Schemantic.io: automated context layers across clouds and warehouses, built in hours rather than months, so LLMs and analysts get accurate answers from enterprise data.
Enterprise relational data is only as useful to an AI agent or analyst as the context layer above it: what each table means, how tables join, and which entities, events, and attributes they describe. Missing or wrong, that layer caps accuracy no matter which model runs underneath.
Anthropic's June 2026 evaluation of its self-service analytics agent ("How Anthropic enables self-service data analytics with Claude") reports accuracy at 21% without a structured context layer and above 95% with one. A stronger model does not close the gap, because the manual work is the context layer itself. Raw retrieval over thousands of prior queries moved accuracy less than one point. Anthropic places the remedy in data foundations, not the model: dimensional modeling, shift-left testing, and freshness and completeness checks on critical pipelines.
Schemantic produces that foundation automatically: schema, statistics, hygiene, descriptions, lineage, joins, entities, events, and attributes. Seven of the nine are generated; events and attributes arrive as prioritized proposals for review. A full context layer is ready in tens of hours.
At Alaska Airlines, Schemantic was run by Robin Yong, Director of Safety and Audit Analytics, where data accuracy carries safety- and audit-grade stakes. Their principal architect scoped a warehouse migration at 700 hours with no semantic layer. With Schemantic, the migration plus a full context layer across hundreds of tables completed in roughly 100 hours. They report 30 to 50% less time interpreting and validating data while supporting three times the coverage at flat effort.
Schemantic also finds errors, including underutilization. On BEAVER, a benchmark of real-world warehouses curated by database experts and coordinated by MIT, Schemantic connected more tables, qualified more joins, overcame more data mutations, and caught more than 200 errors in the benchmark's own expert-verified answer key.
Single-cloud, single-warehouse deployments run with zero raw-data egress. Some clouds support multi-warehouse with no egress; multi-cloud requires egress. Palantir FDE leadership cited the privacy-first design for privacy-sensitive multi-warehouse work with defense clients. Schemantic runs on Redshift, BigQuery, Fabric, Snowflake, and Databricks for single-warehouse deployments. In June 2026, a beta spans multiple warehouses and clouds with one unified, automated context layer.
