Graphlit is a cloud-native, API-first platform that automates the ingestion and processing of unstructured data from various sources, including documents, audio, and images, using advanced LLMs and multimodal AI. It enables developers to efficiently extract, search, and repurpose knowledge, accelerating the development of domain-specific AI applications without the need for extensive infrastructure.
Funding
$3.6M 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.
Founders
Product
Problem
Building AI applications and agents requires efficient ingestion and processing of unstructured data from various sources and formats. Developers often face challenges in extracting, searching, and repurposing knowledge from documents, audio, images, and other unstructured content. This process can be time-consuming and resource-intensive, hindering the development of domain-specific AI solutions.
Solution
Graphlit is a cloud-native, serverless RAG-as-a-Service platform that automates the ingestion and processing of unstructured data using LLMs and multimodal AI. It provides developers with an API-first platform to efficiently extract, search, and repurpose knowledge from various sources, including websites, cloud storage, SharePoint, podcasts, Jira, Notion, YouTube, email, and Slack. Graphlit supports any unstructured data format, such as documents, HTML, Markdown, audio, video, and images. The platform offers automated data preparation, intelligent text extraction and chunking, built-in vector embeddings, conversation history, and LLM-based entity extraction.
Target Audience
Graphlit is designed for developers building AI applications and agents, including chatbots, copilots, and vertical AI applications with domain-specific data.
Features
- Automated data ingestion from various sources, including web scraping, Google Drive, Notion, GitHub, Slack, Jira, email, and RSS
- High-performance data preparation with text and table extraction from documents and images using OCR and LLMs
- Automatic audio transcription with Deepgram
- Integrated with Large Multimodal Models (LMMs) including OpenAI GPT-4o and Anthropic Sonnet 3.5 for image descriptions and similarity search via image embeddings
- RAG and GraphRAG capabilities with intelligent text extraction and chunking, built-in vector embeddings, and conversation history
- Semantic search with vector-based search and metadata filtering
- Content creation features for automated text and transcript summarization, social media post generation, and long-form content creation
- Native SDKs for Python, Node.js, and .NET
- Multitenant-ready with RBAC and data encryption at rest