Provide your AI models with the necessary context to avoid hallucinations and deliver reliable answers that align with your business reality.
Consolidate scattered models from your BI tools, data warehouses, and analytics platforms into one consistent source of truth.
Convert raw data into business insights by automatically connecting technical metadata with your organization’s domain knowledge.
1. Extract raw intelligence
Capture all metadata—tables, columns, SQL schemas, and descriptions—to build a complete foundation for your semantic layer.
2. Unify business logic
Analyze SQL query behaviors and BI tool usage to identify key relationships, calculations, and business definitions while filtering noise.
3. Generate semantic model
Automatically transform your enriched business context into a standardized semantic model ready for AI tools like Snowflake Cortex.
Build a trustworthy semantic layer automatically
Extract, normalize, and enrich technical metadata to identify entities, metrics, and relationships. Generate a standards‑based semantic model ready for AI tools and downstream analytics.
- Auto‑generate entities, metrics, and relationships.
- Leverage existing schemas, queries, and usage.
- Serve a standards‑based model for AI & BI.
- Link semantics to lineage for end‑to‑end traceability.
- Reduce manual YAML to accelerate time‑to‑value.
Unify business logic across tools
Merge BI, SQL, and platform definitions into a single governed model. Use real query and dashboard usage to reconcile duplicates and align calculations.
- Unify definitions across BI, SQL, and platforms.
- De‑duplicate measures and dimensions
- Map synonyms to canonical terms.
- Certify and assign ownership to approved logic.
- Bi-directionally link knowledge to assets for traceability.
Govern, version, and evolve at scale
Manage the semantic layer with the same rigor as code. Propose changes, test impacts, and approve updates with full auditability.
- Run Git‑native reviews, versioning, and approvals.
- Validate changes with pre‑change tests and impact analysis.
- Enforce contract checks to prevent breaking changes.
- Enforce RBAC/ABAC with policy templates.
- Surface SLA and freshness in the Catalog.
Power AI and analytics with business context
Ground LLMs and agents in the same semantics your business trusts. Serve consistent definitions to AI, BI, and ad hoc analysis from a single, governed source.
- Power AI assistants via a semantic API.
- Ground LLM answers in trusted definitions.
- Map natural‑language questions to metrics and entities.
- Provide citations to glossary and lineage.
- Enforce row/column rules downstream.
A governed model of your business entities, metrics, and relationships that standardizes logic across AI, BI, and analytics.
It ingests metadata and usage patterns (schemas, queries, BI usage), infers relationships and definitions, and generates a standardized semantic model.
It grounds LLMs/agents with trusted definitions and context, improving answer accuracy and consistency while providing citations and lineage.
Yes—Coalesce consolidates and de‑duplicates measures/dimensions from BI and warehouse queries into one certified source of truth.
Use Git‑native reviews, impact analysis, lineage, and tests to approve updates with full audit trails and rollback.
Coalesce works across Snowflake, Databricks, Microsoft Fabric, and BigQuery, with a consistent catalog and governance experience.
Yes—RBAC/ABAC, masking, and policy templates ensure least‑privilege access; search and AI endpoints honor these controls.
Absolutely—natural‑language search maps questions to certified metrics and entities, with plain‑language context and ownership.
Timelines vary by environment and scope, but automation and reuse of existing metadata accelerate rollout.