Litigation support
Litigation Document Review & Analysis
Produced records arrive as ten thousand pages of crooked scans. You need to know what's in them, what's missing, and be able to point to the page.
The problem
Document-heavy matters fail in two directions. Either the review costs more than the exposure — associate hours, contract reviewers, per-gigabyte hosting on a platform priced for a much larger case — or corners get cut and something material sits unread in box seven.
Automated review is the obvious answer, and it is also where the second problem starts. A model will summarise a corpus fluently whether or not it read it correctly. Bad OCR on a fax-of-a-fax silently becomes bad analysis. A missed page is invisible. Neither shows up until opposing counsel finds it.
This practice is built around closing that gap: the output comes with the evidence that the output is right.
What you get
- Chronologies and timelines built from the record, every entry citing page and document
- Issue-coded indexes across the full production, including scanned and handwritten material
- Medical record summaries with treatment timelines and provider-by-provider breakdowns
- Gap and completeness analysis — what the production implies exists but does not contain
- Entity and relationship extraction across a corpus too large to read manually
- A measured OCR accuracy report and a log of every page that could not be read reliably
- Native-format working files and the intermediate data, not just a locked PDF report
How the work runs
Representative sample
A few hundred pages establish the real condition of the record — scan quality, layout variety, handwriting, stamps, duplicates.
Calibrated extraction
Text extraction is scored against controlled ground truth before your corpus runs, so the error rate is measured rather than assumed.
Independent cross-checking
Findings are re-derived by independent passes; disagreements are escalated to a human, never silently resolved.
Cited delivery
Every assertion resolves to a page. The accuracy report and unreadable-page log ship with the work product.
Common questions
Is this e-discovery, or something else?
It sits next to e-discovery rather than replacing it. Formal ESI collection, preservation and privilege workflows belong with your e-discovery vendor or platform. This is the analysis layer — making sense of a production you already have, particularly when much of it is scanned paper that a review platform indexes poorly.
How do you handle privileged and confidential material?
Handling terms are set in the engagement agreement before any documents move, including where the material is processed, who has access, retention, and destruction at close. Work can be run entirely on isolated infrastructure when a protective order requires it.
What about handwriting and marginalia?
Handwritten notes, stamps and annotations are treated as first-class content and extracted separately from printed text, because confidence in them is genuinely lower. They are surfaced with that lower confidence marked, so a reviewer knows where to look rather than trusting a clean-looking transcript.
Can you testify to the process?
The methodology, the calibration results and the processing log are documented to support a declaration describing how the analysis was produced. Expert opinion testimony is a separate question and should be discussed at scoping.
What size matter is worth this?
Roughly: any production large enough that reading it linearly is unattractive, and small enough that standing up a full platform-and-review-team apparatus is disproportionate. That band is wide — a few thousand pages to a few hundred thousand.
Start with a sample of your corpus
Send a representative slice — a few hundred pages is plenty. You get back the processed output, an accuracy report against that slice, and a fixed price for the full job before any commitment.