This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
What I Built
DecisionDrop is an AI-powered decision-making agent that helps users research alternatives, compare trade-offs, and make informed decisions without spending hours gathering information manually.
Instead of providing only a chatbot response, DecisionDrop combines AI-assisted analysis, research integrations, and deterministic Multi-Criteria Decision Analysis (MCDA) to produce structured recommendations.
Key features include:
- AI-assisted decision analysis using open-weight models.
- Research integration to gather supporting information.
- Transparent, criteria-based scoring and ranking.
- Alternative comparisons and trade-off visualizations.
- Sensitivity analysis to explore how changing priorities affects recommendations.
- SQLite-based decision history.
- Local model inference through Ollama.
The goal is to reduce repetitive research and comparison work while keeping the decision-making process transparent.
Demo
🎥 Video demonstration:
Watch the DecisionDrop demo
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Code
💻 GitHub Repository:
https://github.com/msairitvik/DecisionDrop
How I Built It
DecisionDrop uses a modular full-stack architecture:
- Frontend: React 19 and Vite
- Backend: Python and FastAPI
- Database: SQLite
- Local inference: Ollama
- Open-weight models: Qwen3 4B, Gemma 2 2B, and Qwen3 0.6B
- Testing: Pytest
The agent pipeline organizes the decision-making workflow, from understanding the user's problem to analyzing alternatives and presenting recommendations.
Open-weight models assist with analysis and structuring information, while a deterministic MCDA scoring engine calculates rankings based on evaluation criteria. This separation helps make recommendations more transparent and easier to inspect.
Why Does Open Innovation Matter?
Open innovation makes it possible to build AI applications without depending entirely on closed, proprietary model APIs.
Using open-weight models with Ollama gives users greater control over inference and enables experimentation with different models. The modular architecture also creates opportunities for contributors to improve the research pipeline, scoring engine, and user experience.
The aim is to build an AI system that people can inspect, adapt, and extend rather than treating AI as a black box.
My Agent Session
DecisionDrop was developed with AI-assisted coding.
Optional: Add your shareable DevRelay session link here if you saved one. Otherwise, remove this section.
The Lazy Payoff
DecisionDrop aims to reduce the manual effort involved in researching options, comparing alternatives, and calculating weighted scores.
By bringing research, scoring, and trade-off analysis into one workflow, it helps users spend less time organizing comparisons and more time evaluating the results.
I haven't measured the actual time saved yet, so I won't claim a specific number. Benchmarking manual decision-making against the agent's workflow would be a useful next step.
Prize Categories
Add the exact eligible prize categories from the official challenge rules that apply to this project. Remove this section if it isn't required.
Built for the Hacktoberfest Open-Source AI Challenge — Week 3: Slow and Steady.
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