Send half the tokens. Get better answers. Reduce your agent context token usage on batch, streaming and everything in between for analytics tasks.

Most GenAI projects stall at the same spot: the model doesn't know what your data actually means. A semantic layer fixes that - a definition layer that sits on the database you already have and makes it legible to LLMs and agents. No migration. No new data team to hire.

And it's not just tables anymore. Semantido now covers Kafka streaming schemas, enforces grain and definition quality with built-in lint checks, and separates meaning from deployment so your definitions travel with your data — from warehouse to topic to agent. On one production pipeline, this took text-to-SQL accuracy from ~50% to ~90%, benchmarked against a pure schema-in-context baseline.

I'm a physicist by training, an engineer by trade, and spent the last 14 years building systems and data architectures in banking and capital markets, the kind of environments where "the number is wrong" isn't an option. Creator of the open-source semantic layer semantido.

Explore my long-form articles on leveraging semantic layers, or Book a free consultation

Companies I previously worked with

Deutsche Boerse
HSBC
Capco
MediaMarkt
Bauhaus
Zencore
Deutsche Boerse
HSBC
Capco
MediaMarkt
Bauhaus
Zencore
Deutsche Boerse
HSBC
Capco
MediaMarkt
Bauhaus
Zencore

The semantic layer can now fail your build - semantido v0.5 release.

v0.5 is the release where semantido stops being a descriptor and starts enforcing it. the three features of this release are grain - at which a concept identifies itself, a separate groundings document splitting meaning from deployment bindings and a linter that runs a sqlglot with ten static checks.

AISemantic Layersemantido

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