A Semantic Layer for Enterprise AI Self-healing, always current

A semantic execution layer sits between your data warehouse and the applications, agents, and dashboards that query it. It encodes business meaning - definitions, relationships, metrics, and governance policies - in one versioned place so every consumer queries the same logic. Colrows extends this into a semantic execution layer: it autonomously builds the graph, then compiles every agent intent into governed, deterministic, dialect-perfect SQL. Read the full pillar guide.

Yes - Colrows is a semantic layer platform, with one architectural difference: it is a semantic execution layer. Traditional semantic layers (dbt Semantic Layer, Cube, AtScale, LookML) hold hand-authored definitions that BI tools read at presentation time. Colrows builds its semantic graph autonomously and compiles every query through it at execution time - joins proven, RBAC/ABAC enforced, dialect-perfect SQL emitted for 16+ engines.

Most semantic layer tools depend on a human team to author and maintain the model. Colrows is the semantic layer that builds itself: it reads warehouses, catalogs, BI metric stores, and documentation, keeps the graph current with drift detection, and executes - compiling intent into governed SQL rather than serving definitions for other tools to interpret. Deployed at Cipla (8× data adoption), SSP Group, and a confidential BFSI ARC (>95% faster evaluation cycles).

Cube.js, dbt Semantic Layer, and Looker resolve meaning at presentation time, primarily for human consumers querying through APIs or BI tools. Colrows resolves meaning at compile time, across any warehouse, for AI agents. The semantic graph is autonomously built and maintained, join paths are formally proven, and RBAC, ABAC, and row/column-level policies are enforced before any SQL leaves the planner. Output is dialect-perfect SQL for 16+ engines, not a metric API. Existing dbt metric definitions can be ingested into the graph as a starting point. See full comparisons: vs Cube.js, vs dbt Semantic Layer, vs Looker, vs AtScale, vs ThoughtSpot.

Yes. Colrows reads from Snowflake, Databricks, BigQuery, Redshift, Postgres, MySQL, ClickHouse, Trino, and 10+ more datasources. The compile pipeline emits dialect-specialised SQL per engine, so the same agent intent produces optimised, valid SQL whether it runs on Snowflake or Databricks.

Row-level security, RBAC, ABAC, and column-level predicates are evaluated at compile time, before any SQL touches your warehouse. Every query is compiled with the user's identity, role, and attributes attached; the planner injects the relevant predicates into the generated SQL. Filtered-out rows are never read - governance is structural, not advisory. Unauthorised queries fail compilation, not in production.

Yes. Colrows is LLM-agnostic and framework-agnostic. Any agent that can call an HTTP API or a JDBC endpoint can use Colrows as its execution layer. The agent passes intent; Colrows returns governed SQL or executed results, grounded in the typed semantic graph.

Connecting a datasource and auto-building the initial semantic graph takes hours, not weeks - Colrows runs an introspection pass and proposes mappings you can edit before publishing. Production rollouts in regulated environments (SSO, policy authoring, validation against existing definitions) typically run in weeks, not months, depending on environment complexity. Shared, dedicated, and fully private VPC deployments are available across AWS, Azure, and GCP.

Out of the box: Snowflake, Databricks, BigQuery, Redshift, Postgres, MySQL, ClickHouse, Trino, and more - 16+ engines in total. Colrows also connects document sources - AWS S3, Google Drive, and Microsoft OneDrive - and reads uploaded PDF, DOCX, Markdown, and text files, so you can ask questions of your documents with cited answers alongside your warehouse data. For exotic in-house engines, the dialect adapter is pluggable.

Yes. Connect AWS S3, Google Drive, or Microsoft OneDrive, or upload PDF, DOCX, Markdown, and text files, and ask fact, comparison, and summary questions. Every answer quotes the exact passage it came from, and Colrows says when the evidence is not there rather than guessing. Complete queries read every document in a collection to count, sum, or list matches, and each document's source permissions are enforced on every request. See Document Intelligence.

The same model that scores about 91% on an academic benchmark like Spider 1.0 drops to roughly 21% on Spider 2.0's real enterprise schemas - hundreds of columns, ambiguous names, undocumented joins, and governance rules. The failure is not the model; it is missing context. A semantic execution layer closes that gap by compiling questions against a governed semantic graph instead of guessing, so the query is proven before it runs. The full evidence is in the enterprise text-to-SQL accuracy benchmark.

The right layer for agents is the one that enforces governance before execution and compiles to deterministic, dialect-perfect SQL across every engine your agents touch - not one that retrieves context and hopes. BI semantic layers suit human dashboards; warehouse-native surfaces suit single-warehouse estates; a semantic execution layer is the structurally-correct answer when agents issue high-volume queries across multiple sources under audit. The 2026 buyer's guide walks through each scenario.

Colrows retrieves, and it holds retrieval to the same standard as SQL. A data question compiles through a typed semantic graph into governed, deterministic SQL, with the join path proven and access control enforced at compile time. A document question returns claims that each quote their source, or an explicit answer that the evidence is not there. Neither one guesses, and both are reproducible - where plain RAG feeds retrieved text to a model that still writes SQL probabilistically, Colrows compiles data questions and verifies document answers against the source.