Case Study: How a Law Firm Indexed 30 Years of Archives for a Private LLM
A hypothetical walkthrough: from inaccessible WordPerfect binaries to a firm-wide AI assistant that answers questions against decades of searchable case history. This law firm AI case study shows how indexing legal archives turns "dark data" into instant, citeable answers—without sending a single file to the cloud.
TL;DR
A law firm turned decades of inaccessible WordPerfect archives into a searchable, AI-powered knowledge base. The key: batch-converting WPD to Markdown locally with WPDConverter, then feeding clean text into a private RAG pipeline.
Before: 30 Years of Inaccessible Binary Files
Imagine a mid-sized firm with a shared drive full of matters dating back to the early 1990s. Thousands of memos, pleadings, and opinions—many in Corel WordPerfect (.wpd). New associates couldn't search them; even finding "that case we had on indemnification in '98" meant opening folders one by one or hoping someone remembered the filename. The knowledge was there, but indexing legal archives for search—let alone for AI—was impossible while everything stayed in binary WPD.
The Goal: Searchable Case History and a Private LLM
The firm wanted a private LLM/RAG assistant: ask a question in natural language and get answers grounded in their own precedent and work product. That meant converting WPD to a text format (Markdown or TXT), then chunking, embedding, and loading into a vector store—all on-prem or in a controlled environment. The first step was the bottleneck: they needed to batch-process millions of words without sending confidential client data to the cloud.
How They Did It: WPDConverter + Bulk Export
They pointed WPDConverter at the legacy matter folders and exported everything to Markdown (with TXT as an option for the simplest ingestion). Folder structure was preserved, so matter and year metadata stayed intact. Conversion ran locally on a single workstation; no documents left the building. Output went straight into their existing pipeline: chunk by section, embed with their chosen model, load into their vector DB. Within a few days they had a searchable case history spanning three decades—ready for RAG.
After: Instant Answers from a Firm-Wide AI Assistant
The "after" is the payoff. Attorneys and staff now query the assistant: "What did we argue in Smith v. Jones on the statute of limitations?" or "Summarize our position on arbitration clauses in vendor agreements." The system retrieves relevant chunks from the converted archives and the LLM answers with citations to internal documents. Indexing legal archives didn't just make old files openable—it made them the backbone of a private, firm-specific AI. No cloud conversion, no data leakage; just local conversion followed by their own RAG stack.
Takeaway
This law firm AI case study (hypothetical but realistic) shows the pattern: before = inaccessible binary archives; after = searchable case history powering a private LLM. The enabler is local, bulk conversion from WPD to text—so you can index decades of work product without ever uploading it.
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