equivio - zoom in find out
equivio - zoom in find out
• Zoom in on the relevant data
• More signal. Less noise
• Find more of the "good" documents
• Get fewer bad documents

Train Zoom. Then filter.

  • Train.
    Train Zoom to distinguish between relevant and non-relevant documents. 
  • Calculate.
    When training is complete (aka “stabilized”), calculate relevance scores for the entire collection. 
  • Decide.
    Use Zoom’s decision-support environment to construct the review set. For example, review documents with scores above 73, representing 20% of the collection, but yielding 85% of the relevant documents.

Moving beyond keywords.

In e-discovery today, the most widely-used culling technique is still keyword search. Zoom’s predictive coding achieves results that are dramatically better:

  • Better recall. 
    The TREC studies show that keyword search typically retrieves 20-30% of the relevant data. Zoom consistently retrieves 80-95% of the relevant data.
  • Better precision. 
    TREC results also show that around 4 in every 5 documents retrieved by keywords are junk. This causes wasted cost and effort in review. Equivio achieves results that are 2 to 4 times better. 

Review less. Find more.

  • Less risk. 
    Zoom helps you fulfill your discovery obligations by finding significantly more of the relevant documents.
  • Less cost. 
    By filtering out more of the non-relevant documents, Zoom generates smaller, more compact review sets. In fact, many users rely on Zoom as a replacement for first pass review.
  • Bottom line.
    Reduced review costs, and reduced risk.