Inside the architectural hurdles we solved to power 2.4M+ verified articles, real-time AI insights, and sub-100ms semantic search across 140+ languages.
Retrieving contextually relevant results from a corpus of 2.4M articles without sacrificing latency or relevance.
Hybrid retrieval pipeline combining BM25 lexical matching with dense vector embeddings (768-dim). We implemented quantization (FP16 \u2192 INT8) and edge-cached vector shards to bypass cold-start latency.
Maintaining 1.8B+ entity relationships across disciplines without cascading inconsistencies or write bottlenecks.
Event-sourced graph updates using CRDTs for conflict-free replication. Batch compaction runs off-peak, while a dual-write strategy ensures immediate consistency for critical paths.
Preserving technical accuracy and cultural context across 140+ languages without dilution during automated translation or summarization.
Domain-adapted LLM fine-tuning with back-translation validation loops. Expert-in-the-loop queues flag low-confidence translations for human review before publication.
Scaling peer review without creating editorial bottlenecks while maintaining academic-grade citation standards.
Cross-reference AI agents trace claims to primary sources, score confidence intervals, and route edge cases to domain experts. Immutable audit logs track every verification step.
A distributed, event-driven microservices architecture designed for horizontal scaling and fault tolerance.
We're constantly pushing the boundaries of information retrieval, distributed systems, and human-AI collaboration. Explore our open RFCs or join the team.