• Better usability
• Less effort
Encoding the expert’s mind.
Zoom selects samples of documents from the collection.
A subject-matter-expert tags sample documents as relevant or not to the target category. This is an iterative, self-correcting process, where the SME is essentially “training” Zoom to evaluate a document’s relevance.
Zoom uses a patented statistical model to continually monitor training progress and quality.
When the training process has optimized, Zoom calculates the relevance score for each document.
Prior to finalizing results, verify output quality using Zoom’s built-in predictive coding QA mechanism.
Chosen by winners.
Since the beginning of 2013, Zoom has won each of a series of intensive, head-to-head evaluations for predictive coding technology. The evaluations have been conducted by law firms, government agencies and e-discovery service providers. Customers report three key criteria for choosing Zoom:
- Better results.
Zoom finds more of the relevant documents (better recall), while retrieving less of the junk (better precision). That’s due to a combination of the right classification technology, quality monitoring and statistical verification.
- Better usability.
Zoom’s decision-support environment provides all the data the user needs to make key business decisions:
- In e-discovery -- the culling cut-off to ensure proportionate review volumes
- In information governance -- the records retention cut-off to manage risk
- Less effort.
Zoom minimizes the training effort required in predictive coding. Via efficient sampling, rapid generation of samples, and a fully automated workflow.