Time-Aware Queries
Query knowledge as it existed at any point in time. Understand what was known when decisions were made.
Temporal Intelligence for Enterprise Knowledge
Standard RAG tells you what's true today. TeamLoop tells you how you got here.
Why Temporal Intelligence? Context that evolves with your team
Traditional search gives you today's answers. TeamLoop gives you the full story.
Decision Lineage
Track how decisions evolved, what they superseded, and why. Never lose context on architectural choices.
Knowledge Graphs
Automatically extract and connect entities from GitHub, Notion, and Linear into a unified knowledge graph.
AI Synthesis
Generate ADRs, PRDs, and executive briefs from curated knowledge subgraphs.
Get started in 3 simple steps
Connect TeamLoop to your AI assistant via MCP and start building temporal knowledge.
Core Capabilities
Everything you need to build and query temporal knowledge.
Temporal Queries
- Current state queries
- Point-in-time (as_of) queries
- Evolution tracking (from/to)
- Compare states across dates
- Decision lineage
Knowledge Graph
- Automatic entity extraction
- Relationship mapping
- Semantic search
- Version tracking
- Source attribution
AI Synthesis
- Executive briefs
- Architecture Decision Records
- Product Requirements Docs
- Custom templates
Frequently Asked Questions
Common questions about TeamLoop and temporal intelligence.
Getting Started
How to set up TeamLoop.
MCP & Integration
Configuring your AI assistant.
Features & Use Cases
What TeamLoop can do.
What is temporal intelligence?
Temporal intelligence means understanding how knowledge evolves over time. Unlike traditional RAG that only shows current state, TeamLoop tracks when decisions were made, what they superseded, and how your understanding changed.
What integrations are supported?
TeamLoop currently supports GitHub, Notion, and Linear. Connect via OAuth and start querying your knowledge across all three platforms.
Do I need to sync data beforehand?
No! TeamLoop uses a query-first model. Data is fetched fresh when you query and cached for future use. No background sync processes needed.
What is MCP and why do I need it?
MCP (Model Context Protocol) is an open standard for connecting AI assistants to external tools. TeamLoop provides an MCP server that gives Claude (or other MCP clients) access to your temporal knowledge.
Which AI assistants work with TeamLoop?
Any MCP-compatible client works with TeamLoop. Currently, Claude Desktop is the most popular option, but the ecosystem is growing.
How do I connect my tools?
Log into TeamLoop, go to Settings > Integrations, and click Connect for GitHub, Notion, or Linear. You'll be redirected to authorize access.
What are subgraphs?
Subgraphs are curated collections of related entities from your knowledge graph. Use them to organize knowledge for specific topics, projects, or synthesis tasks.
Can TeamLoop generate documents?
Yes! Use the synthesis feature to generate Architecture Decision Records (ADRs), Product Requirements Documents (PRDs), or executive briefs from your knowledge subgraphs.
How is data stored?
Your knowledge graph is stored in PostgreSQL with pgvector for semantic search. Each piece of knowledge is versioned with temporal metadata for point-in-time queries.