Case study: AI Legal Risk Detection | LLM + RAG for Content Review
tl;dr: AI Legal Risk Detection: we built a proof-of-concept system that uses GPT-4 and RAG (Retrieval-Augmented Generation) to flag potential legal risks across documents—per page, per issue. The pipeline automatically reviews text for copyright, trademark, privacy, and other legal concerns, providing clear, structured justifications for every flagged item.
The problem
Reviewing long-form content for legal risks is tedious, manual, and costly—especially when multiple categories of exposure must be considered. Traditional workflows struggle to scale or catch nuanced issues like defamation or privacy rights violations spread across hundreds of pages.
Our client wanted to explore if language models + retrieval systems could help automatically analyze large documents and pinpoint potential issues on a per-page basis, categorized by legal concern.
This is where AI Flow stepped in to build a smarter, faster, and highly scalable solution.
The approach
Discovery: We worked with the client to define the key legal risk categories and the desired output format. The goal: high signal, low noise alerts, with structured justifications per issue, per page.
Design: We opted to assess each page and legal issue in isolation, ensuring that prompts remained focused and specific. For context retrieval, we chose Pinecone as the vector store, paired with GPT-4 for analysis. This architecture allowed us to keep the system modular, auditable, and adaptable.
Implementation:
- We ingested both the documents to be reviewed and a curated dataset of legal definitions and precedents into the RAG.
- Each page was analyzed once per issue, ensuring each query stayed focused and easy to interpret.
- The system retrieved: Definitions of the legal concept in question, Context from surrounding pages, Relevant law references.
- This information was fed into GPT-4 using a structured prompt format asking if the page raises a concern for that issue and why.
Testing: We iteratively refined the retrieval pipeline and prompt strategy by reviewing flagged results with the client. Adjustments to query phrasing and retrieval logic led to marked improvements in relevance, clarity, and consistency.
Delivery: We delivered a script-based prototype that can be run by technical teams on new documents. The system outputs structured results in JSON-like format, highlighting any issues, the issue type, and the supporting rationale.
The solution
Our solution consisted of an end-to-end legal review pipeline that:
- Parses and chunks any text/PDF-based document.
- Stores both legal knowledge and document content in a vector database.
- For every page, retrieves relevant definitions and context.
- Prompts GPT-4 separately for each issue type.
- Returns clear results indicating:
- Whether a risk exists
- Why it was flagged
- What part of the page caused concern
The design emphasized modularity and interpretability, ensuring that future enhancements—like UI layers or feedback loops—could be easily layered on.
The results
- A working prototype for page-by-page legal issue detection, ready for internal legal teams to review.
- Structured outputs for rapid triage and documentation of flagged content.
- A scalable baseline for integrating into larger content review workflows.
- A launchpad for future extensions like web-based UIs, active learning, or fine-tuned legal models.
If you reached this far, let’s set up a call to discuss more about AI and how it could transform your business. Click here to find out more.