Graphlit

Rather than building content ingestion, knowledge graph construction, and semantic retrieval from scratch, development teams use Graphlit to deploy production-ready knowledge infrastructure through a single unified API.

The platform's content ingestion capabilities span the full breadth of organizational information sources.

Automatic transcription with speaker diarization handles audio and video content; OCR with vision models processes complex documents; web crawling with search integration captures online content; and native connectors for Slack, Microsoft Teams, Gmail, Outlook, GitHub, and Jira bring communication and project management data into the knowledge graph.

This multi-source ingestion means Graphlit can serve as the organizational memory layer for AI systems that need to reason across the complete information landscape of a business.

Graphlit's knowledge graph engine automatically extracts entitiespeople, organizations, concepts, and relationshipsfrom any ingested content, building a queryable network that enables semantic reasoning across document boundaries.

This graph-based approach reveals connections that keyword search cannot surface: the relationship between a mention in a meeting transcript and a GitHub issue, or the pattern of expertise signals distributed across thousands of Slack messages and email threads.

The retrieval layer offers multiple query modalities designed to support different AI application patterns. Semantic search finds content by meaning rather than keywords, enabling natural language queries that surface relevant results even when exact terminology differs.