Graphlit
AI Agent Development Platforms
A serverless RAG-as-a-Service platform for building AI-powered applications and agents with unstructured data.
Graphlit is a serverless Retrieval-Augmented Generation (RAG)-as-a-Service platform designed to accelerate the development of AI-powered applications and agents that utilize unstructured data. It offers automated ETL processes, seamless integration with Large Language Models (LLMs), and supports multimodal data types, including text, audio, video, and images. Graphlit provides tools for data ingestion, text extraction, vector embeddings, and conversation history management, enabling developers to build advanced AI solutions efficiently.
Industry: Technology
Pricing: freemium
Use cases: AI developers, data engineers, software architects, machine learning engineers
Capabilities: Developers building AI applications that require integration of unstructured data., Automating data ingestion and processing workflows for AI agents., Implementing Retrieval-Augmented Generation (RAG) techniques in AI solutions., Handling multimodal data types in AI-driven applications.
Tags: RAG-as-a-Service, unstructured data, AI development platform, data ingestion, LLM integration
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Graphlit
A serverless RAG-as-a-Service platform for building AI-powered applications and agents with unstructured data.
About
Graphlit is a serverless Retrieval-Augmented Generation (RAG)-as-a-Service platform designed to accelerate the development of AI-powered applications and agents that utilize unstructured data. It offers automated ETL processes, seamless integration with Large Language Models (LLMs), and supports multimodal data types, including text, audio, video, and images. Graphlit provides tools for data ingestion, text extraction, vector embeddings, and conversation history management, enabling developers to build advanced AI solutions efficiently.
Key Capabilities
- Developers building AI applications that require integration of unstructured data.
- Automating data ingestion and processing workflows for AI agents.
- Implementing Retrieval-Augmented Generation (RAG) techniques in AI solutions.
- Handling multimodal data types in AI-driven applications.
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