The Truth Layer for Snowflake AI

Semantiqa is an intelligent infrastructure layer that generates your governed semantic model inside Snowflake — enabling trusted, enterprise-grade intelligence in 1 day

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Autonomous Semantic Intelligence

Enterprise Governance Preserved

Native to Snowflake

Production Ready in 1 Day

⚡

Snowflake AI Ready in 1 Day

Full semantic model · No manual modeling

🔒

Zero Data Movement

Runs entirely inside your Snowflake account

Semantiqa

Live

Setup

Ask

Visualise

Insight

Point at your Snowflake schema

New Registry

Name

Demo_Automated_Trusted_Intelligence

Database

RETAIL_SNOWFLAKE_INTELLIGENCE_DBx v

Schema

ANALYTICSx v

Generate

Reset

No YAML. No manual modeling. No months of consulting.
Select your database and schema — Semantiqa generates the entire governed semantic model automatically.

Any business user · plain English · no SQL

Who are our highest-value repeat store customers in 2002, and how do their purchasing habits differ from one-time buyers?

···

Planning

Planning the next steps

Semantic AI planning underway. Semantiqa routes every question through your governed semantic model — grounded in canonical definitions, not probabilistic guesses. Silence over hallucination. Always.

Question processed entirely inside your Snowflake account — zero data movement

Instant visual intelligence

Governed answer · consistent across Finance, Sales & AI✓ Governed

Deep business insight — not just a chart

$3.44M · $3.29M · $3.23M · $3.13M · $3.08M — ranked, explained, grounded in your governed semantic layer. Every answer traceable. Every metric canonical.

Snowflake Native · Zero data movement

The Problem

Your AI Is Only As Reliable
As the Semantic Layer Behind It.

Modern AI systems are powerful — but without a governed semantic layer inside Snowflake, even advanced AI cannot distinguish interpretation from enterprise truth.

🤖

AI Hallucinates Business Logic

When core metrics like Revenue or Customer aren’t formally defined, AI interprets raw tables without business context.

Result: AI answers become unreliable.

⚔️

Every Team Has a Different Number

Finance, Sales, and Product query the same data but rely on different metric definitions.

Result: Decisions slow. Alignment breaks.

🏗️

Manual Semantic Projects Take Months

Traditional semantic layers require manual modeling, stakeholder alignment, and ongoing maintenance.

Result: Business users wait. Engineers maintain models.

The Solution

A Single System of Meaning

Semantiqa Turns Raw Snowflake Data
Into Governed Enterprise Intelligence.

📐

Standardized Metrics & Dimensions

One canonical definition — across BI, AI, and analytics.

🔗

Auto-Mapped Relationships

Dependency graphs and joins generated directly from your schema — no manual modeling.

🤖

Governed AI Agents

Business questions answered in plain English — grounded in governed semantics, not probabilistic guesses.

Snowflake → Semantiqa → Enterprise Intelligence

🗄️

Your Snowflake Schema

Raw tables, nested views, and columns — meaningful to engineers, opaque to everyone else.

⚙️

Semantiqa Engine

Parses expressions, normalizes metrics, detects conflicts, builds canonical definitions. Governed. Validated. Zero guesswork.

🧠

Governed Semantic Layer

Standardized metrics, dimensions, and relationships — enforced across every query, every tool, every team.

💬

Enterprise Intelligence

Business users ask questions in plain English. AI agents answer with governed accuracy. Real-time. Trusted. Instant.

Why Semantiqa

The Infrastructure Standard for a Governed
Semantic Layer in Snowflake

01 / Accuracy

🎯

Architected for Accurate Enterprise AI

Semantiqa enforces a governed semantic model across every query, tool, and AI workflow. If a metric exists — it is standardized. If a relationship exists — it is preserved. If something is undefined — it is flagged, not fabricated. This is not prompt tuning. It is structural semantic enforcement inside Snowflake.

→ Silence over hallucination. Always.

02 / Security

🔒

Snowflake-Native Semantic Layer. Governed by Design.

Semantiqa runs entirely inside your Snowflake account as a Snowflake Native App. There is no data movement, no external identity system, and no separate governance layer. Your existing RBAC, row-level security, masking policies, and Snowflake controls are inherited automatically. This is enterprise AI governance built into the semantic layer — not added on top.

→ Zero external exposure. Zero re-architecture.

03 / Speed

⚡

Automated Semantic Layer Generation

Semantiqa automatically generates your Snowflake semantic layer in 15–20 minutes. What traditionally requires months of manual metric alignment, YAML configurations, dbt semantic modeling, and ongoing maintenance now happens autonomously — and adapts as schemas evolve. Your Snowflake environment becomes AI-ready the same day.

→ 15–20 minutes vs. 3–6 months. Every time.

04 / Value

📈

Immediate Time-to-Value for Enterprise AI

Install directly from Snowflake Marketplace in minutes. No professional services engagement. No long integration cycle. No semantic consulting dependency. Business users ask questions in plain English from day one — grounded in a governed semantic model. Executives gain trusted answers. Engineering regains capacity. ROI becomes measurable in the first week.

