LLM-based document analysis

Which brands AI mentions

Evisort and Luminance share the lead for extracting contract data and spotting anomalies across large portfolios. Other options emerge for drafting legal documents or integrating analysis into enterprise workflows.

For your specific problem

If the core problem is:

"We already own several TB of PDFs and need to ask questions across the entire corpus, find clauses/obligations, connect amendments to originals, and get answers grounded in the source documents."

I'd put Luminance, Relativity, and Evisort at the top of the initial POC list.

Luminance is particularly interesting because its architecture is designed around the problem you're describing: it doesn't simply throw retrieved PDF chunks at an LLM. It says it pre-computes legal concepts, clauses, entities and relationships and builds a searchable knowledge layer, including relationships between amendments and related agreements.www.luminance.com+1

Relativity is particularly compelling if your corpus resembles an e-discovery/investigation environment—massive collections, heterogeneous documents, review workflows, custom models and defensible processing.www.relativity.com

Evisort is worth evaluating if your documents are primarily contracts and you want to turn the existing repository into searchable structured contract data, including scanned PDFs via OCR.www.evisort.com

One important architectural point

For terabytes, I would not evaluate these as simply "Which LLM gives the best answers?"

The more important architecture is:

PDFs → OCR/layout parsing → document/contract classification → semantic indexing → legal entity/relationship extraction → retrieval → LLM reasoning → source/page-level citations

You want the system to answer something like:

"Which of our supplier agreements permit unilateral price increases?"

and return the contracts, exact clauses, page numbers, amendments that modify them, confidence/evidence, and ideally the reasoning, rather than just an LLM-generated paragraph.

This is especially important because some vendors are effectively LLM assistants, while others are document intelligence/search systems with LLMs on top. For a multi-terabyte corpus, the latter distinction is significant.

I'd run a bake-off

Give 3–4 vendors a representative corpus containing:

• scanned and born-digital PDFs
• 100+ page agreements
• amendments and schedules
• duplicate/versioned contracts
• tables and exhibits
• poor OCR
• multiple jurisdictions/languages
• unusual contract templates

Then test cross-document questions, clause extraction, amendment linkage, citation accuracy, ingestion throughput, incremental indexing, permissions, auditability, API access, and total cost at your actual corpus size.

If you tell me roughly how many PDFs / TB, whether they're mostly contracts vs. litigation files, and where they're currently stored (SharePoint, iManage, NetDocuments, S3, file shares, etc.), I can narrow this to 3–5 vendors and give you a detailed enterprise comparison including likely architecture, strengths/weaknesses, and questions to ask each vendor.