The team designed a context-aware Lessons SME Agent that builds on an existing Lessons Library to deliver targeted, actionable insights from historic MOD Gateway Reviews. Their work shows how semantic retrieval and persona-driven prompts can surface the most relevant lessons and recommended actions for different roles and project phases.

• Updated Apr 14, 2026

The team produced a data‐driven risk heuristics analysis pipeline that combines Python analytics with large language model feedback to assess and enrich existing risk registers. Using Jupyter notebooks, they analyse risk and mitigation data, apply SME heuristics via an LLM, and output annotated spreadsheets and summary datasets designed for do...

• Updated Apr 14, 2026
• Jupyter Notebook

The team developed an automated Work Breakdown Structure generator that converts narrative project scope documents into structured, standardised schedules. Using defined activity standards and sample enterprise schedule data, the solution demonstrates how unstructured text can be transformed into consistent WBS elements with activities, duration...

• Updated Apr 14, 2026

PRISM (Planning Risk Insight and Scheduling Monitor) is a working behavioural analytics solution that exposes risky resource and forecasting practices across portfolios. Built for Challenge 5, it provides persona‐specific dashboards for planners, resource managers, project managers, and senior leaders, analysing utilisation, forecast accuracy,...

• Updated Apr 14, 2026

The team built an automated Lessons Learned Library that extracts recommendations and insights from historic MOD Gateway Review reports and turns them into a structured, searchable knowledge base. The solution combines document parsing, NLP categorisation, AI summarisation, and Power BI reporting to surface relevant lessons at project start-up a...

• Updated Apr 14, 2026
• Jupyter Notebook

The team developed a proactive behavioural analytics solution focused on surfacing risky resourcing and forecasting behaviours early. Using Python-based analysis and clear visual storytelling, they translate schedule and resource data into behavioural flags and practical recommendations that support early intervention and constructive, blame-fre...

• Updated Apr 14, 2026
• Jupyter Notebook

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,...

• Updated Apr 14, 2026
• Jupyter Notebook

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.

• Updated Apr 14, 2026
• Jupyter Notebook

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.

• Updated Apr 14, 2026
• Jupyter Notebook

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.

• Updated Apr 14, 2026

The team proposed a comprehensive behavioural analytics dashboard to expose hidden patterns in project scheduling and resourcing that undermine delivery confidence. Using defined schedule and resource integrity metrics, the solution highlights chronic under‐ and over‐utilisation, forecast inaccuracy, ignored dependencies, and reliance on gen...

• Updated Apr 14, 2026

The team developed an AI-enabled risk management solution that integrates SME heuristics with automated evaluation to improve the quality and actionability of project risk registers. The approach combines Microsoft Power Platform components with LLM-driven analysis to identify weak risks and mitigations, prioritise critical issues, and support c...

• Updated Apr 14, 2026

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,...

• Updated Apr 14, 2026
• HTML

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.

• Updated Apr 14, 2026
• Python

hack26 is a collaborative hackathon-style event focused on rapidly exploring and prototyping practical data and AI solutions against a defined set of challenges. Teams work within clear challenge boundaries to test ideas, build proof‐of‐concepts and share learning in a short, intensive format.

• Updated Apr 14, 2026

The team delivered a Power BI–based behavioural analytics solution that visualises forecast accuracy and resource utilisation to expose poor planning practices. By cleaning and transforming milestone and financial data, they created interactive dashboards that highlight generic resource use, under‐utilisation, over‐allocation, and forecast...

• Updated Apr 14, 2026