Inspiration
Project planning is often based on static spreadsheets and optimistic assumptions. We wanted to build a system that treats planning like a probabilistic science — combining AI reasoning with quantitative risk modeling to reduce uncertainty before execution begins.
What it does
AI Project Planner and Risk Analyzer transforms a simple project idea into a structured, predictive execution blueprint. It generates a detailed Work Breakdown Structure (WBS), timeline forecasts with confidence intervals, budget projections, risk probability analysis, and resource allocation plans. Using Monte Carlo simulation and weighted risk scoring, it quantifies uncertainty and presents insights through an interactive dashboard with KPI cards, Gantt charts, heatmaps, and scenario comparisons.
How we built it
We built a full-stack web application using Next.js and Node.js. MongoDB stores session-based project data and versioned blueprints. Google Gemini powers structured scope decomposition, risk identification, and executive summaries. A custom simulation engine performs Monte Carlo forecasting and confidence scoring. The UI follows a clean SaaS design system with a dynamic carousel and responsive dashboard components.
Challenges we ran into:
Enforcing strict structured JSON from AI outputs Designing realistic probability distributions Balancing AI flexibility with deterministic simulation logic Maintaining UI professionalism without making it feel AI-generated Accomplishments that we're proud of Built a fully predictive planning engine, not just a text generator Implemented real probabilistic modeling with confidence scoring Created a polished, investor-ready SaaS interface Designed an adaptive forecasting mode that updates dynamically
What we learned:
We learned that AI reasoning is powerful, but it becomes significantly more impactful when combined with statistical modeling. Structured outputs and validation layers are critical for production reliability.
What's next for AI Project Planner and Risk Analyzer
Add historical learning to improve forecast accuracy over time Introduce team collaboration features Integrate real-time execution tracking vs predictions Expand into enterprise planning and portfolio-level forecasting Integrate with Stripe API to monetize the app.
Log in or sign up for Devpost to join the conversation.