| Customer: |
Fortune 100 corporation |
| Case: |
8,000,000 documents and emails |
| Near-duplicates: |
28% |
| Value: |
Estimated saving in review costs of over $5,000,000 |
| |
| Customer: |
Fortune 100 corporation |
| Case: |
1,000,000 documents and emails |
| Near-duplicates: |
35% |
| Value: |
Corporation’s law firm was able to skip 250,000 documents.
Review cost was ~$3 per document.
Equivio saved over $700,000 in review costs. |
| |
| |
| Customer: |
One of top 5 US litigation firms |
| Case: |
450,000 documents and emails |
| Near-duplicates: |
67% |
| Value: |
Law firm reviewed pivot document and differences only.
Review time and cost savings of ~50% |
| |
| |
| Customer: |
Leading law firm |
| Case: |
350,000 OCR documents |
| Near-duplicates: |
35% |
| Value: |
Equivio’s near-duplicate sets used to de-dupe paper collection. |
| |
| |
| Customer: |
Fortune 500 corporation |
| Case: |
1,500,000 documents and emails |
| Near-duplicates: |
58% |
| Value: |
Corporation’s law firm reviewed pivot document.
In many cases, the attorneys skipped the rest of the set Where the pivot was relevant, the attorneys reviewed only the differences vis-à-vis the pivot.
Review time and cost savings of 45% |
| |
| |
| Customer: |
Major global law firm |
| Case: |
300,000 documents and emails in English and German. |
| Near-duplicates: |
40% |
| Value: |
Law firm reviewed pivot document and differences only.
review costs of $3 per document, law firm saved over $250,000 in review costs – a saving of almost one-third. |
| |
| |
| Customer: |
AML 200 law firm |
| Case: |
750,000 OCR documents |
| Near-duplicates: |
24% |
| Value: |
Equivio was used for two purposes:
• De-duping of OCR collection
• Save time in review |
| |
| |
| Customer: |
Fortune 200 corporation |
| Case: |
2,000,000 documents and emails |
| Near-duplicates: |
31% |
| Value: |
The company realized that it could not review all the documents in the available time window.
The attorneys read the pivot document in each near-duplicate set to enable a prioritized review of the entire collection. |
| |
| |
| Customer: |
Corporate customer uses a text mining application |
| Case: |
1,100,000 emails |
| Near-duplicates: |
85% |
| Value: |
Equivio reduced the input set:
• Enabling processing to complete in the given time window
• Eliminating data skews resulting from redundant data |
|
