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🤝 Top Collaborators
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🤓 Latest Submissions
ReguAI
ReguAI is an intelligent incident response and compliance workflow platform that automates the entire incident management lifecycle. When production incidents occur, engineering teams face critical challenges: responding quickly while maintaining comprehensive documentation for compliance, audits, and post-mortem analysis. ReguAI solves this by transforming raw incident data into comprehensive, audit-ready documentation instantly. The platform features intelligent rule-based AI report generation that automatically creates multi-section incident reports including risk classification, timeline reconstruction, compliance checklists, technical action plans, stakeholder summaries, and detailed post-mortem analysis. A real-time dashboard monitors all incidents with live metrics showing total incidents, active cases, resolution times, and severity breakdowns. Automated workflow management creates and tracks incident response tasks with priority levels, ownership assignment, and status tracking. Every action is automatically logged with timestamps, actors, and state changes in compliance-ready audit trails that satisfy SOC2, ISO 27001, and other regulatory requirements. The platform exports incidents as formatted Markdown post-mortems ready for stakeholder review. Smart template selection based on incident type (Security, Payment, API Contract, General) ensures relevant, context-aware report generation. Built on Next.js, React, TypeScript, PostgreSQL with Prisma ORM, and containerized with Docker, ReguAI uses a deterministic rule-based AI approach ensuring zero latency, 100% reliability, cost efficiency, offline capability, and fully explainable audit compliance. Perfect for startups needing compliance documentation, scale-ups managing growing incident volumes, and enterprises requiring audit-ready incident management.
17 May 2026
AMD Smart Product Assistant API
Our project is an AI-powered Smart Product Assistant designed to help users discover, understand, and compare products more easily through natural language interaction. The system allows users to ask questions about products, get intelligent recommendations, compare specifications, and receive helpful explanations based on product data. The main goal of this project is to create a practical AI assistant that can improve the product exploration experience, especially for users who need fast, clear, and personalized information before making a decision. Based on the PRD, this project includes several key components: a backend API, AI model integration, product data management, user interaction flow, and a frontend interface that connects directly with the AI assistant. The backend is designed to handle requests, manage product-related data, connect with the AI model, and return accurate responses to the user. The AI system has also been connected to Ollama 3 running on AMD Developer Cloud, allowing the assistant to process user prompts and generate relevant responses. This setup demonstrates how AMD cloud infrastructure can be used to run AI workloads and support real-world application development. This project was fully designed and developed by two Senior Fullstack Engineers, Agung Laksono and Naila Sijabat. We worked on the system from end to end, including backend architecture, API development, frontend integration, AI connection, database preparation, and deployment setup. We are submitting this project to Lablab AI as a completed solution that demonstrates our ability to build an AI-powered product assistant with practical use cases, scalable architecture, and real implementation using modern AI technology.
10 May 2026
