eDiscovery Document Processing
Before review comes the grind: collecting, extracting, deduplicating, and staging a million files for the reviewers.
eDiscovery document processing is the industrial preparation stage between collection and review: taking the raw harvest of electronically stored information — mail stores, file shares, chat exports, phone images, cloud drives — and converting it into a normalized, deduplicated, searchable review-ready corpus. The stage is invisible in courtroom drama and decisive in practice: processing choices determine what reviewers see, what searches can find, and whether the production survives challenge.
The pipeline handles ESI's full messiness. Container expansion unpacks mailboxes, archives, and embedded attachments to arbitrary depth, preserving family relationships (the email and its attachments reviewed together, produced together). Text and metadata extraction pulls content and the forensically significant fields (authors, dates, custodians, paths) — with OCR for the scanned and image content that pure-text pipelines silently drop, a classic gap that document AI closed. Normalization converts formats and time zones; exception handling accounts for the encrypted, corrupted, and unsupported files (each logged, because "we couldn't open it" must be a documented category, not a disappearance). Deduplication — exact and near — collapses the enormous redundancy of corporate ESI, and email threading reconstructs conversations so reviewers read threads once rather than every constituent message separately. Search-term and date filtering then cull to the review population, with the culling criteria negotiated and disclosed.
Modern document AI extends each step: classification and PII detection prioritize and protect; semantic clustering organizes the corpus by topic before human eyes arrive; and extraction-quality OCR rescues the scanned exhibits that once required manual coding. Throughout, the defensibility ledger runs — chain of custody, processing specifications, exception reports, and reproducible counts at every stage — because opposing counsel's first expert question is not about the review's conclusions but about what the processing did to the evidence before anyone reviewed it.
A review team that reads a million documents without fatigue — finding the responsive, flagging the privileged.
The same invoice, submitted three times — dedup catches the copy before it's paid twice.
Litigation is reasonably anticipated — now nothing relevant may be deleted, and the system must make that true.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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