Amna Rauf@amna_rauf435

FailureTwin AI is an AI-powered pre-mortem decision workspace designed to help founders, product managers and operations teams discover how a plan could fail before committing time, money or resources. Instead of simply generating a plan, FailureTwin challenges it from four perspectives: Customer, Operations, Finance and Risk. Users describe a launch, workflow, automation or business decision, answer focused follow-up questions, and receive a structured analysis of weak assumptions, evidence gaps and high-impact risks. FailureTwin then connects those risks into failure chains, showing how one weak assumption can cascade into operational, financial or customer consequences. The Decision Room recommends safeguards that users can accept or reject while the readiness score updates from an immutable baseline, preventing scoring drift. The workflow ends with an explainable Go, Revise or Stop Decision Brief containing the most important risks, safeguards, confidence level and next actions. The project includes guided scenarios for AI customer support, delivery expansion and hospital claim automation, plus custom simulations. A deterministic fallback engine keeps the experience reliable if the live AI path is unavailable. Our goal is simple: help teams find expensive mistakes while they are still inexpensive to fix.

🌎 Online Hackathon | 📅 August 3–10, 2026| 💻 Powered by native.builder The official native.builder hackathon, hosted by NativelyAI and lablab.ai. Join founders, developers, designers, and builders to create real AI-native applications powered by native.builder: from idea to deployed product, fully in the cloud. 💻 This hackathon is fully online. No travel required.