Litigation generates documents. Discovery produces them by the tens of thousands. Medical records arrive in fragmented batches from dozens of providers. Construction cases come with years of daily logs, RFIs, and change orders. Financial disputes involve transaction records spanning entire corporate histories. The documents contain the facts that win or lose the case — but extracting those facts at volume is a problem that manual review handles poorly and expensively.
Contract attorneys reviewing at 50–80 documents per hour cost real money and deliver inconsistent results. The first reviewer’s judgment drifts from the last reviewer’s. Relevant documents get missed. Patterns that span thousands of pages — the kind that reveal a cover-up, a course of dealing, or a systematic failure — are invisible to any single reviewer because no single reviewer sees enough of the set to recognize the pattern.
Automated document analysis applies consistent, case-specific logic across the entire document set. Every page is evaluated against the same criteria, at the same standard, without fatigue or drift. The system does not replace attorney judgment — it extends attorney judgment to a scale that human review cannot reach.
Document classification and coding — categorizing documents by type, relevance, subject matter, custodian, date range, or any custom taxonomy defined by the legal team. The system applies your coding scheme across the full set, flagging documents that require attorney review and sorting the rest.
Fact extraction — pulling specific data points from unstructured documents: dates, amounts, named parties, medical diagnoses, policy numbers, product identifiers, regulatory citations. The extracted data is structured into searchable, sortable outputs that feed directly into case chronologies, damages analyses, and trial preparation.
Pattern identification — detecting recurring language, communication sequences, behavioral patterns, and anomalies across large document sets. In insurance bad faith cases, this means identifying every instance where the adjuster used delay language. In employment cases, it means mapping communication patterns that reveal decision-making authority. In fraud cases, it means tracing terminology and transaction structures across entities and time periods.
Cross-referencing and gap analysis — comparing document sets against each other or against a known standard. Medical records against billing records. Inspection reports against code requirements. Contractual obligations against performance documentation. The system identifies discrepancies, missing records, and inconsistencies that manual review would take weeks to surface.
Summary and synthesis — generating structured summaries of individual documents or document groups, organized by the categories and priorities that matter to your case. Not generic abstracts — case-specific analysis that answers the questions you are actually asking of the documents.
The system is not a generic AI tool. Each engagement begins with a scoping conversation where the legal team defines the review criteria: what are you looking for, what categories matter, what constitutes relevance for this case, what outputs do you need. Those criteria are encoded into the processing logic before any documents are analyzed.
Documents are ingested in any common format — PDF, TIFF, Word, email archives, spreadsheets, scanned images with OCR. The system parses each document, extracts text and metadata, and applies the case-specific analysis framework across the full set. Results are delivered as structured data: coded spreadsheets, extracted fact databases, flagged document lists, pattern reports, and narrative summaries.
The system signals uncertainty explicitly. When a document is ambiguous, when the classification is not confident, when the extracted data cannot be verified against the source — the system flags it for attorney review rather than guessing. This is a design principle, not a feature. The value proposition of automated analysis collapses the moment the outputs cannot be trusted, and we build accordingly.
Medical record review — processing thousands of pages of clinical notes, operative reports, lab results, imaging studies, and billing records across multiple providers. Extracting treatment timelines, identifying gaps in care, flagging records that reference the injury or condition at issue, and structuring the results for expert review and trial preparation.
Insurance claims file analysis — reviewing the complete claims handling file for coverage disputes and bad faith cases. Identifying every communication, every reserve change, every internal evaluation, and mapping the decision-making chronology against the policy language and regulatory requirements.
Construction document sets — processing daily logs, inspection reports, RFIs, submittals, change orders, and correspondence to build a complete project chronology. Identifying code violations, scope changes, delay events, and notice failures across document sets that routinely exceed 100,000 pages.
Financial and transactional records — analyzing bank statements, invoices, contracts, and communications to trace fund flows, identify unauthorized transactions, reconstruct financial histories, and support damages calculations in commercial disputes.
Employment litigation — reviewing HR files, email archives, performance records, and policy documents to establish patterns of conduct, map decision-making chains, and identify documents relevant to discrimination, retaliation, or wrongful termination claims.
Automated document analysis is not a standalone service. The structured data it produces feeds directly into CTM’s visual production pipeline. The treatment timeline extracted from 4,000 pages of medical records becomes the source data for a trial chronology graphic. The pattern identified across 800 claims adjuster communications becomes the foundation for a bad faith timeline. The financial flows traced through 50,000 transaction records become the inputs for a damages visualization.
This integration — from raw documents to structured data to trial-ready visuals — is what distinguishes this service from general-purpose document review tools. The analysis is designed from the start to produce outputs that serve a visual strategy, not just a document database.