本项目针对ChinaVis挑战赛挑战一的参赛思路以及实现结果进行了整理

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...

MoD Assurance Assessment Build delivered a data-driven assurance assessment build that automates evaluation of project documents against GovS 002 criteria, producing structured ratings, scores, and commentary at scale.

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.

Frontend Mentor Challenge

Coding challenge as per doc.

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...

Evidence Query Assistant demonstrated a lightweight AI-assisted evidence query approach using ChatGPT to interrogate assurance documents against defined criteria and return clear, traceable answers identifying where evidence exists or is missing.

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.

PEAT Document Assessment System developed an interactive assurance evidence assessment solution that applies large language models to analyse project documentation, score maturity, and surface assurance evidence and gaps aligned to recognised governance frameworks.

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.

PHP MySQL Login System

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.

Link Figma: https://www.figma.com/file/ALfMJuTgM3cmcmcJ8068kp/ToDo-List-(Copy)?t=W45b6YLUaV2yB7K8-1

AI PEAT Evidence Tool developed an AI-assisted PEAT-style evidence assessment tool that uses structured prompts to extract assurance evidence from project documents, apply RAG ratings, and generate auditable JSON outputs for reporting and dashboards.

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.

Team 1B applied structured prompt engineering with Microsoft Copilot to automate assurance evidence identification and scoring across multiple personas, aligned to PEAT success criteria.