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.

NDA Harm Evidence Explorer built a policy-facing web application that turns anonymised survey data and survivor testimonies into clear, judge-ready evidence of NDA-linked harm. The solution surfaces patterns across sectors, regions and reporting paths, and generates concise narratives that policymakers can reuse in consultation and briefing mate...

The team developed a scalable Lessons Library pipeline that ingests historic MOD Gateway Review documents and converts them into a large, structured lessons dataset. Their solution focuses on high‑volume extraction, semantic classification, and sentiment analysis to rapidly surface reusable lessons for assurance and organisational learning.

Assumption Drift Canvas focused on collaboratively mapping how critical assumptions emerge, drift and impact delivery confidence across projects. Using a shared visual workspace, the team structured the logic linking assumptions, confidence, external signals and portfolio‑level assurance to support earlier, clearer decision‑making.

Early Slip Predictor focused on identifying early indicators of delivery slippage by analysing capacity pressure and task behaviour across work centres. Using simple machine‑learning techniques and clear capacity metrics, the team demonstrated how likely future slip can be predicted early and translated into understandable risk signals.

SpeakOutIQ built SpeakOutIQ, a policy decision‑support platform that combines statistical analysis with an interactive dashboard and optional locally hosted AI to translate NDA misuse evidence into clear, policy‑ready insights. The solution is designed to help campaigners and legislators explore harm, reporting behaviour and sector patterns...

Evidence Engine built Evidence Engine, a web‑based Assumption Assurance platform that captures project assumptions, links them to evidence, and highlights drift through confidence scoring and visual alerts. The solution focuses on making assumptions explicit, traceable and continuously reviewed rather than static entries in documentation.

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.

Forecast Confidence Lens focused on turning unreliable supply chain spend forecasts into a confident, decision‑ready view for Programme Directors. Using the provided Rolls‑Royce datasets, the team verified key drivers of forecast fade and reframed them into a clear confidence and action narrative that leaders can stand behind when committing...

Local RAG Assurance Engine delivered a fully local, offline-capable assurance analysis engine using retrieval‑augmented generation (RAG) to identify and surface evidence from project documentation and return structured, machine‑readable outputs.

The team built a rule‑driven risk assessment system that converts SME survey responses into structured, validated heuristics. Using LLMs, fuzzy matching, and human‑in‑the‑loop review, they generate, deduplicate, and govern high‑quality risk and mitigation rules that can be applied consistently across risk registers.

The team focused on standardising the capture and reporting of lessons learned from MOD Gateway Reviews by creating a structured lessons dataset and Power BI ingestion flow. Their work demonstrates how consistent data schemas, Microsoft Forms, and Power BI automation can turn assurance outputs into a repeatable, analysable Lessons Library.

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.

Delivery Confidence Radar built an early‑warning Delivery Confidence Radar that integrates activity and capacity data to surface instability, pressure and likely slippage before dates move. The solution emphasises explainable signals and a clear ‘what’s at risk, why, and where to intervene’ structure suitable for planners and senior lead...

The team built Jim‑E, an interactive AI‑assisted risk review tool that applies SME heuristics to project risk entries. Using a lightweight Streamlit interface and encoded heuristic rules, the solution helps users identify weak risks and mitigations, capture structured feedback, and generate clear audit‑ready reports.

The team explored persona‑driven behavioural analytics to address risky resource planning practices. By combining detailed persona definitions, behavioural metrics, and deep analysis of forecasting and utilisation data, they designed a dashboard concept that highlights over‑optimistic planning, generic resource use, and weak feedback loops,...

HCD Action Console built a full Human‑Centric Data Action Console combining secure data capture, analytics APIs and role‑based dashboards to monitor team wellbeing and performance during the Hackathon. The solution supports both a portfolio view for organisers and a team‑level view for participants, with optional AI‑assisted insight gene...

Forecast Insight Canvas focused on collaboratively mapping the drivers of forecast fade and reframing them into a clear, executive‑ready decision journey. Using a shared visual workspace, the team structured how Programme Directors can move from raw supply chain data to confidence, explanation and action when committing spend.

The team focused on establishing strong data quality and analytical foundations for a Project Health and Behaviour Monitor. Using a structured synthetic dataset, they demonstrated how task-level schedule, cost, and resource attributes can be cleaned, validated, and analysed to identify volatility, critical path risk, forecasting accuracy issues,...

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.