Two editions of an open-source LLM Knowledge Base purpose-built for team chat — Open Source (Apache 2.0) for individuals · Enterprise for teams. A searchable, citation-bearing memory layer answering OpenAI founding member Andrej Karpathy's viral call for "an incredible new product." OpenClaw and Hermes Agent integration shipping in Q2 2026.

TORONTO, CANADA/HONG KONG SAR - Media OutReach Newswire - 15 May 2026 - Hong Kong and Toronto-headquartered enterprise AI company Votee AI, together with its Toronto-based research lab Beever AI, today open-sourced Beever Atlas — an LLM Knowledge Base shipping in two editions: an Apache 2.0 Open Source Edition for individuals, and an Enterprise Edition for teams (banks, government agencies, and large organizations with high-security requirements). Beever Atlas automatically transforms personal and team chat across Telegram, Discord, Mattermost, Microsoft Teams, and Slack into a structured Neo4j knowledge graph, auto-generated wiki, and MCP-ready memory layer for any AI assistant. Votee AI (Votee Limited) is headquartered in Hong Kong and Toronto, with operations across Asia. Beever AI is its dedicated AI research lab based in Toronto. Answering a Viral Call from the AI IndustryAndrej Karpathy — OpenAI founding member and former director of AI at Tesla — shared a viral post on X about "LLM Knowledge Bases" that drew tens of millions of impressions. Karpathy's core argument: LLMs need structured, evolving knowledge — not just raw context windows or vector similarity search. He concluded with a direct call to the industry:"I think there is room here for an incredible new product instead of a hacky collection of scripts." Beever Atlas is that product — built first for teams, with an Open Source edition for individuals. Karpathy's prototype starts with curated file ingestion, relies on Obsidian and an LLM coding agent (Claude Code / Codex), and is single-user and largely manual. Beever Atlas takes a fundamentally different starting point: team chat. Because the bulk of organizational knowledge lives — and dies — in the unstructured conversations inside Telegram, Discord, Mattermost, Microsoft Teams, and Slack. "Hong Kong has always been known for property and finance," said Pak-Sun Ting, Co-Founder and CEO of Votee AI. "Beever Atlas is proof that world-class AI infrastructure can emerge from an HK-headquartered company and be shared openly with the world. Every growing organization faces the same silent liability: conversational knowledge loss. Beever Atlas turns this perishable resource into a compounding organizational asset." Key Differences from Karpathy's Local Approach Beever Atlas extends the LLM Knowledge Base pattern in six fundamental ways:

1. Chat-native ingestion across Telegram, Discord, Mattermost, Microsoft Teams, and Slack — not manual file uploads.
2. Zero-install web UI — no Obsidian or command-line interface required.
3. Multimodal intelligence — text, images, voice, video, and PDFs unified in one searchable memory layer (not text-only).
4. Multi-user and team-ready architecture — not single-user only.
5. Full Neo4j knowledge graph with typed entity relationships between people, projects, technologies, and decisions — not text-only cross-references.
6. Native MCP server integration — Cursor, AWS Kiro, Qwen Code, OpenClaw (coming), and Hermes Agent (coming) — or any AI assistant — can query team knowledge directly. Karpathy's prototype has no agent integration.

OpenClaw and Hermes Agent Integration — Upcoming Feature for the Open-Source Edition Beever Atlas will ship a dedicated update in Q2 2026 for OpenClaw and Hermes Agent. The integration lets both tools read and write to a user's Beever Atlas memory layer natively — making it among the first MCP-native knowledge backends purpose-tuned for these workflows. Solo developers and small teams will be able to point either tool at a personal or shared Beever Atlas instance and have it cite, retrieve, and chain across the entire conversational memory. The Technical Bet: Structure Beats Similarity"The key technical decision was to treat agent memory as a knowledge engineering problem, not a retrieval problem. Structure beats similarity — a typed graph of who works on what is more useful to an AI than vector search over a Slack archive," - Jacky Chan, Co-Founder and CTO of Votee AI (developer of the first fully pre-trained open-source Cantonese LLM) Beever Atlas ships with a native MCP server, letting AWS Kiro, Qwen Code, Cursor, or any AI assistant query team knowledge directly — making it the memory layer that every downstream AI agent has been missing. Built for Sovereignty — 100% On-Premise, Bring Your Own LLM Beever Atlas runs entirely in customer environments as a Docker stack. Zero telemetry. AES-256-GCM encryption at rest. Private channels are filtered by...