Colrows

Colrows builds and deploys data AI agents, analytics, reporting, migration, and pipeline root-cause-analysis agents, for enterprises operating on regulated or complex data. Delivery happens through forward-deployed engineering: a Colrows engineer builds the governed infrastructure and agents against the customer's real data, then hands the system over. Underneath is Colrows' own product, a deterministic semantic compiler that resolves natural language into governed, dialect-perfect SQL, so agents answer from agreed business definitions rather than guessing.

The Problem It Addresses

Most AI analytics tools generate SQL by guessing at business terms, producing different answers to the same question depending on how it is phrased. Text-to-SQL benchmarks on complex enterprise schemas (Spider 2.0) show accuracy in the 10 to 21 percent range on end-to-end tasks. Colrows resolves every question against a definition the business has already agreed on, so the same question returns the same answer every time.

The Deployment Model

Colrows deploys through a four-stage engagement: Map (datastores and first business questions), Build (semantic graph, governance layer, and first agents against real data), Ship (agents in production with compile-time governance on every query), and Handover (ownership of the agents, graph, audit trail, and runbooks transfers to the customer). The engagement runs eight weeks, and Colrows does not remain embedded as an ongoing operator after handover.

Governance and Auditability

RBAC, ABAC, row-level, and column-level security are enforced during compilation, not applied afterward, so a user without permission to see a column never has that column enter the query. Every definition is versioned, so any answer can be reproduced exactly as it was given at the time it was asked, supporting audit requirements in regulated industries such as banking and pharmaceuticals.

Autonomous Semantic Graph

The semantic graph builds and maintains itself. Discovery agents crawl databases, catalogs, the BI layer, and query history to propose definitions; a forward-deployed engineer shapes what the crawl proposes, and knowledge approved by an analyst or data owner is ranked as authoritative over anything the system infers.

Conversational Analytics and Cost

A Conversational AI Analyst lets business users ask questions in plain language and get governed answers through the same compiler and governance as any other query. Resolving context before a query reaches a language model has cut token consumption by up to 90 percent on measured enterprise workloads.

Extensibility and Deployment

A Semantic API lets logic defined once be reused across dashboards and agents. Colrows is model-agnostic, MCP-native, supports bring-your-own-LLM, and deploys as SaaS, on-premise, or private VPC.