How we ingest, verify, structure, and serve 2.4M+ encyclopedia articles across 140 languages with sub-100ms latency and 99.99% uptime.
From raw sources to verified knowledge nodes, every piece of data passes through a rigorously engineered pipeline.
Streaming connectors pull from 12,000+ academic journals, open repositories, and contributor APIs in real-time.
→Apache Spark transforms unstructured inputs into canonical schemas, resolving entities and removing duplicates.
→Custom LLMs cross-reference claims against primary sources, flagging conflicts and assigning confidence scores.
→Verified nodes propagate to Neo4j and PostgreSQL, triggering CDN cache invalidation globally.
Battle-tested tools chosen for scalability, observability, and developer velocity.
Our data engineering principles ensure reliability at scale without sacrificing velocity.
Schemas evolve backward-compatibly. We treat data as an immutable append-only ledger until verified.
Edge-cached reads, materialized views, and connection pooling keep p99 latency under 120ms globally.
Every node carries lineage metadata. PII is stripped at ingestion; contributor data is never commingled.
Great Expectations + custom validators run on every pipeline run. Broken contracts halt deployment.
Access our real-time knowledge graph, verified article streams, and semantic search via our public API. Or join the team shaping the future of data at scale.