Semantiqa · Semantic Engineering Platform

Semantic Engineering for Enterprise AI in Snowflake

Autonomously engineer, govern, and evolve enterprise semantic models across schemas, domains, and environments.

Semantic Intelligence in ONE DAY
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Semantiqa semantic intelligence workflow
Watch Semantiqa Created by Kloudgen and approved through Snowflake
Cross-Domain Semantic Intelligence

Connect Business Meaning Across Domains

Semantiqa connects metadata, relationships, and query history across schemas and business domains into one governed semantic model.

"Which products have declining revenue, increasing support incidents, and worsening delivery performance?"
Sales + Product + Support + Logistics. One governed semantic model.
Cross-schema · Cross-database · Cross-domain
Cross-domain semantic intelligence diagram
Also spans: Customer · Finance · Operations · Inventory
Autonomous + Human Governed

Create at Machine Scale. Govern with Business Judgment.

Step 01

Discover & Create

Autonomously discover business structure and create the semantic model across selected Snowflake data.

FactsDimensionsMetricsRelationships
Step 02

Enrich

Add enterprise knowledge and business context to strengthen the semantic model.

GlossariesGovernance MetadataDocumentsPolicies
Step 03

Review & Resolve

Put human judgment in control when definitions or business meaning conflict.

ReviewEditResolveApprove
Step 04

Publish

Publish only the approved semantic state while preserving changes as controlled versions.

VersionApprovePublish
Autonomous creation does not mean uncontrolled creation. Semantiqa combines machine-scale semantic engineering with human business judgment.
Snowflake-Native Architecture

Semantic Engineering
Inside Snowflake

Semantiqa runs as a Snowflake Native App — keeping semantic engineering inside your Snowflake environment.

Zero Data Egress

Data stays inside Snowflake.

Native Execution

Semantic processing runs inside Snowflake.

Snowflake Governance Alignment

Works with RBAC, RLS, masking, and permissions.

Native Semantic Assets

Creates reusable Snowflake Semantic Views.

Built on Snowflake. Engineered by Semantiqa. Snowflake provides the native data, semantic, governance, and AI foundation.
Complete Semantic Engineering Architecture

Six Coordinated Layers. One Governed Semantic System.

Semantiqa autonomously creates and coordinates six layers of semantic intelligence across schemas and domains — ready for enterprise analytics and AI.

1
Native Semantic ViewsSnowflake-native semantic assets representing governed dimensions, facts, metrics, and relationships.
2
Semantic ModelFacts, dimensions, metrics, joins, relationships, synonyms, definitions, and business logic.
3
Ontology & ContextOrganizes business concepts and relationships and connects meaning across domains.
4
Enrichment LayerAdds enterprise knowledge from metadata, query history, documents, glossaries, policies, and governance platforms.
5
Agentic LayerMakes governed semantic intelligence available to AI applications, Snowflake experiences, and MCP-connected agents.
6
Intent LayerInterprets each request and determines which governed semantic context is relevant.
The Semantiqa Registry

The Governed Enterprise
Semantic Model

The Semantiqa Registry is the single governed model your enterprise maintains as the trusted definition of business meaning — versioned, approved, and ready to use.

Semantiqa Registry diagram
Enterprise-Owned by Design A single managed semantic model for approved enterprise business meaning.
The Semantiqa Lifecycle

From Semantic Creation to Enterprise SemanticOps

Semantiqa manages the complete semantic lifecycle — from autonomous discovery and creation to governed production operation.

01
Discover
Metadata · Query History · Structure
02
Build
Metrics · Relationships · Semantic Views
03
Enrich
Definitions · Documents · Policies
04
Validate
Questions · SQL · Business Rules
05
Version
Draft · Compare · History
06
Promote
DEV → TEST → PROD
07
Operate
Audit · Monitor · Rollback
Human-in-the-Loop Governance
Conflict Resolution · Review & Approval · Publishing · Promotion Controls
Autonomous
Create semantic intelligence at enterprise scale.
Controlled
Validate and govern change before production.
Operational
Version, promote and evolve semantics continuously.
Build once. Govern continuously. Deploy everywhere. Turn semantic meaning into a governed, versioned and deployable enterprise asset. Explore Enterprise SemanticOps →
Reuse Governed Semantic Intelligence

One Governed Semantic Foundation. Across Enterprise AI.

Semantic intelligence engineered and governed by Semantiqa can be reused across multiple AI, analytics, and application experiences rather than recreated for each one.

Semantiqa Chat

Governed natural-language analytics with SQL, lineage, and supporting data.

Self-Service Analytics

Persistent semantic context across interactive dashboards and follow-up analysis.

Governed Semantic Foundation

Snowflake AI Experiences

Native Semantic Views created by Semantiqa remain available to Snowflake AI experiences.

MCP-Connected Agents

External agents can request governed semantic context through Semantiqa's MCP interface.

APIs & Applications

Semantic intelligence can be exposed to applications and workflows programmatically where supported.

