Hack25 is a collaborative hackathon-style event focused on rapid experimentation, problem-solving and practical innovation across data, AI and modern digital tooling. Teams explore defined challenges, prototype solutions and share learnings within a short, delivery‑driven format.

RIO Co‑lab built the RIO Co‑lab, a multi‑agent risk‑register analysis and visualisation solution that applies specialist AI agents to identify themes, assess data quality, summarise change, and surface actionable insights across project and portfolio risk registers.

WBS Cost Estimation Tool developed a desktop‑based Work Breakdown Structure (WBS) and Cost Breakdown Structure (CBS) estimation tool that supports structured cost entry, versioned change tracking, and comparison of estimates against actuals across the project lifecycle.

ProCost built ProCost, a Power Apps and Power BI prototype for intelligent cost management that integrates actuals, forecasts and requisition data to provide scalable, modular cost visibility across the project lifecycle.

Integrated Cost Data Platform delivered an end‑to‑end cost management architecture that migrates fragmented Excel‑based cost data into a structured PostgreSQL database and exposes a single, trusted cost model to Power BI for analysis across estimation and execution.

Forecast Input Cost App delivered a Power Apps and Power BI based cost‑forecasting solution that enables controlled forecast entry, integrates actual spend data, and provides clear visibility of cost performance against estimates across projects.

TerraCast developed TerraCast, a machine‑learning based forecasting approach that combines data quality checks, classification, and regression models to predict schedule delay risk and likely lateness across energy projects, supported by dashboard‑ready outputs.

FutureFlo delivered FutureFlo, a data‑quality‑led schedule forecasting solution that combines structured data cleansing, feature analysis and Power BI visualisation to highlight drivers of project slippage and forecast future delivery risk.

Project Overrun Predictor built a machine‑learning driven schedule‑forecasting prototype that predicts the likelihood of project overruns by analysing feature trends across completed and in‑progress energy projects, supported by an interactive Streamlit application.

CutVac Risk Cleaner implemented a Python‑based risk‑register processing and feedback pipeline that cleans, validates and analyses risk and mitigation data, then generates automated, insight‑driven email prompts to drive corrective action.