Semantic Layer Intelligence

One Semantic Layer.
Every Platform.

SemaBridge eliminates semantic drift across your data ecosystem. Extract business logic from any BI tool or data platform, normalize it into a portable canonical standard, and deploy it anywhere — automatically, securely, and within your own environment.

Request a Demo → See How It Works
Live Semantic Sync
Power BI
Power BI
→
Tableau
Tableau
→
Qlik
Qlik
→
SemaBridge
Canonical Semantic Model · Open Standard
→
Snowflake
Snowflake Semantic View
→
Databricks
Databricks Metrics Views
→
Microsoft Fabric
Fabric Semantic Model
100%
No Lock-in
0
Secrets Stored
∞
Extensible
The Problem

Your Business Logic Is Breaking Down

Enterprises running multiple BI tools and cloud data platforms are accumulating a silent but critical liability — fragmented, contradictory semantic definitions that erode trust in data.

⚠️

Semantic Drift & Report Inconsistency

"Revenue," "Churn Rate," "Active Users" — the same metric returns different values depending on which tool a stakeholder opens. No single source of truth. Decisions are made on contradictory data.

🔀

Segregated Platforms, Siloed Logic

Power BI, Tableau, Snowflake, and Databricks each maintain their own private semantic definitions. There is no cross-platform consistency layer, leaving business logic permanently fragmented across your data estate.

🔒

Vendor Lock-in Stifles Modernization

Proprietary semantic layers hold your business logic hostage. Migrating to a modern data cloud means months of manual re-engineering — expensive, error-prone, and deeply disruptive to the business.

🛠️

Manual Migrations Destroy Value

Developers painstakingly hand-translate measures, dimensions, hierarchies, and relationships for every platform change. No automation, no traceability, no guarantee the logic survives intact.

🤖

AI Agents Lack Semantic Grounding

Without a governed semantic layer, AI agents and Copilots query raw tables with no business context. They hallucinate metrics, misinterpret dimensions, and produce answers that conflict with your BI reports.

📋

No Audit Trail for Business Logic

When semantic definitions change, there is no record of what changed, when, or why. Compliance teams cannot trace metric lineage. Regulated industries face significant exposure without an immutable logic audit trail.

The Solution

Canonical Semantics. Zero Manual Work.

SemaBridge introduces a canonical semantic layer — SML (Semantic Modeling Language) — as the universal passport for your business logic. Extract once. Deploy anywhere.

⚙️

Configure

Define source, target and auth via a secure YAML config. Zero secrets stored anywhere.

📥

Extract

Connect to your BI tool or data platform and extract the full semantic model metadata.

🔄

Normalize

Convert proprietary formats into canonical SML/OSI — open, versioned, and portable.

✅

Validate

Detect lossy mappings and unsupported features before deployment. Nothing breaks silently.

🚀

Deploy

Emit to any target platform with full traceability. Every run is versioned and auditable.

8+
Platform Adapters
100%
Secure Data Boundary
∞
Immutable Audit Trail
0
Manual Re-engineering
Adapters

Connect Your Entire Data Ecosystem

Pre-built, production-ready adapters for the world's leading BI tools and cloud data platforms. Each adapter speaks the platform's native language — and translates it to SML.

Cloud Data Platforms

Modern Data Clouds

Deploy canonical SML models as native semantic objects on the world's leading cloud data platforms — no re-engineering required.

Snowflake Semantic View
OSI-native Semantic Views, dimensions, facts, metrics, Cortex integration
Source / Target
Databricks Metrics Views
Unity Catalog metrics views, Delta tables, Genie AI integration
Source / Target
Fabric Semantic Model
Microsoft Fabric semantic models, OneLake, workspace REST API
Source / Target
Data Transformation

Semantic Layer Tools

Bridge the metric layer with transformation frameworks — carry governed business definitions from your dbt or Cube models directly into any target platform.

dbt Semantic Layer
dbt metrics, dimensions, semantic manifest, MetricFlow definitions
Source / Target
Cube Semantic Layer
Cube.dev metrics, dimensions, joins, pre-aggregations, REST & GraphQL API
Source / Target
BI and Analytics

Visualisation Platforms

Extract rich semantic models — measures, dimensions, hierarchies, DAX expressions, relationships — from your existing BI investments.

Power BI
Semantic models, TMSL, DAX measures, row-level security, REST API
Source / Target
Tableau
Published data sources, calculated fields, relationships, LOD expressions
Source / Target
Qlik
Data model scripts, master items, set expressions, associations
Source / Target
Agent-Ready Semantics

Power AI Agents with Trusted Business Logic

The SML canonical layer doesn't just bridge platforms — it feeds AI agents with verified, governance-approved semantic context. No hallucinations. No ambiguity.

