Graphlit

What is Graphlit?

Graphlit is an API-first platform for developers building AI-powered applications that work with unstructured data. It provides cloud infrastructure and automates backend data workflows: ingestion, knowledge extraction, semantic search, and integration with other systems via webhooks.

The platform supports building conversational knowledge graphs using models like OpenAI’s GPT-3.5 and GPT-4. It handles a wide range of content types—multimedia included—stores them securely, and uses vector-based methods for knowledge retrieval. You can pull information from PDFs, web pages, images, RSS feeds, podcasts, YouTube videos, and Slack messages. Usage tracking helps developers monitor costs.

It serves industries including legal, sales, entertainment, healthcare, and engineering.

Who created Graphlit?

Graphlit was designed from the start as an API-first system for developers creating AI applications on top of unstructured data, with cloud-native infrastructure that automates data ingestion, knowledge extraction, semantic search, and related workflows. The founder’s identity isn’t publicly known.

What is Graphlit used for?

Graphlit covers the full pipeline of working with unstructured data in AI applications:

• Data Ingestion: Pull data from websites, cloud storage, SharePoint, podcasts, Jira, Notion, YouTube, email, and Slack.
• Document & Image Processing: Extract text and tables from documents and images using OCR and LLMs.
• Automated Workflows: Build custom content workflows that extract metadata, identify named entities, and structure data automatically.
• Web Scraping & Data Enrichment: Scrape web pages, take screenshots, and enrich data with external APIs like Wikipedia and Crunchbase.
• RAG Features: Built for Retrieval Augmented Generation, with text extraction, chunking, vector embeddings, and conversation history management.
• Semantic Search: Search by meaning using vector-based search with metadata filtering.
• Content Creation: Generate transcript summaries, social media posts, and longer articles.
• Multimodal Processing: Work with Large Multimodal Models like OpenAI’s GPT-4 Vision to process images and audio, transcribe audio, generate image descriptions, and detect visual objects.
• Image Similarity Search: Find similar images via image embeddings.
• Knowledge Graphs: Create knowledge graphs using LLMs.
• Multimedia Management: Securely store documents, audio, and video in various formats.

Graphlit gives developers the infrastructure to ingest content in many formats, build knowledge graphs on top of it, and ship AI applications without managing the underlying data plumbing.