Graphlit

Graphlit is a comprehensive API-first platform designed to ingest, process, and unify unstructured data from a vast array of sources, transforming it into actionable, real-time context for AI agents and applications. Its core value proposition lies in eliminating the operational complexity of data pipelines, allowing developers and teams to instantly connect their tools and content—from documents and conversations to code and tickets—into a single, intelligently searchable knowledge graph. This enables AI systems to operate with accurate, up-to-date information, dramatically improving their reliability and effectiveness.

Key features: The platform automates the entire content lifecycle, starting with robust ingestion from over 100 connectors for platforms like Slack, GitHub, Jira, Google Drive, and websites via sitemap crawling. Once ingested, it enriches content through built-in services such as audio transcription via Deepgram, entity extraction, and summarization. All content is automatically indexed for vector similarity search, enabling semantic queries across diverse data types. Developers can leverage SDKs for Python, Node.js, and .NET to build custom workflows, content retrieval systems, or MCP (Model Context Protocol) servers, all managed through a single API without infrastructure overhead.

What sets Graphlit apart is its real-time synchronization capability and zero-ops architecture. Unlike static knowledge bases, it continuously monitors connected sources, ensuring the context provided to AI agents is always current. Technically, it handles multimodal data—text, audio, images—and normalizes it into a unified graph model. This deep integration with developer ecosystems and focus on live data sync positions it as more dynamic than traditional content management or vector database solutions, which often require manual updates and complex pipeline management.

Ideal for software development teams, product managers, and enterprises building AI-powered applications that require fresh, contextual data. Specific use cases include creating AI customer support agents with access to the latest internal documentation and support tickets, developing coding assistants that reference real-time GitHub commits and Jira issues, or building internal knowledge hubs that automatically aggregate updates from Slack channels and cloud storage. It is particularly valuable in fast-paced industries like technology, consulting, and media where information evolves rapidly.

Pricing starts with a generous free tier for exploration, with professional plans beginning at $49 per month for increased data volumes and advanced features. Enterprise plans offer custom scaling, dedicated support, and enhanced security controls for large organizations.