Graphlit
AI platform for unstructured data
About Graphlit
What this tool does and where it fits best.
Platform for building AI applications on unstructured data, offering APIs for ingestion, enrichment, and querying.
Prompts for Graphlit
Challenges using Graphlit
Key capabilities
What Graphlit is actually good at.
Unstructured Data Ingestion
Automated ETL pipeline that ingests any unstructured data format including documents, HTML, Markdown, audio, video, images, emails, and code files. Supports data connectors for Google Drive, Notion, GitHub, Slack, OneDrive, and cloud storage (S3, Azure Blob).
Conversational Knowledge Graph
Automatically extracts knowledge graph entities and relationships between people, organizations, places and topics found in content. Builds a searchable, conversational knowledge graph using AI that can be queried through natural language.
RAG-as-a-Service Platform
Serverless Retrieval-Augmented Generation platform that provides managed vector databases, graph databases, and integrations with leading LLMs (OpenAI, Azure OpenAI, Anthropic, Mistral). Eliminates need for Langchain, Pinecone, or S3 setup.
Multimodal Content Processing
Processes multiple content types with built-in capabilities including audio transcription for podcasts and videos, OCR for images and PDFs, web scraping for websites, and Markdown extraction. Automatically indexes and makes content searchable.
Tool details
Core technical and commercial details.
Feature highlights
Details that help this tool stand apart in the directory.
Unstructured Data Ingestion
Automated ETL pipeline that ingests any unstructured data format including documents, HTML, Markdown, audio, video, images, emails, and code files. Supports data connectors for Google Drive, Notion, GitHub, Slack, OneDrive, and cloud storage (S3, Azure Blob).
Conversational Knowledge Graph
Automatically extracts knowledge graph entities and relationships between people, organizations, places and topics found in content. Builds a searchable, conversational knowledge graph using AI that can be queried through natural language.
RAG-as-a-Service Platform
Serverless Retrieval-Augmented Generation platform that provides managed vector databases, graph databases, and integrations with leading LLMs (OpenAI, Azure OpenAI, Anthropic, Mistral). Eliminates need for Langchain, Pinecone, or S3 setup.
Multimodal Content Processing
Processes multiple content types with built-in capabilities including audio transcription for podcasts and videos, OCR for images and PDFs, web scraping for websites, and Markdown extraction. Automatically indexes and makes content searchable.
GraphQL API & SDKs
Cloud-native API-first platform accessible via GraphQL API with native SDKs for Python, Node.js, and .NET. Provides programmatic access to all platform features including semantic search, content querying, and conversation management.
Enterprise Security & Compliance
Azure-native security with enterprise-grade encryption, auto-scaling via Azure Functions, and compliance readiness for SOC 2, GDPR, and HIPAA. Supports multi-tenant applications with secure data isolation.
Agent Tools Library
Open-source Python toolkit for building AI agents that streamline data handling and LLM-driven workflows. Provides pre-built tools for data ingestion, text extraction, vector embeddings, and conversation history management.
Semantic Search & Retrieval
Advanced semantic search capabilities with metadata filtering, vector similarity search, and content retrieval. Enables context-aware information retrieval across all ingested unstructured data.