Semantiqa autonomously creates, coordinates and operates the semantic intelligence required for enterprise analytics and AI — across databases, schemas, business domains and environments.
As AI expands across schemas, domains and teams, business meaning becomes a continuous engineering and operating challenge — not a one-time modeling exercise.
Hundreds of tables, schemas and domains turn semantic engineering into recurring enterprise work.
Semantiqa autonomously discovers, builds and enriches complete business meaning across enterprise data.
Enterprise questions cross schemas and business domains while definitions and context are distributed across the enterprise.
Connect business meaning across schemas, databases and domains as one coordinated semantic system.
The governed semantic context needed for one business request may be completely different from the next.
Interpret each request and dynamically select the governed semantic context relevant to that question.
Metrics, relationships, policies and definitions evolve. Production semantics need controlled change.
Manage semantic change with validation, version control and controlled promotion across environments.
Semantiqa combines autonomous semantic engineering with human control and enterprise lifecycle management — natively in Snowflake.
Autonomously discover business structure and create facts, dimensions, metrics, relationships and cross-domain context across selected Snowflake schemas and domains.
Create native Snowflake Semantic Views as part of the broader semantic estate — preserving reusable Snowflake-native assets for Cortex and other Snowflake AI experiences.
Combine autonomous creation with human business judgment. Review semantic definitions, enrich context, validate behavior and resolve conflicts before approved semantics become production assets.
Manage semantic assets through controlled versions, publishing and promotion across DEV, TEST and PROD — without rebuilding the semantic estate for every environment.
Semantiqa manages the complete semantic lifecycle — from autonomous discovery and creation to governed production operation.
Semantiqa helps teams create, manage, validate, and publish governed semantic models for Snowflake. It connects business metrics, source metadata, governance terms, and AI-powered analytics in one reusable semantic registry.
| Snowflake Semantic Model YAML | Upload Snowflake Semantic Model YAML files and preview changes before applying them to your registry. |
| Metadata Refresh and Schema Drift | Refresh registry metadata from Snowflake to detect schema changes and update semantic definitions safely. |
| Governance Metadata Enrichment | Connect Collibra metadata to enrich your semantic registry with glossary terms, ownership, and classifications. |
| Conflict Review | Resolve semantic model conflicts when incoming metadata cannot be merged automatically. |
| Validation and Publishing | Validate semantic views before publishing them for chat, dashboards, and analytics workflows. |
| Access Control | Control access with application roles, registry permissions, and Snowflake object privileges. |
| SemanticOps Promotion | Version, publish, and promote governed semantic definitions across development, test, and production environments. |
Capability comparison across setup time, architecture, governance, and operational maintenance.
| Capability | Semantiqa | Manual Modeling Tools | External Semantic Layers | Text-to-SQL Only |
|---|---|---|---|---|
| Setup Time | ✓ 15–20 Minutes | Days to Weeks | Weeks to Months | Hours to Days |
| Native to Snowflake | ✓ Yes — 100% Native App | ⚠ Varies | ✗ No (External SaaS) | ⚠ Varies |
| Auto-Generated Views | ✓ Fully Automatic | ✗ Manual Config | ✗ Manual Config | ✗ No Semantic Layer |
| Dual User Modes | ✓ Auto + Select Agent | ✗ Single Mode | ✗ Single Mode | ✗ Single Mode |
| Data Movement | ✓ Zero — stays in Snowflake | ⚠ Varies | ✗ Required | ⚠ Varies |
| Semantic Governance | ✓ Built-In, Structural | ✓ Yes | ✓ Yes | ✗ Limited |
| Maintenance Required | ✓ Auto-Adapting | ⚠ High (YAML/Code) | ⚠ Medium | ⚠ Medium |
Yes. Semantiqa supports controlled promotion of governed semantic models across development, test, and production environments. Teams can version and publish a registry in one environment, export it, and import it into the next environment without rebuilding the semantic model.
Before promotion, changes can be reviewed, validated, and approved, helping maintain consistent semantic definitions across environments while reducing configuration drift.
Semantiqa combines autonomous semantic creation with human review at the points where business judgment is required. When new metadata, imported semantic models, governance definitions, or schema changes introduce conflicting information, Semantiqa identifies the conflict instead of automatically overwriting an approved definition.
Authorized users can review the proposed changes, compare definitions, edit or enrich metrics and business terms, resolve conflicts, and validate the resulting semantic behavior. Once approved, the updated version can be published and promoted through development, test, and production under controlled permissions.
This allows Semantiqa to automate semantic engineering at scale while keeping people responsible for business meaning and production approval.
Semantiqa coordinates six complementary layers of semantic intelligence:
Together, these layers provide more than a static semantic model. They create a governed semantic-intelligence system that can support analytics, AI applications, and cross-domain enterprise questions.
Semantiqa allows the same governed semantic intelligence to be reused across analytics, AI, and application experiences. Depending on the integrations enabled in the customer environment, it can be consumed through: