AI project management with AMD GPU-accelerated Monte Carlo predictions. Multi-agent AI optimizes resources and automates workflows across Slack, Discord, Google Drive, Notion. Real-time transcription and intelligent task automation included.
Engineering teams face three critical challenges:
- Missed Deadlines — 40% of projects deliver late due to poor estimation and resource allocation
- Tool Chaos — Teams waste 15+ hours/week switching between Slack, Gmail, GitHub, Jira, Notion
- Guesswork Planning — Project managers can't answer "Can we deliver by this date?" with confidence
Result: Broken promises to clients, team burnout, and wasted resources.
Commando AI uses advanced algorithms and multi-agent AI to predict delivery timelines, optimize resources, and automate workflows — all in one unified platform.
Engineering Team → Commando AI → Predictable Delivery
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Monte Carlo Multi-Agent Workflow
Simulation AI Automation
(AMD GPU) (5 Agents) (10+ Services)
Three Core Engines:
- Predictive Delivery Engine — Monte Carlo simulation (10,000 scenarios) predicts delivery dates with 50%, 80%, 95% confidence intervals
- Multi-Agent AI System — 5 specialized AI agents (Optimizer, Manager, Developer, Cost Analyst, Risk Advisor) collaborate to optimize project execution
- Workflow Automation — Visual builder connects Slack, Discord, Google Drive, Notion, GitHub with drag-and-drop automations
- 3-5x faster Monte Carlo simulations than CPU-only solutions
- Run 10,000 delivery scenarios in under 2 seconds
- Real-time "what-if" analysis: Add developers? Remove scope? See impact instantly
- Uses AMD EPYC processors and Radeon GPUs with ROCm platform
Not just one AI — 5 specialized agents working together:
- Optimizer Agent → Identifies inefficiencies and bottlenecks
- Manager Agent → Makes resource allocation decisions
- Developer Agents → Validates technical feasibility and estimates
- Cost Analyst Agent → Finds cost-saving opportunities ($7,900/sprint average)
- Risk Advisor Agent → Assesses and mitigates project risks
Powered by Google Gemini 2.5-flash with function calling
Uses contextual bandit ML to learn optimal task assignments:
- Learns from past allocation successes/failures
- Balances speed, quality, and team health (Pareto optimization)
- Detects burnout risk before it happens
- 92% accuracy in skill-task matching
One dashboard for everything:
- Gmail, Slack, Discord, GitHub, Notion, Google Drive
- AI prioritizes tasks (saves 45 min/day)
- Context preserved across conversations
- Smart notifications filter 80% of noise
- Live transcription via OpenAI Whisper
- Auto-extracts action items and assigns owners
- Generates summaries and posts to Slack/Email
- Multi-language support
| Feature | Description |
|---|---|
| Predictive Delivery | Monte Carlo simulation with confidence intervals (50%, 80%, 95%) |
| What-If Scenarios | Test decisions before committing to clients |
| Resource Planning | AI-powered allocation with burnout detection |
| Workflow Automation | Visual builder with 15+ node types |
| Meeting Transcription | Real-time Whisper-powered transcription |
| GitHub Integration | Full GitHub App with webhooks and OAuth |
| Multi-Agent AI | 5 specialized agents for optimization |
| Role-Based Views | 6 department dashboards (Dev, PM, Exec, Finance, Sales, QA) |
| IDE Integration | MCP server for VS Code/Copilot/Claude/Cursor |
| Service Integrations | Slack, Discord, Google Drive/Gmail/Calendar, Notion |
Infrastructure:
- AMD EPYC 9004 processors (96-core)
- AMD Radeon Instinct GPUs
- ROCm 6.0+ (open-source GPU computing)
Frontend:
- Next.js 14 (App Router)
- React 18 + TypeScript
- Tailwind CSS + shadcn/ui
- ReactFlow (workflow builder)
Backend:
- PostgreSQL + Prisma ORM
- Clerk Authentication
- Google Gemini 2.5-flash
- OpenAI Whisper
- Stream.io Video SDK
Integrations:
- Google Workspace (Gmail, Drive, Calendar)
- GitHub (App + OAuth)
- Slack, Discord, Notion
- Stripe (payments)
- AI Task Generation — Generate epics and stories from project context
- Sprint Planning — AI populates sprints based on capacity and priorities
- Monte Carlo Predictions — See P50, P80, P95 delivery dates
- What-If Analysis — Test scenarios before promising clients
- Resource Dashboard — Heatmaps, utilization, burnout alerts
- PM AI Assistant — Chat with AI to create tasks, plan sprints, get insights
- Unified Inbox — All tasks from Slack, GitHub, Jira in one place
- AI Prioritization — Smart sorting by urgency and impact
- GitHub Integration — Issues, PRs, commits visible in dashboard
- Context Preservation — AI remembers your conversation across chats
- MCP IDE Tools — 26 tools for VS Code/Copilot to access project data
- Delivery Confidence — Real-time probability of on-time delivery
- Portfolio Health — Multi-project overview with risk indicators
- Cost Analytics —
- Delivery Confidence — Real-time probability of on-time delivery
- Portfolio Health — Multi-project overview with risk indicators
- Cost Analytics — Budget tracking and ROI analysis
- Team Utilization — Company-wide resource allocation view
- Visual Builder — Drag-and-drop workflow editor (like Zapier)
- 15+ Node Types — Triggers, actions, conditions, AI processing
- Auto-Execution — Smart dependency-based execution order
- Template Variables — Pass data between nodes dynamically
Example Flow:
PR Merged (GitHub) → Update Jira → Post Slack → Generate Changelog → Email Team
- 45 min/day — Unified inbox vs. tool switching
- 15 min/meeting — Auto-transcription and summaries
- 2 hrs/sprint — AI-powered planning
- 12 min/PR — Automated workflows
Total: ~22 hours/week per team
- $7,900/sprint — AI optimization recommendations
- 30% reduction — Bugs through quality tracking
- 27% improvement — Delivery predictability
- 3-5x faster — Monte Carlo simulations vs CPU
- 10% cheaper — AMD EPYC vs Intel equivalents
- 20% cheaper — AMD Radeon vs NVIDIA GPUs
- $34,000/year — Savings for mid-size teams
- Node.js 18+
- PostgreSQL database
- AMD GPU (optional, recommended for predictions)
# Clone the repository
git clone https://github.com/Virushacks/commando-ai.git
cd commando-ai
# Install dependencies
npm install
# Set up environment variables
cp .env.example .env
# Edit .env with your API keys
# Set up database
npx prisma db push
npx prisma db seed
# Run development server
npm run devVisit http://localhost:3000
# Database
DATABASE_URL="postgresql://..."
# Authentication
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY="pk_..."
CLERK_SECRET_KEY="sk_..."
# AI
GOOGLE_GENERATIVE_AI_API_KEY="..."
OPENAI_API_KEY="sk-..."
# AMD Cloud (optional)
AMD_GPU_ENDPOINT="..." # AWS g4ad or Azure NVv4- Technical Architecture — Deep dive into algorithms and system design
- Cost Estimation — Infrastructure costs and ROI analysis
- Feature Breakdown — Complete feature list with time/cost savings
- API Reference — API endpoints and integration guides
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
MIT License - see LICENSE for details.
- AMD — For EPYC processors and Radeon GPUs powering our predictions
- Google — For Gemini 2.5-flash AI capabilities
- OpenAI — For Whisper transcription
- Stream.io — For video conferencing infrastructure
- Website: commandoai.app
- GitHub: @commando-ai
Built with ❤️ for engineering teams who want to deliver on time.