LLM-based document analysis

Which brands AI mentions

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.

Best for: Litigation, investigations, privileged material, and defensible evidence review.

Watch for: Whether your primary goal—persistent enterprise-wide search across a terabyte-scale archive—is covered by your proposed deployment and licensing.

Official website ·e-discovery capabilities

2. Microsoft Azure AI Search

(Best customizable search architecture)

Supports full-text, vector, hybrid keyword-plus-semantic retrieval, metadata filtering, and AI-enriched indexing pipelines. Combined with Azure Document Intelligence and an LLM, it can underpin a custom legal search and question-answering system.learn.microsoft.com+2

Best for: Organizations that want a tailored legal knowledge platform, granular metadata, custom access controls, and source-grounded answers.

Watch for: You must design the ingestion, indexing, chunking, citations, permissions, and answer-generation workflow. PDF extraction and OCR are separate parts of the architecture.

Azure AI Search documentation ·Document Intelligence

3. Google Vertex AI Search

Offers semantic search and conversational answers over unstructured documents, including PDFs, with document parsing and generative summarization.docs.cloud.google.com

Best for: Cloud-first teams that want managed search and RAG rather than building every retrieval component themselves.

Watch for: Verify document count, data-size quotas, ingestion throughput, ACL handling, and whether complex legal PDF layouts are parsed accurately at your required scale.

Official documentation

4. Coveo

An enterprise search option to investigate if the main requirement is unified retrieval across multiple repositories, with AI-assisted discovery and relevant source passages. Its integration ecosystem includes enterprise-content retrieval for AI assistants.openai.com

Best for: Cross-repository search where legal PDFs are one part of a broader enterprise knowledge base.

Watch for: Confirm native legal-document workflows, OCR capabilities, corpus limits, and whether the required document-level LLM analysis is included or needs integration.

Official website

Another architecture worth considering

A custom retrieval-augmented generation (RAG) platform can be a better fit than a single packaged application when the archive is truly measured in terabytes and the documents span decades, jurisdictions, and repositories.

A typical architecture combines:

• Object storage for original PDFs and extracted text.
• Distributed OCR and layout-aware extraction for scanned or complex documents.
• A full-text index for exact citations, case numbers, statutes, and party names.
• A vector index for semantic retrieval, combined with keyword search.
• An LLM layer that answers questions from retrieved passages and cites the original page and document.
• An identity-aware authorization layer, audit logs, and document-level retention policies.

Azure AI Search documents the hybrid retrieval and vector-indexing components needed for this pattern.learn.microsoft.com+1

5. iManage

Particularly relevant if the PDFs already live in a law-firm document management system. iManage AI includes document enrichment, automated classification and extraction, and natural-language assistance, with emphasis on confidentiality and information governance.imanage.com+1

Best for: Law firms and legal departments that need AI search embedded in their existing document-management environment.

Watch for: Whether your full archive can be ingested into the system economically if it currently resides outside iManage.

Official AI platform

6. OpenText Knowledge Discovery and Aviator Search

A candidate for heterogeneous legacy archives. OpenText describes ingestion across more than 160 repository types and 2,300 content types, with entity extraction, semantic analysis, natural-language search, and governance features.www-akamai.opentext.com+1

Best for: Large, fragmented document estates with legacy repositories and many file formats.

Watch for: Validate the specific product configuration, PDF OCR pipeline, and the amount of work needed to build a legal-specific search experience.

Knowledge Discovery ·OpenText

How I would narrow the shortlist

These are initial fits, not a verified ranking of maximum capacity. For a terabyte-scale deployment, request a reference architecture and a proof of concept using your own representative PDFs.

The six questions vendors must answer

1. Scale: Can you index the entire corpus, and how many documents and pages can you process per day? Ask for production references at comparable scale.
2. OCR: Can you reliably process scanned PDFs, poor-quality scans, handwritten annotations, tables, footnotes, and multi-column legal filings?
3. Search quality: Can users combine exact legal citations, Boolean queries, filters, and semantic search?
4. Answer reliability: Does every generated answer link to the source document and exact page or passage? Can you inspect supporting evidence?
5. Security: Are document-level permissions enforced during retrieval, including for privileged, confidential, and sealed documents?
6. Total cost: What are the costs of initial OCR, extraction, embeddings, index storage, ongoing updates, and LLM queries?

One important technical point: terabytes of PDFs are not equivalent to terabytes of searchable text. Scanned documents require OCR, and indexing extracted text and embeddings adds its own storage and processing requirements. The architecture and costs should be estimated from page counts, average document size, scan percentage, and query volume.

To make this a more precise recommendation

What kind of legal archive are you working with?

1. Document collection

Litigation and e-discovery productions

Contracts and corporate legal documents

Case law, court filings, and legal research

Mixed legal archive / scanned historical records

2. Deployment constraints

Cloud is acceptable

Private cloud or dedicated tenant

On-premises only

3. Main objective

Search and ask questions across the entire corpus

Extract metadata and classify every document

Review matters and discover evidence

4. Approximate archive size

Under 1 TB

1–10 TB

10–100 TB

Over 100 TB

[Compare platforms for my use case]