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# The Batcomputer Copilot: How I Built a WayneTech Decision Engine for Emergency Logistics

**[Medium](https://daily.dev/sources/medium_js)** · 10 min read · 1 upvotes · 0 comments

## Summary

A Batman-themed portfolio project combines econometrics, machine learning forecasting, statistical testing, and an LLM copilot into a fictional 'WayneTech Tactical Decision Engine' for emergency medical supply logistics. It walks through log-log OLS regression for price elasticity (-0.491), an XGBoost vs ARIMA backtest showing XGBoost cut RMSE from 1.841 to 0.710, Welch's t-test and Cohen's d for A/B policy testing, a 1,000-run Monte Carlo stockout simulation (4.2% stockout risk), and a Streamlit dashboard wired to a hybrid OpenAI/local rule engine, later extended to Google Cloud using Vertex AI Agent Builder and a tuned Gemini model for an interactive dispatch agent.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://ai.plainenglish.io/the-batcomputer-copilot-how-i-built-a-waynetech-decision-engine-for-emergency-logistics-d19ba9a58ae2>

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---

Tags: [#data-science](https://daily.dev/tags/data-science), [#time-series-forecasting](https://daily.dev/tags/time-series-forecasting), [#xgboost](https://daily.dev/tags/xgboost)

[View this post on daily.dev](https://daily.dev/posts/the-batcomputer-copilot-how-i-built-a-waynetech-decision-engine-for-emergency-logistics-trujucpgy)

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