🤖

Agent Integration Layer

SemaBridge outputs SML/OSI that is natively consumable by the leading agentic AI frameworks — giving agents the grounded business context they need to reason accurately.

Databricks Databricks

AgentBricks

Deploy SML-normalized semantic models directly into Databricks AgentBricks. Your business metrics become the grounded knowledge layer for Mosaic AI agents — ensuring every query resolves to a governed, consistent definition.

Unity Catalog semantic grounding
Governed metric resolution for AI queries
Delta table & Genie AI context injection
Snowflake Snowflake

Snowflake Cortex

SemaBridge outputs Snowflake-native Semantic Views (OSI) that power Cortex Analyst — Snowflake's natural language-to-SQL AI. Agents answer business questions using the same metric definitions as your BI dashboards.

Semantic View auto-generation from SML
Cortex Analyst metric grounding
NL-to-SQL with governed business context
Microsoft Fabric Microsoft

Fabric Data Agents

Feed Microsoft Fabric AI skills and Data Agents with SML-normalized semantic models from OneLake. Copilot and agentic workflows gain access to certified, cross-platform business logic — not raw table schemas.

OneLake semantic model integration
Fabric Copilot context enrichment
Cross-workspace metric consistency
Why SemaBridge

Built for the Enterprise

Every design decision in SemaBridge is driven by the real operational, security, and compliance demands of enterprise data teams.

🔗

Seamless Cross-Platform Interoperability

Extract from one platform and deploy to another in a single automated pipeline. No manual re-engineering, no guesswork — the same business logic, everywhere.

📜

Immutable Audit Trail

Every execution is tracked with a Project ID and Run ID. Every artifact — SML, conversion reports, source metadata — is permanently logged, creating a tamper-proof flight recorder for compliance teams.

🏛️

Open Standards. No Lock-in.

SML (Semantic Modeling Language) and OSI (Open Semantic Interchange) are open standards, not proprietary formats. Your business logic is always yours — readable, versionable, and portable.

⚡

Automated Orchestration

Replace error-prone manual deployments with a standardized, automated workflow. From extraction to validation to deployment — fully automated, reproducible, and CI/CD-ready.

🔮

Future-Proof Architecture

Plug-in connectors, versioned rule packs, and a stable converter core mean new platforms are addable without disrupting existing pipelines. Built to evolve with your data stack.

🏠

Self-Contained in Your Environment

SemaBridge runs entirely within your own infrastructure — no external servers, no cloud dependencies. All semantic artifacts persist locally, ensuring your business logic never leaves your controlled perimeter.

Zero-Trust Security

Runs Inside Your Customer Environment

SemaBridge is architected from the ground up to run entirely within your own infrastructure — no data leaves your perimeter, no external dependencies, no cloud services required.

✓

Runs Entirely in Your Environment

SemaBridge is deployed and executed within your own infrastructure — on-premises or your private cloud. There is no SaaS component, no call-home, and no external data transmission. Your semantic models never leave your network.

✓

Zero Secrets Stored. Ever.

All credentials — tokens, passwords, client secrets, private keys — are provided exclusively via environment variables. They are never written to YAML configuration, logs, or artifact storage.

✓

Local Artifact Sovereignty

All semantic artifacts — source metadata, SML output, conversion reports — are persisted locally in your own environment using your chosen storage backend. You own and control all data at all times.

✓

Compliance-Grade Traceability

Every run is identified with a Project ID and Run ID, with immutable execution status (SUCCESS / FAILED / PARTIAL). Designed for internal audits, SOC 2, and regulated industry requirements.

# semabridge.yaml — runs in your environment

project:
  name: "revenue-semantic-model"

source:
  type: "fabric_semantic_model"
  workspace: "prod-workspace"

target:
  type: "snowflake_semantic_view"
  database: "ANALYTICS"

auth:
  fabric:
    client_secret_env: "SB_FABRIC_SECRET"
  snowflake:
    password_env: "SB_SF_PASSWORD"

# ✓ All processing stays within your network
# ✓ No data transmitted externally
# ✓ Credentials isolated to env vars only

Ready to Unify Your Semantic Layer?

See how SemaBridge eliminates semantic drift, accelerates platform migrations, and powers AI agents with trusted business logic — in a personalized demo tailored to your data stack.

Request a Demo → Explore Adapters