Beever Atlas: AI-Powered Knowledge Management from Team Chats

Beever Atlas is an innovative, AI-powered knowledge management platform designed to automatically transform your team's chat conversations from Slack, Discord, Microsoft Teams, and Mattermost into a self-maintaining, structured wiki. Its primary purpose is to distill noisy, redundant chat history into actionable, searchable knowledge, making critical information easily accessible and reducing information silos.

This solution is ideal for teams, enterprises, small and medium businesses, startups, and nonprofits seeking to enhance knowledge sharing, streamline onboarding, and improve decision-making by leveraging their existing communication data.

Key Features

• Multi-Platform Connect: Seamlessly integrates with Slack, Discord, Microsoft Teams, Mattermost, and supports file imports.

• LLM Wiki: Generates and auto-maintains a structured wiki per channel, complete with overviews, topics, people, decisions, and traceable citations.

• QA Agent: Provides cited answers via a smart router that intelligently selects between semantic or graph-based retrieval strategies.

• Memory Ingestion: Utilizes a 6-stage ADK pipeline to distill messages into atomic facts, entities, and relationships.

• MCP Server: Offers a Model Context Protocol (MCP) server to plug into external AI agents like Claude Code and Cursor, providing 16 specialized tools.

Use Cases

Beever Atlas excels in several scenarios. For knowledge management, it automatically builds a structured knowledge base from daily team communications, eliminating the need for manual wiki updates. This is particularly beneficial for onboarding new team members, who can quickly grasp channel context by reading a distilled wiki instead of sifting through months of chat history.

It also significantly aids in decision tracking and project context by extracting decisions, entities, and their relationships from conversations. This provides a clear historical context for projects and discussions, helping teams quickly find answers to complex relational questions like "who worked on X with Y?"

Furthermore, it provides an enhanced Retrieval-Augmented Generation (RAG) source for AI agents. By working with clean, deduplicated, and structured knowledge, Beever Atlas delivers more accurate answers, fewer hallucinations, and traceable citations compared to traditional RAG systems that rely on raw chat logs.

Pricing Information

Beever Atlas is an open-source project, making the software itself free to use and self-host. However, it requires API keys for external services like Gemini (for extraction, entity graph, and answers) and Jina v4 embeddings (for semantic search), which offer generous free tiers but may incur costs with higher usage.

User Experience and Support

The platform features a user-friendly web dashboard accessible via a local URL, offering both a "Real mode" for connecting workspaces and a "Mock mode" for UI iteration without platform credentials. Comprehensive documentation is available at docs.beever.ai/atlas. Community support is provided through Discord, X/Twitter, and GitHub Discussions, with commercial support and partnerships available via email.

Technical Details

Beever Atlas is primarily built with Python (70.7%) and TypeScript (28.0%), utilizing FastAPI for the backend and React for the frontend. It employs uv for Python dependency management and Docker Compose for deployment. Its robust architecture includes Weaviate for semantic memory, Neo4j for graph memory, MongoDB for state and wiki cache, and Redis for sessions.

Pros and Cons

• Pros: Automated wiki generation from chat; Dual-memory architecture (semantic + graph) for precise retrieval; High-quality, cited answers with fewer hallucinations; Integrates with major chat platforms; MCP server for external AI agent integration; Open-source and self-hostable; No telemetry collected, ensuring privacy.

• Cons: Requires external API keys (Gemini, Jina) which may incur costs; API is currently UNSTABLE (v0.1.0) with potential breaking changes; Initial setup involves Docker Compose and API key configuration; Potential learning curve for advanced features like the MCP server.

Conclusion

Beever Atlas offers a unique, AI-driven solution to transform chaotic team communications into a structured, searchable knowledge base. By distilling chat into an auto-maintained wiki, it provides accurate, traceable answers and a browsable artifact for improved team efficiency and onboarding. Explore Beever Atlas to unlock the hidden knowledge within your team's conversations.

For enterprise version, contact: hello@votee.ai