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TrustGraph

Transform AI agents from experimental concepts into a new paradigm of continuous operations.

The TrustGraph Autonomous Knowledge Operations Platform provides a robust, scalable, and reliable AI infrastructure designed for complex environments, complete with a full observability and telemetry stack. TrustGraph automates the deployment of state-of-the-art RAG pipelines using both Knowledge Graphs and Vector Databases in local and cloud environments with a unified interface to all major LLM providers.

The Demo-To-Production Problem​

Building enterprise AI applications is hard. You're not just connecting APIs with a protocol - you're wrangling a complex ecosystem:

  • Data Silos: Connecting to and managing data from various sources (databases, APIs, files) is a nightmare.
  • LLM Integration: Choosing, integrating, and managing different LLMs adds another layer of complexity.
  • Deployment Headaches: Deploying, scaling, and monitoring your AI application is a constant challenge.
  • Knowledge Graph Construction: Taking raw knowledge and structuring it so it can be efficiently retrieved.
  • Vector Database Juggling: Setting up and optimizing a vector database for efficient data retrieval is crucial but complex.
  • Data Pipelines: Building robust ETL pipelines to prepare and transform your data is time-consuming.
  • Data Management: As your app grows, so does the data meaning storage and retreival becomes much more complex.
  • Prompt Engineering: Building, testing, and deploying prompts for specific use cases.
  • Reliability: With every new connection, the complexity ramps up meaning any simple error can bring the entire system crashing down.

What is TrustGraph?​

Cool agent demos often bypass the hard parts: robust knowledge integration, error handling, scalability, security, and monitoring needed for real-world value. TrustGraph turns AI agents into continuous and reliable operations by deploying automated RAG pipelines (KG+VectorDB), unified access to any LLM, and managing it all with enterprise-grade infrastructure and observability.

architecture

The TrustGraph Stack​

  • 📄 Data Ingest: Bulk ingest documents such as .pdf,.txt, and .md
  • 📃 OCR Pipelines: OCR documents with PDF decode, Tesseract, or Mistral OCR services
  • 🊓 Adjustable Chunking: Choose your chunking algorithm and parameters
  • 🔁 No-code LLM Integration: Anthropic, AWS Bedrock, AzureAI, AzureOpenAI, Cohere, Google AI Studio, Google VertexAI, Llamafiles, LM Studio, Mistral, Ollama, and OpenAI
  • 📖 Automated Knowledge Graph Building: No need for complex ontologies and manual graph building
  • ðŸ”Ē Knowledge Graph to Vector Embeddings Mappings: Connect knowledge graph enhanced data directly to vector embeddings
  • ❔ Natural Language Data Retrieval: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses
  • 🧠 Knowledge Cores: Modular data sets with semantic relationships that can saved and quickly loaded on demand
  • ðŸĪ– Agent Manager: Define custom tools used by a ReAct style Agent Manager that fully controls the response flow including the ability to perform Graph RAG requests
  • 📚 Multiple Knowledge Graph Options: Full integration with Memgraph, FalkorDB, Neo4j, or Cassandra
  • ðŸ§Ū Multiple VectorDB Options: Full integration with Qdrant, Pinecone, or Milvus
  • 🎛ïļ Production-Grade Reliability, scalability, and accuracy
  • 🔍 Observability and Telemetry: Get insights into system performance with Prometheus and Grafana
  • ðŸŽŧ Orchestration: Fully containerized with Docker or Kubernetes
  • ðŸĨž Stack Manager: Control and scale the stack with confidence with Apache Pulsar
  • ☁ïļ Cloud Deployments: AWS, Azure, and Google Cloud
  • ðŸŠī Customizable and Extensible: Tailor for your data and use cases
  • ðŸ–Ĩïļ Configuration Builder: Build the YAML configuration with drop down menus and selectable parameters
  • ðŸ•ĩïļ Test Suite: A simple UI to fully test TrustGraph performance

Why Use TrustGraph?​

  • Accelerate Development: TrustGraph instantly connects your data and app, keeping you laser focused on your users.
  • Reduce Complexity: Eliminate the pain of integrating disparate tools and technologies.
  • Focus on Innovation: Spend your time building your core AI logic, not managing infrastructure.
  • Improve Data Relevance: Ensure your LLM has access to the right data, at the right time.
  • Scale with Confidence: Deploy and scale your AI applications reliably and efficiently.
  • Full RAG Solution: Focus on optimizing your respones not building RAG pipelines.