Enterprise LLM Foundation

Solution

Power Enterprise LLMs' Data Retrieval with Context and Relationships

Timbr turns enterprise data into a governed SQL knowledge graph that makes large language models (LLMs) accurate, explainable, and cost-efficient.

Timbr GraphRAG SDK and SDKs for LangChain, LangGraph empower copilots, chatbots, and autonomous agents with context-rich responses grounded in your real business knowledge.

BEHIND THE SOLUTION

With built-in support for LangChain, LangGraph, and GraphRAG, Timbr enables modular, scalable architectures for retrieval-augmented generation (RAG), agents, and copilots. Plug any LLM of your choice, OpenAI, Azure OpenAI, Anthropic, Mistral, etc. into workflows, securely and at scale.

REEFERENCE ARCHITECTURES

Timbr serves as the semantic backbone for GenAI architectures across your existing data platforms. Whether you’re using Databricks, Microsoft Fabric, Snowflake, or Google Cloud, Timbr integrates natively to deliver governed, context-rich knowledge for LLMs.

Challenges

Are LLMs Producing Unreliable or Inconsistent Outputs?
LLMs without context often hallucinate or oversimplify. Enterprise teams need grounded responses with traceable logic, – not generic answers generated in a vacuum.
High LLM Costs with Little Reuse?
Without reusable logic and governed access, prompt and token usage quickly become inefficient. Teams face rising LLM costs and duplicated efforts across use cases.
Struggling to Operationalize RAG or Agents?
Even with tools like LangChain, building production-grade agents is difficult without a structured knowledge source. Connecting LLMs to governed, enterprise-grade data remains a major blocker.

Why Timbr

Graph-Powered RAG, Chatbots & Agents
Timbr’s semantic graph provides the structured foundation LLMs need. With GraphRAG, LangChain, and LangGraph integrations, developers can build intelligent agents and copilots that reason, explore, and explain.
Native SQL-to-NL & NL-to-SQL Support
Use natural language to query governed enterprise data, or reverse it: translate existing SQL logic into prompts and agent workflows. Timbr makes both directions LLM-native, secure, and fully explainable.
Minimize Token Use, Maximize Intelligence
By guiding LLMs with structured context, Timbr reduces dependency on long prompts and full document retrieval. This not only improves response quality, but also significantly lowers LLM operational costs.

Impact

Faster Time to AI Value
With Timbr’s ready-made semantic models and plug-and-play LLM integrations, teams get from prototype to production faster—no need to reinvent data pipelines or governance.
Trusted, Governed Responses at Scale
Timbr ensures that every LLM interaction reflects your organization’s definitions, rules, and permissions—whether it’s a chatbot, copilot, or autonomous agent.
Cost-Efficient GenAI Workloads
Reduce unnecessary token use, eliminate redundant logic, and streamline LLM calls. Timbr provides the structure and logic to help your organization scale GenAI affordably.

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Model a Timbr SQL Knowledge Graph in just a few minutes and learn how easy it is to explore and query your data with the semantic graph

Model a Timbr SQL Knowledge Graph in just a few minutes and learn how easy it is to explore and query your data with the semantic graph

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