Inspiration
What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of*Inspiration*
We noticed that in most companies, critical operational decisions — like detecting machine failures or stockout risks — still depend on manual monitoring, scattered spreadsheets, and delayed human responses. This leads to unnecessary downtime, lost revenue, and slow action. We were inspired to build a system that could identify risks early, recommend the right actions, and even execute them automatically. The goal was simple: turn raw data into real decisions.
What We Learned
- How to design a full AI pipeline: ingestion → prediction → reasoning → action.
- Using AI agents to break complex tasks into modular steps.
- Building end-to-end automation using Base44 (data tables, triggers, workflows).
- How risk scoring models work, including basic ML heuristics and feature engineering.
- The importance of auditability, logging, and orchestrating multi-step actions.
How We Built It
- Created a data ingestion module to upload CSVs and convert rows into Incidents.
- Built preprocessing steps to clean data and compute features like [ \text{days_of_supply} = \frac{\text{stock_level}}{\text{sales_velocity}} ]
- Designed a Prediction Agent that assigns Low/Medium/High risk.
- Added a Reasoning Agent (LLM-based) that generates three prioritized actions per incident.
- Implemented an Orchestrator that sends alerts, creates tickets, and logs everything.
- Connected all components into a single flow: Incident → Prediction → Actions → Ticket → Logs
Challenges We Faced
- Finding a clean structure to organize multiple AI agents in the right order.
- Ensuring predictions were meaningful even with simple CSV data.
- Designing actions that were both accurate and safe to automate.
- Handling edge cases like missing values, inconsistent columns, and noisy data.
- Keeping the demo simple while showing real end-to-end automation.
What we learned
What's next for Unified AI Decision Intelligence Engine (DIE)
Built With
- base44*
- css
- keras*
- matplotlib*
- ml
- numpy*
- pandas*
- python*-*-*html
- scikit-learn*
- tensorflow*
Log in or sign up for Devpost to join the conversation.