Engineering withSAMI
A council of AI models that debate, vote, and reach consensus before any code is accepted. Chat stays on your device. No API key needed.
Every run is reviewed by all models before code ships
You choose the review depth for each run. Every selected model participates in all 7 structured phases — from requirements to final audit — and each casts an equal vote before any result is accepted.
Core Features
Context Engine, multi-stage voting, security red-teaming, and repo-aware retrieval — all in a single desktop workflow.
ExploreArchitecture
Multi-agent consensus, hybrid context search, and the full system stack — visualized layer by layer.
Deep DivePlans & Pricing
Flexible plans from Free to Enterprise with managed models and RAG on Pro, Team, Ultra, and Enterprise.
CompareDocumentation
Everything from getting started to security, privacy, integration setup, and the desktop workflow.
Read DocsWhat is SAMI-Agent?
Most developers juggle several AI assistants in separate tabs. SAMI-Agent lets you pick the models that fit the task - one for a second opinion, two for a cross-check, or up to ten for a full Council - and they debate your code in the same editor.
One workflow, no copy-paste marathon. Built for the decisions that actually matter - architecture, security, complex refactors.
What is SAMI-Agent?
Strategic Agentic Multilayer Intelligence (SAMI) is a dedicated desktop IDE designed for high-stakes engineering. While a single AI assistant excels at snippets, it can still miss context in isolation. SAMI replaces the manual “copy-paste marathon” between browser tabs with Adversarial Consensus: a Council of up to 10 AIs that debate, critique, and cross-validate each other’s logic directly in your editor.
The workflow follows a rigorous 7-Stage Pipeline- from context retrieval and architecture planning to red-teaming and final auditing. Instead of dumping your entire repo into a prompt, SAMI’s Hybrid Context Engine uses vector and keyword RAG to feed each stage only the essential data. This structured approach ensures that what what one model misses, another can catch, maintaining surgical precision even in massive codebases.
SAMI is built for the decisions that actually matter: complex refactors, security-sensitive logic, and performance optimization. You maintain absolute control throughAutonomy Levels, watching the debate unfold live with token-by-token transparency. Every tool call or file edit requires your approval (unless you opt for full-auto), and every decision is recorded in an immutable Audit Log - providing the traceability required by regulated industries.
Unlike cloud-heavy tools, SAMI prioritizes local control and a privacy-first workflow (chat stays on your device; see our Privacy Policy for details). It provides the industrial-grade scrutiny serious software engineering requires, rather than just slapping code together. Sign up, download the desktop app, and experience an AI workflow that doesn’t just guess - it examines, debates, and verifies.
Why Adversarial Consensus?
Single models have blind spots. SAMI eliminates them by letting multiple AIs debate your architecture before a single line is written.
The Status Quo
Juggling browser tabs between separate AI tools. Manual copy-pasting leads to context rot, version fragmentation, and unvetted hallucinations.
The SAMI Standard
A unified War Room where selected AI perspectives debate. Every proposal is cross-checked against your actual repository via the Hybrid Context Engine.
Consensus Review Perspectives
| Scenario | Single-model toolsone assistant at a time | SAMI-AgentConfigurable AI review |
|---|---|---|
| One AI introduces a security vulnerability | The flaw slips into your code unnoticed. You may only find it on the next pen-test. | Another AI says: “That’s vulnerable to X.” The debate happens before code is written — the flaw never lands. |
| A solution is clumsy or slow | You get exactly that one approach. You have to spot alternatives or demand them yourself. | Multiple AIs propose variants, compare trade-offs, and agree on the most robust one. |
| Audit trail for regulated environments | No trace of how the AI decided. You have to reconstruct compliance manually. | Full discussion and voting recorded - auditable, exportable, GDPR-compliant. |
One AI introduces a security vulnerability
- Single-model tools
- The flaw slips into your code unnoticed. You may only find it on the next pen-test.
- SAMI-Agent
- Another AI says: “That’s vulnerable to X.” The debate happens before code is written — the flaw never lands.
A solution is clumsy or slow
- Single-model tools
- You get exactly that one approach. You have to spot alternatives or demand them yourself.
- SAMI-Agent
- Multiple AIs propose variants, compare trade-offs, and agree on the most robust one.
Audit trail for regulated environments
- Single-model tools
- No trace of how the AI decided. You have to reconstruct compliance manually.
- SAMI-Agent
- Full discussion and voting recorded - auditable, exportable, GDPR-compliant.
Side-by-side comparisons: SAMI vs GitHub Copilot · SAMI vs Cursor
Up to 10 models debate your architecture before a line of code is written.
Free plan. No API key needed. Chat stays on your device.
Get Started for Free