timbr core
Enterprise LLM Foundation
The Timbr LLM Foundation is the enterprise-grade backbone that enables Large Language Models and autonomous agents to interact seamlessly with structured, governed, and contextualized data, by leveraging ontologies, relationships, measures, and virtualization.
Connect Any LLM to Trusted Data
Use Timbr’s SDKs to power LLMs with trusted context, accurate SQL, and explainable access to enterprise data.
GraphRAG
SDK
Combine SQL-driven semantic grounding with vector-based retrieval for precision and explainability.
LangChain
SDK
Plug Timbr into chain-based workflows with multi-LLM support of public, proprietary and open source models.
LangGraph
SDK
Build multi-step, context-aware workflows that leverage structured and unstructured data.
MCP Ready
Timbr also supports deployment via MCP (Model-Context-Platform) servers, enabling AI systems to invoke semantic context on demand through centralized APIs. This architecture makes Timbr’s knowledge graph available to any service, ensuring multi-agent interoperability and consistent access to governed, queryable data. MCP readiness helps organizations scale LLM operations across varied interfaces and runtime environments.
A Launchpad for GenAI Data Products
With Timbr LLM Foundation, organizations can build AI-native data experiences that are accurate, explainable, and scalable:
- AI-powered analytics copilots.
- Autonomous agents enhanced with governed data access and semantic decision logic.
- Semantic-aware RAG systems.
- NLQ in BI tools and Excel.
- Data assistant chatbots grounded in metrics and ontology.
- Workflow automation driven by structured context.
Timbr enables you to move from GenAI pilots to production-grade applications faster and with confidence.
Built for the NL2SQL Paradigm
Modern enterprise LLM applications depend on their ability to translate natural language into SQL (NL2SQL). Accurate generation requires more than raw table access, training and smart promp engineering. It demands context, structure, and semantic alignment.
LLM Foundation delivers precisely that:
- SQL-native ontologies organize data into business concepts, relationships, and hierarchies.
- Semantic relationships replace complex JOINs with intuitive navigation paths.
- Standardized SQL measures and business rules are embedded directly in the model.
- Query constraints and filters that are enforced at the ontology level.
Compatible with LLM’s Training
Timbr gives LLMs the semantic scaffolding they need to reliably translate human intent into correct, optimized SQL queries. Unlike RDF/SPARQL-based knowledge graphs, Timbr is purpose-built for enterprise LLMs:
- SQL is deeply embedded in LLM training data, making generation more reliable and accurate.
- No need to train or fine-tune models on custom languages.
- Agile alignment with popular orchestration frameworks like LangChain, LangGraph, and RAG agents.
Benefits at a Glance
Timbr provides the semantic intelligence GenAI needs to interact with enterprise data safely and effectively.
Natural language into SQL translation using Timbr’s structured semantic context
Access control, lineage, and policies, applied in AI-generated queries
Precise, explainable results grounded in business logic and relationships
Connects to any LLM, agent, or platform via flexible APIs and SDKs
Reusable KPIs defined once, governed everywhere
Reduced token usage made possible by relevant context
Governed, Transparent, Explainable
Trust is critical in enterprise AI. Timbr brings governance and observability to LLM interactions:
- Access controls, row-level security, and masking apply even to AI-generated queries.
- Audit trails track query generation, execution, and user behavior.
- Semantic lineage allows every answer to be traced back to business-defined logic and metrics.
- Auto-generated OpenAPI/Swagger provides self-documenting, relationship-aware APIs for transparent and secure AI access.
This makes Timbr not just AI-compatible, but AI-accountable.
