Tacnode Context Lake is a PostgreSQL-compatible context infrastructure platform that gives AI systems, services, and agents access to the shared, live context they need at decision time — from a single system, under one consistent snapshot. Enterprise data stacks today average ~5 different infrastructure technologies. The result: fragmented context, stale features, and AI decisions made against data that was already outdated by the time it was read. According to a Forrester Consulting study commissioned by Tacnode (February 2026), 60%+ of surveyed leaders report using 40% or less of their available data within a useful timeframe, and 70% say fewer than 20% of AI proofs of concept reach production within six months. Tacnode Context Lake solves this by unifying data ingestion, transformation, and retrieval into a single transactional system. Incremental materialized views keep derived context — velocity features, risk signals, aggregated balances, embeddings — continuously current without external stream processing. All retrieval patterns (point lookups, range scans, aggregations, vector search) are served from one system under one consistent snapshot, eliminating the propagation lag and consistency gaps that plague composed data stacks. Key capabilities include sub-millisecond point lookups at 10K+ queries/sec, incremental materialized views that replace Flink/Spark jobs with SQL, native Apache Iceberg integration for analytics consumers, PostgreSQL compatibility with existing tooling and ORMs, and separation of storage and compute for independent scaling. Tacnode Context Lake is trusted by organizations in financial services, gaming, e-commerce, and AI infrastructure for use cases including real-time fraud detection, dynamic pricing, portfolio tracking, and AI agent coordination.