TrustGraph: Data-to-AI, Simplified
Connect your AI app directly to your data with a full stack solution. Fully connected Agentic Graph RAG pipelines mean you can focus on fine tuning your app and not building data infrastructure.
The AI App Problem: Everything in Betweenâ
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?â
TrustGraph removes the biggest headache of building an AI app: connecting and managing all the data, deployments, and models. As a full-stack platform, TrustGraph simplifies the development and deployment of data-driven AI applications. TrustGraph is a complete solution, handling everything from data ingestion to deployment, so you can focus on building innovative AI experiences.
The Stack Layersâ
- ð Data Ingest: Bulk ingest documents such as .pdf,.txt, and .md
- ð OCR Pipelines: OCR documents with PDF decode, Tesseract, or Mistral OCR services
- ðŠ Adjustable Chunking: Choose your chunking algorithm and parameters
- ð No-code LLM Integration: Anthropic, AWS Bedrock, AzureAI, AzureOpenAI, Cohere, Google AI Studio, Google VertexAI, Llamafiles, LM Studio, Mistral, Ollama, and OpenAI
- ð Automated Knowledge Graph Building: No need for complex ontologies and manual graph building
- ðĒ Knowledge Graph to Vector Embeddings Mappings: Connect knowledge graph enhanced data directly to vector embeddings
- â Natural Language Data Retrieval: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses
- ð§ Knowledge Cores: Modular data sets with semantic relationships that can saved and quickly loaded on demand
- ðĪ 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
- ð Multiple Knowledge Graph Options: Full integration with Memgraph, FalkorDB, Neo4j, or Cassandra
- ð§Ū Multiple VectorDB Options: Full integration with Qdrant, Pinecone, or Milvus
- ðïļ Production-Grade Reliability, scalability, and accuracy
- ð Observability and Telemetry: Get insights into system performance with Prometheus and Grafana
- ðŧ Orchestration: Fully containerized with Docker or Kubernetes
- ðĨ Stack Manager: Control and scale the stack with confidence with Apache Pulsar
- âïļ Cloud Deployments: AWS, Azure, and Google Cloud
- ðŠī Customizable and Extensible: Tailor for your data and use cases
- ðĨïļ Configuration Builder: Build the YAML configuration with drop down menus and selectable parameters
- ðĩïļ Test Suite: A simple UI to fully test TrustGraph performance
Why Use TrustGraph?â
- 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.