Caden AI provides a fully hosted platform for building AI applications using Retrieval-Augmented Generation (RAG) and GraphRAG architectures. It supports model agnosticism and various data formats, offering pre-built connectors and APIs for easy integration. The service transforms unstructured data into queryable knowledge graphs, enabling developers to rapidly deploy domain-specific chatbots and analytics tools.
Funding
$250K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
FVFounders
Product
Problem
Building AI applications that leverage unstructured data requires significant infrastructure management and expertise in Retrieval-Augmented Generation (RAG) and knowledge graphs. Transforming diverse data formats into structured knowledge and integrating them with language models can be complex and time-consuming.
Solution
Caden AI offers a fully hosted platform that simplifies the development of AI applications using RAG and GraphRAG. The platform eliminates the need for users to manage infrastructure by providing pre-built connectors for common data sources, support for various data formats, and integrations with multiple language models. Caden AI enables users to transform unstructured data into structured formats, deploy knowledge graphs, and rapidly experiment with different prompts and models. The platform offers end-to-end logging for full visibility into AI applications, facilitating troubleshooting and identification of data issues and LLM errors.
Target Audience
Caden AI targets developers and organizations looking to build AI-powered applications that leverage unstructured data, including those building chatbots, analytics tools, and recommendation systems.
Features
- Pre-built connectors for common data sources like Google Docs and Notion
- Support for various data formats, including PDF, text, JSON, CSV, and Excel
- Model agnostic, supporting OpenAI, Google Gemini, Anthropic Claude, Cohere, and open-source models (Llama 3, Mistral)
- Fully hosted infrastructure, including vector databases, full-text search indexes, and graph databases
- TypeScript/JavaScript library and REST APIs for easy integration with applications
- Webhooks for real-time data ingestion
- End-to-end logging for visibility into AI application performance
- Graph builder interface for creating graph structures