Case study

Automated document review

We helped a small legal team pull specific facts out of thousands of pages of scanned public records, where a single file runs 900 pages, replacing a page by page read with one pass that returns only what they asked for.

Document Review SystemsAI Extraction SystemsAI Enablement Systems
Client
Small legal team (name withheld)
Litigation practice working from public government records
900+
Pages read end to end in a single pass
0
Pages opened by hand
1
Plain-language brief, the only input the team writes
0
People added to handle the volume
Outcomes

What the system does on every set of documents.

Outcome / 01

We put whole document sets, thousands of pages at a time, within reach of a single question, and return only the pages that answer it.

Outcome / 02

We read scanned files of 900 pages and up end to end, with zero pages opened by hand.

Outcome / 03

We work directly on documents that are images rather than text, so zero pages are transcribed or pre-processed first.

Outcome / 04

We reduced the team's input to one thing: a plain description of what they are looking for.

Outcome / 05

We trained the team to run their own reviews, so pointing the system at a new set of documents takes zero involvement from us.

Outcome / 06

We added document review capacity to a small team without adding a person to it.

Challenge

The evidence is public, and it is thousands of scanned pages deep.

The team's evidence comes from government records sites, which publish case files as long lists of attachment links, and the attachments are scans rather than text. Files run to hundreds of pages each, and new ones keep arriving. Finding the three pages that matter means opening every link and reading every page, and that time comes out of billable work.

Solution

One collector, one reader, one hit list the team can verify.

Build / 01

We built a collector that pulls every attachment off a records page as an ordered image set, so each file arrives complete and in page order.

Build / 02

We built the review workflow inside Claude Cowork, working through the pages in batches, so the team runs it over a folder of documents instead of a codebase and no page gets skipped.

Build / 03

We made the search criteria a plain-language brief the team writes themselves, so a new question against documents they already loaded is rewriting a sentence, not a new build.

Build / 04

We designed the output as a hit list of specific pages, each pointing back at its source image, so a lawyer verifies a citation instead of hunting for one.

Build / 05

We trained the team on the workflow itself, so they point it at new document sets and cut the same manual hours out of work we were never involved in.

Build / 06

We scoped the system to retrieval only: it finds and cites the pages, the team reads them and decides what they prove.

Ready when you are

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