We’re excited to announce that the first fully deployable MVP version of the Grey Hat Labs Platform is now operational and available for internal testing. This marks a major milestone for the team—the first time the full multi-service system is running end-to-end in a production-like environment.
What We Accomplished
Over the course of this sprint, the team delivered:
🚀 A Cloud-Hosted, Secure Demo Environment
• The full GHL microservice architecture (schema-engine, rlie, kgf, orchestrator) is now deployed on AWS EC2.
• The environment is fully isolated, HTTPS-enabled, and accessible via a dedicated domain.
• External testers can now interact with the platform through a polished demo frontend.
⚙️ Robust CI/CD & Deployment Automation
• GitHub Actions now builds, tests, bundles, and publishes SHA-versioned Docker images to AWS ECR.
• A unified deployment script (deploy.sh) lets us roll out updates with a single command.
• All infrastructure steps are fully documented so future team members can deploy confidently.
🧩 Stability, Migrations & Service Interoperability
• Fixed cross-service environment variable inconsistencies.
• Standardized ports, networking, and service-to-service URLs.
• Ensured all services compile cleanly, include proper dist bundles, and run identically locally and in staging.
🔐 Production-Grade Networking
• Added an Nginx reverse proxy for routing and TLS termination.
• Implemented Let’s Encrypt certificates for secure external access.
• Ensured non-public endpoints remain internal and protected.
📘 Documentation
• Created a complete EC2 Deployment Runbook.
• Updated .env.example, docker-compose files, and internal service docs.
• Prepared everything for the next phase: user validation.
What’s Next
With the MVP deployed, we are shifting focus toward:
• Guided demo sessions
• User validation and structured feedback
• Iterating on the admin tooling and access controls
• Exploring hosted options for scalable production deployment
This milestone proves the architecture works end-to-end and sets the stage for rapid iteration and onboarding of early users.
