SAMI
A council of AI models reviews your architecture before a line of code is written.
How a run works
Every selected model works all five stages and casts an equal yes/no vote on each.
- 01 Understand & Contract
- 02 Plan
- 03 Build
- 04 Harden
- 05 Acceptance
Every stage ends in a yes/no vote from every model.
How a No resolves
A No never ends the run: it loops back to deliberation, the council brings counter-arguments, and it works toward a genuine Yes. If the objection is not resolved within a bounded number of rounds — or the run budget runs out, or you cancel — the stage moves on with it recorded as unresolved, visible in the War Room.
Core features
The five stages, the Context Engine and the desktop app.
Architecture
The council and the full system, visualized layer by layer.
Plans & pricing
Compare what each plan includes, from Free to Enterprise.
Documentation
Everything from getting started to security, privacy, integration setup, and the desktop workflow.
What is SAMI?
SAMI puts a managed council of models to work in one editor: they debate, cross-check and vote on every stage before code ships.
SAMI in detail
Strategic Agentic Multilayer Intelligence (SAMI) is a desktop IDE for high-stakes engineering. Instead of copying code between AI assistants in separate browser tabs, you get Adversarial Consensus: a managed council of models that debates, critiques and cross-checks the work in your editor, every model with an equal vote.
Every run follows the same five stages, and each stage ends in a yes/no vote. SAMI’s Context Engine uses semantic and keyword retrieval to give each stage only the context it needs, not your whole repository.
You stay in control through Autonomy Levels, from approving every tool call (L1, the default) to full auto (L3). The council reviews every run at every level; autonomy only sets how often the work pauses for you. The stages, votes and results are recorded, so you can replay how a decision was reached.
Your chat history stays on your device; your task and the context the council reads are sent to SAMI’s service for the run in progress (see our Privacy Policy). Sign up, join the desktop app waitlist, and get ready to experience a workflow that examines, debates and verifies.
Why Adversarial Consensus?
Single models have blind spots; a council catches them before code is written.
| Scenario | Single-model tools | SAMI |
|---|---|---|
| One model introduces a security vulnerability | The flaw slips into your code unnoticed. You may only find it on the next pen-test. | Another model objects: “That’s vulnerable to X.” It is worked through before the stage is sealed. |
| A solution is clumsy or slow | You get exactly that one approach. You have to spot alternatives or demand them yourself. | Multiple models propose variants, compare trade-offs and settle on one. |
| Tracing how a decision was reached | No trace of how the AI decided. You have to reconstruct it manually. | The stages, votes and results are recorded, so you can replay how a decision was reached. |
Side-by-side comparisons: SAMI vs GitHub Copilot · SAMI vs Cursor