Graphlit
AI platform for unstructured data
Best For
About Graphlit
What this tool does and how it can help you
Platform for building AI applications on unstructured data, offering APIs for ingestion, enrichment, and querying.
Prompts for Graphlit
Challenges using Graphlit
Key Capabilities
What you can accomplish with Graphlit
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
Technical specifications and requirements
License
Paid
Pricing
Subscription
Supported Languages
Similar Tools
Works Well With
Curated combinations that pair nicely with Graphlit for faster experimentation.
We're mapping complementary tools for this entry. Until then, explore similar tools above or check recommended stacks on challenge pages.