What's new in Colrows.

Semantic datasets, composable metrics & analytics-platform ingestion

The semantic model gains semantic datasets, composable and SQL-based metrics with grain, and semantic personas. Metadata discovery and the AI Assistant reach more platforms, and pipelines become repairable and safely queued.

• Semantic datasets (authored SQL, source linkage, publishing, validation) and composable, SQL-based metrics.
• Analytics-platform ingestion: Apache Superset, Power BI, and Unity Catalog, plus Dremio query history.
• Reusable embedded AI Assistant with organization-aware context and token tracking.
• Full and incremental pipelines with queueing, repairable projections, and a new DEGRADED state for partial failures.

Heads-up: the new DEGRADED pipeline state and remodeled metric/persona fields are breaking; test Superset, Power BI, or Unity Catalog connections before scanning.

Governed agent access, documents & unified analytics

Colrows MCP ships as the supported surface for agent access to the semantic layer, alongside a durable document-processing pipeline and a unified AI Analyst cache for repeat queries.

• Colrows MCP server: REST endpoints, OAuth discovery, tool-call continuation, and governed metadata and data access.
• Document upload and a durable processing pipeline on S3.
• Unified AI Analyst data cache with configurable eviction and refresh.
• analyze_data renamed to ask_data; Workspace merged into the SQL Editor.

Heads-up: MCP replaces the retired CLI clients; update callers of analyze_data and regenerate join-discovery clients. The organization CLI no longer creates a database automatically.

Semantic platform re-architecture

The semantic core was rebuilt for durability and scale, with a single MongoDB Atlas vector store and a durable consensus control plane governing how meaning is generated and versioned.

• One MongoDB-backed vector collection spanning actions, events, entities, and categories.
• Signal → Claim → Proposal → Asset workflow bounded by immutable semantic scopes.
• Durable consensus service with source scanners for profiles, query history, catalog metadata, and joins.
• New catalog connection framework; MindsDB, Dremio, and Databricks onboarding.

Heads-up: unscoped legacy runs are rebuilt, Business Term Anchor becomes TableHint, and bundled MySQL and Snowflake drivers must now be supplied explicitly.

Multi-vector embeddings & expanded dialect support

Concept lookup moves to multi-vector embeddings per entity (definition, usage, combined), and dialect compilation extends to ClickHouse and Trino.

• Up to 3× faster compile-time policy evaluation on graphs with 10K+ entities.
• Join-path-proof edge cases resolved across 4+ datasets with ambiguous cardinality.

Compile-time governance, GA

RBAC and ABAC policies are materialized into the execution plan at compile time. A persona's allowed subgraph is resolved before any SQL is generated, so unauthorized queries fail compilation instead of running.

• Row and column-level predicates compiled per persona.
• Point-in-time reproducible audit log for any historical query.
• Query-explosion guards: ambiguous joins fail compilation with clear errors.

Conversational AI Analyst

Natural-language questions compile through the semantic graph into deterministic, governed SQL, and every answer ships with its reasoning chain rather than a probabilistic guess. Slack integration included.

• Stateful conversational scope: follow-ups resolve against the same graph version and policy context.

Semantic Studio

A visual editor brings definitions, relationships, and governance rules into one plane, with multi-scope inheritance from global to datastore to persona to user.

• Autonomous drift detection and conflict resolution across thousands of entities.
• Versioned graph with structural diffing for rollback and point-in-time reproducibility.

16+ data sources & dialect-perfect SQL

Support for Snowflake, Databricks, BigQuery, Redshift, Postgres, MySQL, and 10+ more, with dialect-perfect SQL per engine and no data movement.

• Inline visualization and query-plan introspection before execution.

Colrows Semantic Execution Layer - GA

The runtime launches with four isolated stages (intent parsing, semantic resolution, constrained planning, governed execution) over a typed, multi-scope, versioned knowledge graph. The first AI Data Analyst Agent compiles natural-language questions into deterministic, auditable SQL.