QM gives each employee and channel an isolated AI agent workspace with its own memory, files, permissions, and cron jobs while supporting shared scopes for collaboration in Slack or a web app. It’s model-agnostic—swap between Pi, OpenCode, Codex, or Claude Code—and offers admin controls for security postures, org-wide configs, and custom plugins. Deploy with the qm CLI by layering your org’s config and skills over the headless core, all under an MIT license.

• Every user and channel gets an isolated sandbox (memory, files, permissions, cron jobs) while still allowing shared skills across scopes, so teammates don't step on each other
• It's model-agnostic—swap between Pi, OpenCode, Codex, or Claude Code without changing the deployment
• Deployment cleanly separates org-specific config/skills from the upstream core, with helper skills (update-qm, upstream-pr) to sync a private fork in both directions
• Admins get org-wide controls over allowed harnesses/models, security postures (Strict, Auto, Dangerous), and skill/app access

This article discusses the rising demand for private AI solutions in Europe, particularly among small to medium enterprises. It highlights LokalGrid, an open-source platform that enables organizations to easily deploy and manage AI models on their own infrastructure without needing specialized teams.

• Mistral's €775M funding round underscores how urgent European data sovereignty has become, but small/medium orgs can't afford similar MLOps resources
• LokalGrid targets the gap between "too complex enterprise tools" and "no private AI at all" by letting general IT staff self-host models like Llama and Mistral
• Revenue model is freemium (free core, paid Team/Enterprise tiers), with growth driven by GitHub developer adoption and SEO tools like a "Sovereignty Cost Calculator"
• Competitive moat comes from API lock-in—once integrated, switching away from LokalGrid becomes costly, not from technical superiority alone