS Protection

Shield Advanced + custom WAF rules

API Gateway
Auth
🔑

Authentication Service

OAuth2 / JWT / API Key management

Rate
📊

Rate Limiter

Token bucket algorithm, per-tenant limits

Log
📝

Request Logger

Structured logging + tracing headers

Cache
⚡

API Cache Layer

Redis-backed response caching

Services
📖

Article Service

CRUD, versioning, revision history

🔍

Search Service

Semantic + keyword hybrid search

🧠

AI Engine

LLM inference, embeddings, RAG

👥

User Service

Profiles, contributions, permissions

🌐

Translation Service

Neural MT for 140+ languages

Data Layer
Primary
🗄️

PostgreSQL Cluster

Multi-AZ, read replicas, logical replication

Vector
📐

pgvector / Milvus

Embedding storage & similarity search

Cache
🔴

Redis Cluster

Sessions, rate limits, hot content

Blob
📦

S3 + CloudFront

Media assets, PDFs, static resources

02 — Technology Stack

Core Technologies

Battle-tested technologies chosen for reliability, performance, and developer productivity across all layers of the platform.

⚛️
Frontend
Client & Rendering

Next.js 14 with App Router, React Server Components, and edge rendering for sub-second initial page loads. ISR for article pages, SSR for dynamic content.

Next.js 14 React 18 TypeScript Tailwind CSS SWR PWA
🔷
Backend Services
Microservices & APIs

Go microservices for high-throughput paths, Python services for ML integration. gRPC for inter-service communication, REST/GraphQL for external APIs.

Go 1.22 Python 3.12 gRPC GraphQL FastAPI Protocol Buffers
🐘
Database & Storage
Persistence Layer

PostgreSQL with pgvector for hybrid search. Redis Cluster for caching and session management. S3 for media with lifecycle policies and CDN distribution.

PostgreSQL 16 pgvector Redis 7 S3 Milvus CockroachDB
🧠
AI / ML Pipeline
Intelligence Layer

Fine-tuned LLMs for content generation assistance, embedding models for semantic search, RAG pipelines for fact verification, and automated translation systems.

PyTorch LangChain HuggingFace vLLM ONNX Ray
☸️
Infrastructure
Orchestration & Ops

Kubernetes (EKS) for container orchestration across 3 regions. Terraform for IaC, ArgoCD for GitOps deployments. Comprehensive observability stack.

Kubernetes EKS Terraform ArgoCD Prometheus Grafana
📡
Messaging & Events
Async Communication

Kafka for event streaming between services. Pub/Sub patterns for real-time notifications. Dead letter queues for fault-tolerant processing.

Apache Kafka Kafka Streams NATS SSE WebSockets
03 — Data Pipeline

Content Ingestion Flow

Every article on Aevum Encyclopedia passes through a rigorous multi-stage pipeline ensuring quality, accuracy, and discoverability before publication.

📝
Step 1

Ingestion

Contributors submit articles via editor API or bulk import. Raw content is queued for processing.

🤖
Step 2

AI Review

Automated fact-checking, grammar analysis, citation verification, and bias detection.

👨‍🔬
Step 3

Peer Review

Subject-matter experts review flagged content. Multi-layer consensus model.

📐
Step 4

Embedding

Text is vectorized, indexed in pgvector, and linked in the knowledge graph.

🚀
Step 5

Publish

Content is deployed to CDN, search index updated, and subscribers notified.

04 — Data Flow

Request Lifecycle

Understanding how a single user request traverses the platform, from edge to data layer and back.

🔍 Search Request Flow

When a user searches, the query passes through semantic understanding, vector similarity search, and re-ranking before results are returned.

// Search endpoint handler async func HandleSearch(ctx, req) { query := NormalizeQuery(req.Query) // Generate embedding vec := await EmbeddingService.encode(query) // Hybrid search results := await Database.hybridSearch({ text: query, vector: vec, k: 50, filters: req.Filters }) // Re-rank with ML model ranked := Reranker.score(results, query) return Paginate(ranked, req.Page) }

📖 Article Read Flow

Article reads are heavily cached at multiple layers. Fresh content is validated against the source-of-truth database.

// Article read handler async func GetArticle(id, lang) { // L1: In-memory cache if hit := Cache.get(`art:${id}:${lang}`) { return hit } // L2: Redis cluster if hit := await Redis.get(`art:${id}:${lang}`) { Cache.set(`art:${id}:${lang}`, hit) return hit } // L3: Database article := await DB.query(` SELECT * FROM articles WHERE id = $1 AND lang = $2 `, id, lang) Cache.set(`art:${id}:${lang}`, article, {ttl: 300}) return article }

✍️ Article Creation Flow

New articles trigger the full pipeline: draft storage, AI analysis, notification to reviewers, and eventual publication.

