SemanticWorx

Cloud Data Storage Integration

Provide deep integration with modern cloud data platforms to enable automated metadata harvesting, lineage capture, and semantic model mapping. The initial focus will be tight integration with Databricks lakehouse environments, followed by broader AWS cloud data platform integration, enabling Affirma to operate seamlessly within enterprise data lake architectures.

Key Product Functions

Databricks Unity Catalog metadata harvesting
Databricks table, schema, and pipeline lineage integration
Mapping semantic models to lakehouse tables
AWS S3 metadata discovery and classification
Automated data source onboarding

RAG Data Preparation

Prepare enterprise datasets and knowledge sources so they can support Retrieval-Augmented Generation (RAG) architectures used by generative AI applications. Affirma organizes enterprise knowledge through semantic relationships and metadata enrichment so LLMs can retrieve accurate context.

Key Product Functions

Dataset semantic indexing
Knowledge graph creation
Vector embedding generation
Document-to-data linking

Prompt-to-Data Mapping

Translate natural language questions into structured queries by leveraging the semantic model, business glossary, and ontology relationships. Business users can ask questions in plain language and the platform maps the question to the correct datasets and fields.

Key Product Functions

Natural language to SQL translation
Query generation via ontology
Contextual dataset identification

LLM Semantic Integration

Integrate large language models directly with Affirma’s semantic layer and ontology framework so AI systems can interpret enterprise data using business context rather than raw schemas. The semantic model provides structured meaning, relationships, and governance metadata that guide LLM reasoning and reduce hallucinations.

Key Product Functions

Semantic context injection for LLM prompts
Knowledge graph grounding for AI responses
Ontology-driven prompt templates
Context retrieval from semantic models
LLM guardrails based on governance rules

AI Metadata Enrichment

Use AI/ML techniques to automatically enhance metadata across datasets, schemas, and documentation. The platform analyzes table structures, column names, and usage patterns to infer business meaning, relationships, classifications, and ownership. This reduces the manual effort required to build a usable data catalog and helps rapidly scale governance programs.

Key Product Functions

Automatic dataset classification
Business glossary mapping suggestions
Automated tagging and labeling
Sensitive data detection (PII, regulatory data)
Schema and entity recognition

Licensing

Create the opportunity for either single user or enterprise licensing.

Allows for testing and getting use to the development of the tool
Allows the use to expand the tool to other users using enterprise licensing
Allows flexibility and opens the door for more people to use our product

Versioning and Branching

Implement version-controlled modeling with branching and governance workflows.

Allow users to make changes to ESMs, data designs, or mappings in their own branches instead of a single shared version.
Introduce an approval process where changes are reviewed and validated before acceptance.
Merge approved changes into the main branch, combining versioning, branching, and governance into one structured workflow.

Data Design and Quality

Enhance data modeling and quality by leveraging constraints, reference models, and advanced customization capabilities.

Use data design constraints to generate and support XSD-type structures and validations.
Build enterprise semantic models from reference models, then derive detailed data designs from them.
Enable highly specific schema customizations while refining data quality capabilities and controls.

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