How Aevum Encyclopedia handles millions of concurrent readers, petabytes of structured knowledge, and real-time AI inference across 6 continents without compromising latency or accuracy.
Our architecture is designed from the ground up to handle exponential growth while maintaining sub-50ms response times and strict data consistency.
Static assets and frequently accessed article fragments are cached at 47 edge locations. Dynamic content uses edge-side includes (ESI) to personalize payloads without hitting origin servers.
Articles are partitioned across distributed document stores using consistent hashing. Range queries and full-text indexing are handled by specialized read replicas with automatic failover.
AI semantic search uses Approximate Nearest Neighbor (ANN) indexing with HNSW. Embeddings are updated asynchronously in batches to keep latency under 80ms for 2M+ vectors.
Kubernetes clusters dynamically provision pods based on CPU, memory, and custom business metrics. Cold starts are mitigated with predictive scaling and spot instance orchestration.
Content updates propagate via Kafka streams to search indexes, CDN invalidation layers, and offline mirrors. Exactly-once delivery guarantees prevent duplicate or stale reads.
Token bucket algorithms protect API endpoints. Challenge-based bot mitigation and WAF rules filter malicious traffic before it reaches application layers.
Every query follows an optimized path designed to minimize origin load and maximize cache hit ratios.
Rate limits & Auth
ESI + CDN Routing
Microservices & Orchestrator
Sharded DB + Vector Index
Our inference pipeline is optimized for high-throughput, low-latency knowledge synthesis without degrading user experience.
We deploy INT8/FP16 quantized models for real-time suggestions, with larger teacher models running asynchronously for deep analysis and fact verification.
Non-urgent enrichment tasks are queued for batch processing during off-peak hours. Interactive features use streaming tokens to maintain perceived responsiveness.
Dynamic GPU allocation across T4, L4, and A10G instances ensures cost-efficient scaling. Preemption-aware workloads prevent expensive idle capacity.
Our engineering team continuously iterates on infrastructure to meet emerging demands.