→ From deployment to enterprise intelligence — immediately.

How It Works

From Snowflake Data
To AI-Ready Enterprise Intelligence

Semantiqa doesn't sit on top of Snowflake. It operationalizes semantic intelligence inside Snowflake.

Step 01

📦

Snowflake Schema

Raw tables, nested views, and columns. Structurally defined — semantically fragmented. Inside your Snowflake account.

Step 02

🔬

Semantic Analysis Engine

Semantiqa parses SQL logic, traverses dependency graphs, detects metric conflicts, and maps relationships across schemas. Definitions are normalized. Ambiguities are flagged. Canonical dimensions are constructed.

Step 03

⚖️

Governed Semantic Layer

A complete Snowflake semantic layer is generated automatically with standardized metrics, enforced relationships, and canonical definitions. One system of meaning — across every query, tool, and AI workflow.

Step 04

💬

Enterprise Intelligence Activated

Business users ask questions in plain English. AI agents interpret through governed definitions. Answers are consistent, auditable, and production-ready — not probabilistic guesswork but semantic truth.

What traditionally requires 3–6 months of semantic modeling now happens in a day — inside Snowflake.

Competitive Landscape

Semantiqa Competitive Landscape

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

Snowflake-Complementary. Semantiqa is not a replacement for Snowflake — it creates the governed intelligence layer that Snowflake Cortex, your BI tools, and your AI applications depend on to produce accurate, consistent answers. Governance is not bolted on. It is structural.

Business Impact

Measurable Impact
Across Engineering, Finance, and Business.

Semantiqa transforms Snowflake into a governed semantic layer that delivers immediate operational and financial return — without consulting cycles or re-architecture.

40+ hrs / week

Reclaimed Engineering Capacity From manual semantic modeling and metric reconciliation.

FOR ENGINEERING TEAMS

Up to $500K

Consulting Spend Avoided From 6-month semantic layer buildouts and maintenance cycles.

FOR CFO & DATA LEADERSHIP

1Day

AI-Ready Snowflake. From schema to governed semantic layer — production-ready.

FOR CTO & ARCHITECTURE

Instant

Trusted Business Answers. Plain-English queries grounded in defined metrics — not LLM guesswork.

FOR SALES & BUSINESS TEAMS

FAQ

Questions We Always Get Asked

What exactly is a semantic layer and why does Snowflake need one? +

A semantic layer is the business logic layer that sits between your raw Snowflake data and the people (or AI) querying it. It defines what "Revenue" means, how "Customer" relates to "Order," and which metrics are the canonical versions across your organization. Without it, every team queries the same tables and gets different answers — and every AI model guesses at meanings it doesn't know. Semantiqa creates this layer automatically from your existing Snowflake schemas.

What does "silence over hallucination" actually mean in practice? +

When a business user or AI agent asks a question that touches an undefined metric or an unmapped relationship, Semantiqa surfaces the gap — it flags the undefined concept and prompts your team to define it — rather than generating a confident but wrong answer. This is the opposite of how most AI tools behave. For finance, regulatory, and executive reporting, this distinction is critical: a flagged unknown is recoverable; a confidently wrong answer is not.

How is Semantiqa different from just using Snowflake Cortex? +

Snowflake Cortex provides the AI processing layer — the model that answers questions. Semantiqa provides the semantic foundation that Cortex (and every other AI tool) depends on for accurate answers. Without Semantiqa, Cortex guesses at business definitions from raw schema metadata. With Semantiqa, it has governed, canonical metric definitions to work from. The analogy: Cortex is the engine. Semantiqa is the GPS map. You need both.

Does Semantiqa require any engineering setup or ongoing maintenance? +

Minimal setup, near-zero ongoing maintenance. After a one-time install from Snowflake Marketplace (2–3 minutes), you point Semantiqa at a schema and it does the rest — building semantic views, mapping relationships, deploying AI agents. Crucially, Semantiqa auto-adapts as your schemas evolve, so the semantic layer stays current without manual updates to YAML files or dbt models.

Does my data or schema information leave Snowflake? +

No. Semantiqa is a 100% Snowflake Native App. Every analysis, semantic generation, and AI agent interaction happens entirely within your Snowflake account. Your data never moves to Kloudgen's servers or any external system. This means no security review is required, no NDA is needed, and your CISO doesn't need to approve Semantiqa as a third-party vendor — it operates within your existing Snowflake trust boundary.

Can business users with no SQL knowledge actually use this? +

Yes — that's the whole point of Auto Agent mode. Sales managers, finance analysts, and operations leads can type questions like "Show me revenue by region for last quarter" and get an immediate, governed answer without knowing SQL or waiting for a data team ticket. For analysts and power users who want more control, Select Agent mode allows free-form complex queries with full natural language flexibility.

The Truth Layer for Snowflake AI

Your AI Is Only As ReliableAs the Semantic Layer Behind It.

Semantiqa Turns Raw Snowflake DataInto Governed Enterprise Intelligence.

The Infrastructure Standard for a GovernedSemantic Layer in Snowflake

From Snowflake DataTo AI-Ready Enterprise Intelligence