// Article creation handler async func CreateArticle(data) { // Validate & sanitize validated := SanitizeInput(data) // Store draft draft := await DB.createDraft(validated) // Trigger AI pipeline await Kafka.publish("article.created", { id: draft.id, author: draft.author_id, topic: draft.category, created_at: Now() }) // Notifications handled async return { status: "queued_for_review", id: draft.id } }

🌐 Translation Flow

Articles are translated using neural machine translation, then reviewed by native speakers in our contributor network.

// Translation pipeline async func TranslateArticle(id, targetLang) { article := await DB.getArticle(id) // Segment text into chunks segments := SegmentText(article.content, {max: 512}) // Neural MT translation translated := await NMTService.translate({ segments: segments, source: "en", target: targetLang, domain: article.category }) // Post-edit queue for humans await Queue.assignReviewers(translated, targetLang) return { status: "pending_human_review" } }
05 — API Layer

Public & Internal APIs

Aevum Encyclopedia exposes comprehensive REST and GraphQL APIs for third-party integrations, with strict rate limiting and authentication.

GET

/articles/:id

Retrieve a full article with revision history, metadata, and related content links.

GET

/search

Semantic and keyword hybrid search with filtering by category, language, and date range.

POST

/articles

Create a new article draft. Triggers the automated AI review pipeline.

PUT

/articles/:id/revisions

Submit a revision to an existing article. Requires contributor permissions.

GET

/knowledge-graph/:entity

Retrieve entity relationships, connections, and the subgraph surrounding a concept.

POST

/ai/insights

Generate AI-powered insights, summaries, or connections for a given topic.

GET

/categories

List all knowledge categories with article counts, trending topics, and subcategories.

DELETE

/articles/:id

Soft-delete an article. Requires admin or editorial board permissions.

POST

/graphql

Full GraphQL endpoint for complex, nested queries across articles, users, and metadata.

06 — AI / ML Pipeline

Intelligence Architecture

Our ML infrastructure powers semantic search, content verification, automated summarization, and multilingual translation across the entire platform.

🔮
Embedding Service
Vector Generation

Custom fine-tuned embedding model (768-dim) optimized for encyclopedia content. Processes articles into vectors for similarity search and knowledge graph construction.

SentenceTransformers ONNX Runtime Batch Size: 256 ~12ms latency
🔎
RAG Pipeline
Retrieval-Augmented Generation

Multi-stage RAG for fact verification and content assistance. Retrieves relevant articles, cross-references claims, and generates verified responses with citations.

LangChain vLLM Re-ranking: CrossEncoder Hallucination Guard
🌍
Translation Engine
Neural Machine Translation

Domain-adapted NMT models for 140+ languages. Trained on encyclopedia corpora with back-translation for low-resource languages. BLEU scores >35 on test sets.

NLLB-200 Fine-tuned 140 Languages GPU Cluster
07 — Security

Security Architecture

Multi-layered security approach protecting user data, content integrity, and platform infrastructure at every level.

🔐

Zero Trust Network

Every service-to-service call is authenticated and encrypted via mTLS. No implicit trust between any components of the platform.

✓ Implemented
🛡️

DDoS & WAF

AWS Shield Advanced + custom WAF rules blocking 2.4M+ malicious requests daily. Rate limiting at edge and API gateway.

✓ Active
🔑

Authentication

OAuth 2.0 + OIDC with JWT tokens. Multi-factor authentication for contributors. API key rotation for service accounts.

✓ SOC2 Compliant
🔒

Data Encryption

AES-256 at rest, TLS 1.3 in transit. Customer-managed encryption keys (CMEK) for sensitive data. Automatic key rotation.

✓ FIPS 140-2
📋

Audit Logging

Immutable audit trail for all content changes, administrative actions, and security events. 7-year retention, tamper-evident logs.

✓ Immutable
🧪

Penetration Testing

Quarterly third-party pen tests, continuous vulnerability scanning, and a $10,000 bug bounty program for critical findings.

✓ Quarterly
08 — Scale & Performance

Platform Metrics

Real-time performance metrics and capacity numbers from our production infrastructure.

2.4M
Total Articles
140+
Languages Supported
45K
Requests / Second
12ms
Avg API Latency (p50)
99.99%
Uptime (Annual)
3
Active Regions
24
Microservices
180K
Active Contributors