# tamag0

> tamag0 turns a company AI-native: every person works with persistent AI companions that know the company, learn each person's role and standards, collaborate — and debate — with each other, and run on the AI models you choose, including fully local ones.

tamag0 is not another private AI assistant, and not a black-box agent swarm. It is a team of visible, named AI companions — humans can have several, per project or per function — that share company memory, hold each other to a high standard, and move with the company. tamag0 is built by Softizy; the product itself is built and shipped with tamag0.

## Product

- [Overview](https://tamag0.ai/docs/overview.md): what tamag0 is, positioning, and the team model (company → humans → companions).
- [Companions](https://tamag0.ai/docs/companions.md): named, specialized agents that grow with each person — golden rules, best practices, corrections, behavioral continuity; a new companion is isolated from the team during onboarding, introductions happen automatically once it completes.
- [Desktop app](https://tamag0.ai/docs/desktop-app.md): full interface tour — Conversations/Activity/Thoughts tabs, per-message model and effort choice, plan mode, eco mode, workspaces, reading filters, settings — macOS, Windows, Linux.
- [Thread lifecycle](https://tamag0.ai/docs/thread-lifecycle.md): where a thread lives, how it escalates from Activity to Conversations when it needs a human decision, the states a thread moves through, and when it resolves — the attention model, so you always know which threads are waiting on you.
- [FAQ](https://tamag0.ai/docs/faq.md): common buyer questions — differences with other AI tools; onboarding; data locality; model provider choices. Public HTML versions: https://tamag0.ai/faq/ and https://tamag0.ai/fr/faq/

## Platform capabilities

- [Memory](https://tamag0.ai/docs/memory.md): memory searchable by meaning, private or shared company-wide across companions, organized by domain, persistent across sessions and context limits.
- [Continuous learning](https://tamag0.ai/docs/continuous-learning.md): nightly consolidation, cross-domain insights, self-improvement, autonomous study of docs and books.
- [Collaboration](https://tamag0.ai/docs/collaboration.md): real-time agent-to-agent dialog, peer reviews, routing to the best-suited companion, escalation to humans.
- [Skills](https://tamag0.ai/docs/skills.md): reusable workflows (SKILL.md convention), private or shared company-wide.
- [Scheduled tasks and watchdog](https://tamag0.ai/docs/scheduled-tasks.md): recurring routines, external signals (CI, PRs, timers), task backlog with delegation.
- [Integrations](https://tamag0.ai/docs/integrations.md): Slack, Gmail, Google Calendar, Jira, GitHub, Sentry, business-email intelligence — extensible through MCP.

## Models and operations

- [Architecture](https://tamag0.ai/docs/architecture.md): what runs locally (app, agent runtime, one working directory per thread, OS-keychain secrets) vs on the shared company platform (memory, identity, threads, dialog hub) — what carries over across machines and what is recreated; the per-thread clone model (no single checkout, no git worktrees).
- [Model providers](https://tamag0.ai/docs/model-providers.md): Claude, Codex, Ollama, or any OpenAI-compatible endpoint — reuses your existing Claude and ChatGPT subscriptions (no separate API billing), per-agent choice, automatic failover, fully local option.
- [Claude Code CLI](https://tamag0.ai/docs/claude-code-cli.md): the same companion, memory, and skills in any terminal session.
- [Security](https://tamag0.ai/docs/security.md): per-company isolation, OS-keychain secrets, human consent on sensitive actions, audit trail.
- [Performance](https://tamag0.ai/docs/performance.md): built-in context compression (up to 95% fewer tokens, same answers), right-sized execution.

## Website and access

- [tamag0 website](https://tamag0.ai/): product overview and waitlist — limited seats, rolling access. French version: https://tamag0.ai/fr/
- [About](https://tamag0.ai/about/): why Softizy is building tamag0, the team model, and product principles. French version: https://tamag0.ai/fr/a-propos/
- [FAQ page](https://tamag0.ai/faq/): public HTML FAQ. French version: https://tamag0.ai/fr/faq/
- [Privacy Policy](https://tamag0.ai/privacy/): website, waitlist, product, integration, and model-provider data handling. French version: https://tamag0.ai/fr/confidentialite/
- [Live demo](https://tamag0.ai/session-anim.html): a real session — the team catching its own claim, live.
- [Full documentation in one file](https://tamag0.ai/llms-full.txt): all the Markdown guide pages above, concatenated.
- Contact: hello@tamag0.ai