LaunchGate AI

# LaunchGate AI LaunchGate AI is a Band-powered command center for enterprise AI release governance. As organizations rapidly deploy AI products, every release must pass reviews from security, privacy, compliance, engineering, QA, and accountable human stakeholders. Today, these decisions are often scattered across Slack threads, Jira tickets, documents, emails, and meetings, making reviews slow, fragmented, and difficult to audit. LaunchGate centralizes the process in a Band-powered agent review room. Users submit release context such as release briefs, code changes, data flows, deployment settings, test plans, policies, and vendor agreements. Specialized AI agents collaborate in Band using distinct identities, share findings, hand off risks, and generate a final recommendation. The system includes six agents: * LaunchGate Coordinator * Security Reviewer * Privacy & Compliance Reviewer * Engineering Readiness Reviewer * QA Testing Reviewer * Decision Arbiter Built with Band, LangGraph, LangChain, FastAPI, AI/ML API and Next.js, LaunchGate produces an audit-ready decision dossier containing risk scores, findings, remediation tasks, approvals, and human decisions. In the demo scenario, LaunchGate reviews an AI Ticket Summarizer and identifies exposed payload logging, customer PII risks, missing prompt-injection and rollback tests, customer disclosure requirements, and external vendor retention concerns, resulting in a REQUEST_CHANGES recommendation with human escalation. LaunchGate also supports dynamic reviews. For example, HR resume-screening workflows automatically trigger fairness, bias, and protected-attribute risk analysis, demonstrating that findings are generated from uploaded context rather than predefined outputs. By combining multi-agent collaboration with human oversight, LaunchGate demonstrates how Band can power transparent, auditable, and scalable AI governance workflows.