The Enterprise Knowledge Compiler

Colrows autonomously builds a typed semantic graph across your entire data estate. Every agent query compiles through it. Every join is proven. Every answer is traceable.

Book a demo Start your project

Trusted by engineering teams at

Compiled. Governed. Traceable.

Every query resolves through the same semantic graph. Same joins. Same policies. Same output. No surprises in production.

From intent to verified SQL.

Every query travels the same deterministic path. Context resolution, join validation, policy enforcement, then execution. In that order.

01
Intent Prompt, agent call, workflow trigger
02
Context resolution Resolve meaning from the semantic graph
03
Constrained planning Prove joins · enforce policy · estimate cost
04
Governed execution Dialect-perfect SQL · audited · safe

Versioned. Typed. Multi-scope.

// scope hierarchy
global
  → datastore
      → persona
          → user

Dialect-perfect SQL. Every engine.

One semantic graph compiles to optimized SQL for every warehouse — deterministic across all of them.

Snowflake Databricks Redshift BigQuery Postgres MySQL +10 more

Every surface. Same graph.

Whether it is an agent query, a dashboard render, or a studio edit, everything compiles through the same versioned semantic graph.

Auto-built. Auto-maintained.

Reads from your warehouses, catalogs, BI metric stores, and documentation. Rebuilds the graph automatically as each source changes — no human ticket required.

Datasources

Snowflake Databricks BigQuery Postgres +12

Data catalogs

Alation Atlan Collibra Dataplex

BI & transformation

Power BI dbt Query caches

Documentation

Confluence Wikis PDFs

Continuously synced as schemas, metrics, and sources change

Three storages. One semantic substrate.

Meaning, structure, and behavior — the three layers Colrows compiles every query against. Together, they make the runtime deterministic by construction.

01 Meaning layer

Ontologies

Domain concepts, hierarchies, and definitions — the vocabulary your enterprise actually uses, typed and versioned.

// stored as concept · hierarchy · definition · synonym

02 Structure layer

Semantic knowledge graph

Tables, columns, joins, and relationships — proven paths the compiler navigates to assemble safe, dialect-perfect SQL.

// stored as entity · edge · join_path · cardinality

03 Behavior layer

Statistical profile & usage heuristics

Distributions, value frequencies, and query patterns — the empirical fingerprint that powers drift detection and ranks the right join paths.

// stored as distribution · frequency · access_pattern

Engineered for production. Not pilots.

Governed before execution. Self-maintaining under change. Deterministic across every engine. Each property is non-negotiable in production.

Governed execution

Guardrails before execution. Not after.

Compile-time RBAC + ABAC
Row & column-level predicates
Cost & query-explosion guards
Full audit trail

Autonomous maintenance

The graph compounds. The work doesn't.

Statistical drift detection
Structural diffing
Conflict & duplicate resolution
Schema-change handling

Compiled SQL

Dialect-perfect. Every engine.

16+ data sources
Proven join paths
Optimized per engine
Inline visualization

Deployed. Measured. Compounding.

Across pharma, retail, and finance.

Pharmaceutical

Improved sales performance with data-driven insights.

Retail

There isn't any comparable platform in the market.

BFSI

Automated retail NPA portfolio evaluation using semantic reasoning.

Finance Read more

Stop building context twice.

One graph. Every agent compiles through it. Joins proven, policies enforced, SQL emitted. No context rebuilt from scratch on every call.

Book a demo Follow on LinkedIn