Metabocommand

Inspiration MetaboCommand was inspired by the idea that ecommerce companies behave like living metabolic systems: capital flows, inventory moves, customer demand rises and falls, and operational stress shows up as symptoms before it becomes failure. We wanted to build an AI command center where specialized agents monitor those systems continuously, surface anomalies, propose actions, and route risky decisions through human approval.

What it does MetaboCommand is a multi-agent operating dashboard for ecommerce finance and operations teams. It organizes 12 specialized agents across capital, revenue, inventory, customer lifetime, and operational health. Agents detect issues like margin erosion, stockout risk, churn spikes, wasteful spend, logistics delays, and approval bottlenecks. The platform includes realtime approval queues, agent action logs, activity history, role-based dashboards, Slack notifications, and Supabase-backed presence so teams can see who is reviewing or acting on decisions.

How we built it We built MetaboCommand with Next.js 16, React 19, TypeScript, Tailwind, Supabase Auth, Postgres, Realtime, Presence, and Recharts. Supabase handles authentication, role routing, row-level security, database state, and realtime subscriptions. The frontend is structured around finance and operations dashboards, with each agent represented as a focused decision surface. We seeded realistic data for approvals, agent logs, activity history, thresholds, operating modes, and agent recommendations so the product feels like a working command center rather than a static mockup.

Challenges we ran into The hardest part was making the system feel operational instead of decorative. We had to design dashboards that could show a lot of information without becoming noisy, and make every agent output feel tied to a real business action. Realtime collaboration added complexity too: approval presence, activity status, live log updates, and role-scoped data all had to work together without confusing the user. Another challenge was balancing autonomy with governance: agents can recommend and auto-execute low-risk actions, but higher-risk changes need approval and auditability.

Accomplishments that we're proud of We’re proud that MetaboCommand feels like a real operating system for a business, not just a chatbot wrapper. The finance and operations dashboards are fully built out, the 12-agent model is clear, and the approval queue, agent action log, activity history, settings, and profile flows make the product feel complete. We’re especially proud of the realtime approval and presence system, because it turns AI recommendations into a collaborative workflow where humans stay in control of meaningful decisions.

What we learned We learned that agentic products need more than intelligence. They need interfaces for trust: approvals, audit trails, role boundaries, explanations, and clear operating modes. We also learned that good multi-agent design starts with business metabolism, not generic agent roles. When agents are mapped to concrete systems like capital velocity, conversion, retention, logistics, and support, their outputs become much easier to understand and act on.

What's next for MetaboCommand Next, we want to connect MetaboCommand to live ecommerce and finance systems such as Shopify, Stripe, QuickBooks, NetSuite, Slack, and warehouse tools. We also want to replace seeded data with real event streams, add agent pause/resume controls, expand Slack and email approval workflows, and introduce stronger anomaly detection and forecasting models. Longer term, MetaboCommand can become a full autonomous commerce operations layer where agents continuously monitor the business, propose actions, execute low-risk optimizations, and escalate strategic decisions to the right human.

Built With

• css
• custom
• medo
• react
• recharts
• typescript