Projects
⚙️ Flagship Projects
These projects showcase my ability to design AI/NLP systems, retrieval pipelines and intelligent web apps. Each is rooted in real-world problems, many with legal applications, but the technical approaches are generalisable across industries.
⚖️ Document AI Summariser & Risk Detector (Flagship)
This project demonstrates large-scale text ingestion, NLP pipelines, and AI-powered summarisation. It processes long-form documents (e.g. contracts, asylum letters, reports) into concise summaries, risk highlights and plain-language explanations.
Features:
- Document upload (PDF/DOCX/TXT).
- AI-generated summaries and risk detection.
- Flags missing clauses and inconsistencies.
- Generates plain-English explanations for non-experts.
Tech Stack: Python, LangChain, OpenAI API, Flask UI
📚 Semantic Search & Case Explorer
Built as a retrieval-augmented generation (RAG) pipeline, this tool enables intelligent search across case law databases (or any large text corpus). Users can input a case citation or query and receive summaries, key holdings and related precedents.
Features:
- Case citation input → automated retrieval.
- AI summaries with structured breakdown (e.g. ratio vs obiter).
- Embedding-based search for similar/related cases.
- Dashboard UI for efficient browsing.
Tech Stack: Python, RAG pipeline (embeddings + scraping), Vector Database, Dashboard UI
📝 AI Intake & Classification Assistant
This project focuses on document classification, entity extraction and urgency scoring.
Designed for legal aid triage but adaptable to any workflow that requires fast document analysis and task prioritisation.
Features:
- Upload letters or evidence → structured extraction pipeline.
- Entity recognition and issue detection.
- AI-driven urgency and priority scoring.
- Exports structured reports (PDF/email) for downstream use.
Tech Stack: Python, FastAPI, OpenAI API, Workflow-driven UI
🚀 What These Projects Show
- AI/NLP Engineering → document processing, embeddings, summarisation, classification.
- Full-Stack Delivery → from backend pipelines (FastAPI, LangChain) to frontend dashboards.
- Applied Innovation → examples come from legal contexts, but methods apply to finance, healthcare, education and beyond.