A contextual semantic layer is a framework that provides meaningful context to organizational data, enabling systems - especially AI and analytical tools - to interpret, connect, and act on information more intelligently and accurately.
How Is the Contextual Semantic Layer Different from Traditional Semantic Layers?
A contextual semantic layer extends the traditional semantic layer by embedding business meaning, relationships, and domain-specific knowledge into both structured and unstructured data assets - from databases to documents and multimedia. It acts as a bridge, connecting raw technical data to business-friendly language, making information easy to access and understand from both humans and machines.
The traditional semantic layer (found in BI tools and standalone semantic layers like dbt and others) essentially provide a translation layer over the physical database to hide the technical database, table, and column names as well as joins to generate a SQL statement based on the items a user might select.
How Does It Provide Context?
The contextual semantic layer assigns extra metadata through semantic tagging and entity extraction, classifying content by topic, entity, and relationship. It does this by leveraging ontologies, taxonomies, and knowledge graphs to model the domain's entities and their relationships. This adds rich context to each data point, supports advanced content discovery, and helps avoid misinterpretation by AI systems.
Components of a Contextual Semantic Layer
The key components of the contextual semantic layer include:
- Business logic and governed metrics - standardized definitions for KPIs and key business terms
- Data Models and knowledge graphs - models mapping how entities relate (eg. "Product belongs to Category")
- Memory/context layers - maintaining conversation and analytical state for systems like generative AI (GenAI)
- Integration mechanisms - connecting data sources, documents, and analytical workflows across the organization
How Does Contextual Semantic Layers Add Business Value
A contextual semantic layer helps organizations by:
- Making complex data accessible and explainable
- Avoiding errors from lack of context in AI and analytics
- Achieving consistency and trust in business reporting
- Gaining actionable, context-rich insights and recommendations
In short, the contextual semantic layer transforms how both machines and people use enterprise data, enabling advanced analytics and trustworthy AI outcomes by embedding context and relationships within the data itself.
Sample Vendors That Provide Semantic Layer Solutions
A number of vendors offer semantic layer solutions and increasingly promise to bridge business logic, analytics and AI across the data ecosystem.
Leading Vendors
AtScale: Delivers a universal semantic layer that provides AI-ready semantic models, consistent metrics for BI and AI, and centralized metric governance across cloud platforms and BI tools
Denodo: Provides a universal semantic layer for centralized, governed and consistent metadata management, supporting integration with modern analytics and AI workflows
Cube: Offers a universal semantic layer platform enabling data modeling, governed data access, metric consistency and support for BI, data apps, and AI/LLM integrations
dbt Semantic Layer: Supplies version controlled metric logic, business friendly querying and integration with ETL, warehouses and BI endpoints
Dremio: Features a universal semantic layer to enable governed, collaborative analytics and AI access to structured and unstructured data
Looker (part of Google Cloud): Its semantic layer enhances trust and explainability for generative AI and BI by embedding consistent business context
Codd AI: AI powered Contextual Semantic Layer creation by combining technical metadata as well as business knowledge, rules and logic to drive auto-generation of data model and business metrics that is embedded in an AI agent for conversational analytics, BI and any other endpoint integration
What Makes Codd AI Different?
Codd AI is a next generation platform that provides an AI-driven Contextual Semantic Layer for analytics, BI and large scale data environments.
Core Capabilities
GenAI Semantic Layer: Codd AI uses advanced GenAI to build contextual semantic layers or Corpuses automatically, discovering business entities, relationships and technical context across data sources
Knowledge Integration: It unifies technical and business knowledge and logic from documents, databases, data catalogs, websites and internal repositories to create a rich, context-aware semantic map of the organization's data
Natural Language Analytics: Users can interact with their data by asking questions in natural language (NLP) and receiving business-contextualized, explainable answers in real time
AI-driven Data Modeling: Automatically generates data and metric models, aligning technical metadata with business logic and KPI definitions
Human-in-the-Loop: Ensures the accuracy and reliability of the contextual semantic model by incorporating feedback, validation and confidence scoring
BI and AI Integration: Supports seamless connections to BI tools (eg. Tableau, Power BI, Looker) and provides APIs for LLMs, GenAI and other analytical workloads
Vertical Knowledge Hub: Allows combining external domain knowledge, such as industry specific regulations, with internal data to deepen contextual understanding
Codd AI is positioned as a category leader for organizations seeking to automate, scale and govern Contextual Semantic Layers to power AI and analytical use cases with minimal manual data modeling or integration overhead.
How Does Codd AI Compare with Other Tools?
Codd AI distinguishes itself among semantic layer solutions by leveraging advanced GenAI to automate and contextualize the semantic modeling process, aiming to reduce manual setup and increase contextual understanding compared to other solutions.
| Feature | Codd AI | AtScale | Cube | dbt Semantic Layer |
|---|---|---|---|---|
| Automation Level | High - GenAI learns business context and technical metadata, auto-builds data models and metrics | Moderate - Manual model setup, strong BI integrations | Moderate - Code/model-based, limited auto-context | Moderate - Version controlled, code-based with limited automation |
| Contextualization (Business/Semantics) | Deep - integrate business docs, knowledge and regulatory info | Business logic focused with universal metric layer | Business logic by code, user defined | Business logic by code, metric definitions contain some business logic |
| Integration (Data, BI, AI) | Broad - Data, BI, GenAI/LLMs, knowledge graphs, catalogs | Broad - Cloud, BI and analytics | Moderate - BI, apps, some AI | Limited - focused on analytical tools, some LLMs |
| Explainability | High - Contextualized, explainable answers with feedback loop and recommendations | High - Consistent reporting and KPI tracking | Moderate - depends on the setup | Moderate |
| Model Governance | Strong - Automated with Human-in-the-Loop validation | Strong - Centralized metric and access management | Manual | Manual |
| AI-Specific Features | Designed for LLMs and GenAI. Reduces hallucinations | Supports AI agents, not GenAI native | AI integrations growing. Not GenAI native | Limited |
Getting Started With Contextual Semantic Layers
While the benefits of moving towards Contextual Semantic Layers are pretty real, for many organizations it might seem like a massive investment and project. Most organizations have investments in BI tools, databases and possibly data catalogs.
From our experience, it does not have to be a Big Bang project. Like in most data and analytical projects, starting with a well defined and narrowly scoped project is preferred.
Some Example Pilot Projects:
Data modernization: Many organizations are moving from traditional data estates on premises to cloud-based and modern data stacks (even with remaining on premises). This might be an opportunity to bring in your Contextual Semantic Layer from the start to help drive consistency across analytical use cases and accelerate adoption beyond traditional BI tools.
Self-service analytics are not getting adoption: Many organizations have invested heavily in BI tools and data catalogs to facilitate so-called self service. The reality is in most cases that these fail to get adoption. Implementing a Contextual Semantic Layer and more importantly a natural language conversational analytical interface for users and decision makers bring data and relevant insights directly to those who need the data - in real time.
ERP or new App Implementation: This might be more relevant in SMB (small or medium sized businesses) who are in the process of implementing an ERP or any other packaged application. Most business leaders go this path to get better insights. But rather than investing in an expensive BI deployment on top of the ERP, you can move pertinent data from the ERP into an analytical data source and deploy a Contextual Semantic Layer and natural language analytics immediately at very low cost and rapid deployment.
Ready to Transform Your Analytics with Contextual Semantic Layers?
If you are interested in having a strategy conversation or look deeper into how solutions like Codd AI can assist in your journey, reach out to us:
- Schedule a demo discussion with our team
- Start your free trial to experience the power of contextual semantic layers