AI E-Discovery Document Review
Document review is where litigation budgets go to die. PDF.LEGAL builds a line-level index of every page you upload, so the question stops being "who has time to read this?" and becomes "what do we need to know?"
Why traditional e-discovery review is slow
Linear review scales with page count, not with case value. A mid-sized production runs to tens of thousands of pages, and the passages that decide the case are usually a few dozen lines buried inside it. Keyword search in a generic PDF viewer returns hits without context, misses synonyms and paraphrase entirely, and gives reviewers no way to ask a question that spans several documents at once.
How PDF.LEGAL indexes a production set
Every document is parsed page by page and line by line on upload, producing an addressable index in the form [doc][page][line]. That index is what both the full-text search and the AI retrieval layer read from, which is why every answer can point back to a specific line rather than a vague "somewhere in Exhibit C". Nothing is summarised away at ingest — the underlying text stays intact and verifiable.
Ask questions instead of running keyword permutations
Retrieval-augmented generation lets you ask "which witnesses discussed the March invoice?" or "where does the contract address termination for convenience?" and get grounded answers assembled from the actual documents, each carrying its page and line reference. Because the model is answering from retrieved passages rather than from memory, you can check every claim against the source in a single click.
Keeping privileged material inside your perimeter
Discovery material is exactly the category of data most firms are not willing to send to a third-party API. PDF.LEGAL runs against a local model through Ollama as a first-class option, so the entire pipeline — parsing, indexing, retrieval and generation — executes on hardware you control. There is no data egress to review with your client, because there is none.
What you get
- Line-level index built automatically on upload — no manual coding pass
- Sub-second full-text search across every document in the case
- Plain-English questions answered with page and line citations
- Cross-document reference detection across the whole production
- On-premise / air-gapped deployment for privileged material
Frequently asked questions
Can PDF.LEGAL search across multiple discovery documents at once?
Yes. Search and AI analysis both run across every indexed document in the workspace, so a single query covers the whole production rather than one file at a time.
Does the AI make up citations?
Answers are generated from passages retrieved out of your own indexed documents, and each one carries the page and line it came from. That makes every citation checkable against the source text — verification is a click, not an act of faith.
Can we run e-discovery review without sending data to the cloud?
Yes. Selecting the local Ollama provider keeps parsing, indexing, retrieval and generation entirely inside your own network, with no outbound calls to an AI vendor.
Related use cases
Further reading on document intelligence and e-discovery is published on the PDF.LEGAL blog, and the security page documents how on-premise processing works.
See it on your own documents
Book a walkthrough of how PDF.LEGAL handles e-discovery for a matter like yours. Call (970) 423-2818, email we@lawnova.pro, or request a demo below.