Graphlit provides a comprehensive context layer designed to empower AI agents with relevant, up-to-date data. By bridging the gap between disconnected enterprise data sources and intelligent agents, Graphlit ensures that LLMs have the accurate background information required to perform complex tasks reliably. The platform is engineered to eliminate the manual overhead typically associated with building custom data retrieval pipelines, offering developers a streamlined solution for managing and querying unstructured information.

Functionally, the platform serves as an ingestion and retrieval engine that automatically synchronizes data from various productivity and development tools. It transforms this data into a searchable, semantic representation that AI agents can access via a unified API. By handling the complex work of data extraction, embedding generation, and indexing, the platform allows developers to focus on building agent logic rather than managing data infrastructure.

Some of the key features are:

• Real-time Synchronization: Automatically keeps agent context up-to-date by syncing data from platforms like Slack, GitHub, and Jira.
• Unified API: Provides a single, consistent interface for ingesting and retrieving data from multiple diverse sources.
• Semantic Search: Includes built-in vector search capabilities to enable agents to find relevant information based on meaning rather than exact keyword matching.
• Zero-Ops Architecture: Minimizes maintenance requirements by automating the underlying data pipeline, indexing, and infrastructure management.
• Multi-Source Support: Aggregates information from various enterprise applications to create a comprehensive knowledge base for AI applications.
• Scalable Indexing: Efficiently processes and indexes large volumes of unstructured data to maintain performance as knowledge bases grow.

Users typically interact with the system by connecting their desired data sources through the provided API or platform interface. Once connected, Graphlit crawls and processes the information, converting documents and messages into vector embeddings stored in a managed database. When an AI agent needs context to answer a user query or perform a task, the agent queries the Graphlit API. The platform then performs a semantic search across the indexed data to retrieve the most pertinent snippets, which are provided to the agent as context for generating high-quality responses.

Some common use cases include:

• Customer Support Automation: Equipping AI support bots with historical ticket data and product documentation for accurate issue resolution.
• Engineering Documentation Retrieval: Enabling developer agents to query technical specifications and Jira tickets to assist with code reviews or debugging.
• Internal Knowledge Management: Allowing team-wide AI assistants to reference company Slack discussions and project documents to answer employee questions.
• Workflow Automation: Providing agents with the necessary data context to trigger and manage automated business processes across different SaaS applications.