Quickstart
Get started with AI on Tiger Data. Set up pgai and pgvectorscale to automate embeddings, perform semantic search, and build intelligent applications.
By use case
Semantic search
Build high-performance semantic search with vector embeddings. Use pgvectorscale for billion-scale similarity search and intelligent document retrieval.
Multi-agent systems
Create enterprise-grade multi-agent systems. Deploy Slack-native AI agents with durable event handling, horizontal scalability, and complete observability.
RAG pipeline
Automate Retrieval-Augmented Generation workflows. Use the vectorizer to automatically generate, sync, and update embeddings from your data sources.
AI-powered analytics
Build organizational AI that integrates with your data. Connect Slack, GitHub, and Linear for real-time AI-powered insights and analytics.
By product
Tiger EON
Complete organizational AI that automatically integrates agents with your data from Slack, GitHub, and Linear. Process data in real-time with time-series partitioning.
Tiger Agents
Enterprise-grade Slack-native AI agents with durable event handling, horizontal scalability, and flexible model choices. Get complete observability and integrate with specialized data sources.
MCP Server
Integrate Tiger Data directly with AI assistants like Claude Code, Cursor, and VS Code. Manage services and optimize queries through natural language with secure authentication.
pgai
Automate AI workflows in your database with embeddings, vector search, and LLM integrations. Use the vectorizer to automatically generate and sync embeddings from your data.
pgvectorscale
High-performance vector search with StreamingDiskANN indexing. Extend pgvector with optimized algorithms for billion-scale vector workloads and faster similarity search.