Infrastructure for Every Enterprise AI Experience. Semantiqa's governed semantic intelligence powers Chat, dashboards, Cortex, and external agents — all from a single source of truth.
Snowflake Semantic Model Management

Build, Validate, and Publish Snowflake Semantic Models.

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 external governance and glossary metadata to enrich your semantic registry with 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.
Competitive Landscape

Semantiqa Competitive Landscape

Capability comparison across installation time, architecture, cross-domain modeling, and governance.

Capability Semantiqa Manual / Custom Semantic Modeling External Semantic Platforms Text-to-SQL Tools AI Agent / Copilot Tools
App Installation Time ✓ 15–20 Minutes Days to Weeks Weeks to Months Hours to Days Varies
Semantic Intelligence in ONE DAY ✓ Core Semantiqa promise Project dependent Project dependent Not comparable Not comparable
Cross-Domain Semantic Model ✓ Across schemas, databases & domains Possible, but manually engineered Varies by implementation Depends on supplied semantic context Depends on supplied semantic context
Zero Data Egress ✓ Data stays in Snowflake Depends on implementation ✗ Often requires data movement Depends on architecture Depends on architecture
Native Snowflake Semantic Views ✓ Generated and reusable Can be manually created Varies Consumes existing semantic assets Consumes existing semantic assets
Human-in-the-Loop Governance ✓ Review, enrich, resolve & approve Process dependent Varies Limited / outside core function Limited / outside core function
DEV → TEST → PROD Promotion ✓ Governed promotion without rebuilding Custom deployment process Varies Usually outside core function Usually outside core function
Snowflake Governance Alignment ✓ RBAC / RLS / masking Depends on implementation Depends on architecture Depends on implementation Depends on implementation
Semantic Intelligence, Native to Snowflake. Consistent, governed, and trusted — reusable across BI, analytics, and AI.
FAQ

Questions We Always Get Asked

What exactly does Semantiqa create? +
Semantiqa creates and coordinates native Snowflake Semantic Views, the broader semantic model, ontology and cross-domain context, enterprise enrichment, agentic access, and intent-driven semantic context as one governed semantic system.
How does Semantiqa build cross-domain semantic models? +
Semantiqa analyzes selected Snowflake schemas, metadata, query history, relationships, and enterprise context to discover semantic structure and create relationships across schemas and business domains.
Can Semantiqa manage existing semantic assets and YAML? +
Yes. Existing semantic assets can be incorporated into the Semantiqa registry and managed through preview, enrichment, conflict review, validation, and lifecycle controls.
How does human-in-the-loop governance work? +
Semantiqa surfaces changes and conflicts for authorized users to review, edit, enrich, approve, merge, or reject before approved semantics become published enterprise state.
Can semantic models be promoted between environments? +
Yes. Approved and published semantic registries can be promoted across DEV, TEST, and PROD rather than rebuilt manually.
Does Semantiqa run inside Snowflake? +
Yes. Semantiqa is a Snowflake Native App designed to execute within the customer's Snowflake environment.
Does customer data leave Snowflake? +
No. Semantiqa is designed so customer data does not need to move to an external semantic SaaS environment.
Can other AI agents use Semantiqa? +
Yes. Governed semantic context can be consumed through Semantiqa experiences, Snowflake-native experiences, MCP-connected agents, and APIs where supported.
What are Semantiqa's six coordinated semantic layers? +

Semantiqa coordinates six complementary layers:

  1. Native Semantic Views are Snowflake-native semantic assets representing governed dimensions, facts, metrics, and relationships.
  2. Semantic Model covers facts, dimensions, metrics, joins, relationships, synonyms, definitions, and business logic.
  3. Ontology & Context organizes business concepts and relationships and connects meaning across domains.
  4. Enrichment Layer adds enterprise knowledge from metadata, query history, documents, business glossaries, policies, and governance platforms.
  5. Agentic Layer makes governed semantic intelligence available to AI applications, Snowflake experiences, and MCP-connected agents.
  6. Intent Layer interprets each request and determines which governed semantic context is relevant.

These six layers are created and coordinated together as one semantic system, not a rigid sequential stack.

What are Semantiqa's five validation dimensions? +
Technical, Business, Security & Compliance, Performance & Scale, and Governance & Operations — each change is checked against all five before it's published or promoted.
What does "Semantic Intelligence in ONE DAY" mean? +
It means Semantiqa can create the enterprise semantic intelligence and model within that timeframe, based on product and design-partner experience supporting the claim. It does not mean every enterprise review, approval, or production-deployment process completes in one day.
Can Semantiqa generate dashboards from natural language prompts? +
Yes. Semantiqa connects governed semantic definitions to AI data analytics, enterprise data chatbot workflows, and AI dashboard builder experiences so answers and dashboards share the same business logic.
See Semantiqa in Action
See Semantiqa Build
Your Semantic Model
Live.
Bring a representative Snowflake schema or cross-domain use case and see how Semantiqa discovers semantic structure, creates governed business meaning, and applies enterprise lifecycle controls.
Cross-Domain Semantic Engineering
Snowflake-Native Execution
Human-in-the-Loop Governance
Versioning & DEV → TEST → PROD Promotion
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