Graphlit

What is Graphlit?

Graphlit is a developer-centric, serverless platform designed to simplify and speed up the creation of AI applications and agents that utilize unstructured data. It offers an end-to-end pipeline from automated data ingestion and transformation to advanced AI-powered conversational services. Integrated tightly with Azure and other cloud services, Graphlit supports a wide variety of data sources and formats, including documents, audio, video, and images. Its core strengths lie in automated ETL for large language models, multimodal capabilities, semantic search, and a knowledge graph that extracts and links entities across ingested data. By abstracting infrastructure complexities, Graphlit enables rapid operationalization of scalable AI solutions across diverse use cases such as chatbots, content summarization, semantic search, and AI-driven reporting.

Key Features

• Automated ETL for LLMs

  Seamlessly ingest and transform unstructured data from multiple sources like websites, Slack, email, podcasts, and cloud storage, supporting formats including text, audio, video, and images with OCR and metadata enrichment.

• RAG-as-a-Service

  Provides intelligent text extraction, chunking, vector embeddings, conversation history management, and semantic search with metadata filtering to accelerate Retrieval-Augmented Generation workflows.

• Multimodal AI Support

  Integrates with large multimodal models such as GPT-4 Vision for image analysis, audio transcription, and visual search, enabling rich AI interactions beyond text.

• Knowledge Graph Construction

  Builds a graph of entities and their relationships extracted from ingested data, facilitating powerful querying, visualization, and context-aware AI responses.

• Developer-Friendly Serverless Architecture

  Cloud-native, zero-deployment platform with managed APIs, multi-tenant support, role-based access control, and flexible usage-based pricing to streamline AI app development.

• Extensive Data Connectivity

  Supports numerous integrations including Google Drive, OneDrive, SharePoint, Notion, Slack, GitHub, Jira, and more for comprehensive data ingestion.

Use Cases

• Intelligent Chatbots and Virtual Assistants : Create conversational AI interfaces with access to vast, up-to-date knowledge bases for customer support, internal help desks, or personal assistants.
• Content Summarization and Generation : Automatically generate summaries of lengthy documents, transcripts, or web content, and produce social media posts or long-form content.
• Semantic Search and Knowledge Discovery : Enable vector-based semantic search across unstructured data with metadata filtering to quickly find relevant information.
• Data Extraction and Automated Reporting : Extract structured data from PDFs, emails, and other unstructured sources to automate report generation and data analysis workflows.
• Multimodal AI Applications : Leverage audio transcription, image description, and visual similarity search to build rich, multimodal AI experiences.

FAQs