LayerCake's Priority Ranking Can Input Results from:
Unlock Relativity's Full
Data Mining Potential
Data Mining Potential
LayerCake™ is a patented application developed by JND that intelligently synchronizes results across Relativity's suite of analytics, amplifying your ability to locate key documents, cull junk and accelerate review.
How LayerCake™ Works
Using results from TAR, clustering, concept searching, and other queries performed in Relativity, LayerCake calculates and assigns a priority ranking to each document. LayerCake's priority ranking algorithm uncovers hidden pockets of data – the secret ingredients to your case! – and can be used to promote key documents for urgent review and suppress non-responsive documents from the set.
Each set of documents fed into the LayerCake engine comprises a "layer" and acts like a ring on a Venn Diagram. As users submit results from various queries and analytics tools, the document priority rankings adjust automatically based on the overlap between results.
Download Brochure Schedule a Consultation Each set of documents fed into the LayerCake engine comprises a "layer" and acts like a ring on a Venn Diagram. As users submit results from various queries and analytics tools, the document priority rankings adjust automatically based on the overlap between results.
“We’re used to finding 1 in 5 -10 documents responsive using a standard TAR workflow. LayerCake delivered batches where 99 out of every 100 documents were hot. We were able to stop reviewing after getting through just 10% of the documents.”
Abby LaudickeDiscovery Attorney, Schneider Wallace Cottrell Konecky Wotkyns LLP
“JND’s workflow cut our review time in a large class action case by an estimated 50,000 hours, a substantial resource and cost savings of more than $2.5M.”
Christian LevisAttorney, Lowey Dannenberg
PREDICTIVE CODING/TAR
Use predictive coding to
categorize your document set based on seed documents.
categorize your document set based on seed documents.
CLUSTERING
Run clustering to index and discover like documents based on conceptual similarities.
CONCEPT SEARCHING
Use concept searching to
identify documents with contextual and semantic similarities.
identify documents with contextual and semantic similarities.
TIMELINE ANALYSIS
Promote documents created or sent during key time frames and demote those that fall outside of the responsive range.
COMMUNICATION
ANALYSIS
ANALYSIS
Find out who’s talking to who and promote communications between key players.
SEARCH TERM
ANALYSIS
ANALYSIS
Run search term analysis to identify and promote documents containing "hot" keywords.
SAVED
SEARCHES
SEARCHES
Include results from any saved search, filter or query.
THIRD PARTY
TOOLS
TOOLS
LayerCake can input results from any third party application.
Proven Results:
3X
greater recall rate
than predictive coding
greater recall rate
than predictive coding
10X
greater data reduction
compared to only using
search terms
greater data reduction
compared to only using
search terms
1/10th
the time to setup
compared to typical
analytics-based review
the time to setup
compared to typical
analytics-based review
What was the inspiration?
In complex cases, a good analytics strategy is like a cake: it’s built in layers. Watch a conversation between Ben Sexton, Vice President of JND eDiscovery and Christian Levis of Lowey Dannenberg as they walk through the case that inspired a framework for layered analytics technologies, ultimately eliminating 50,000 hours and saving the client $2.5 million.
In an Antitrust case involving
3.5 MILLION DOCUMENTS
3.5 MILLION DOCUMENTS
LayerCake promoted 65,000 documents for immediate review, many of which proved critical to the case
Documents promoted by LayerCake were 100x more likely to be responsive in a doc per doc review
The client ultimately attributed $2.5 million in savings to the LayerCake workflow
Schedule a demo of LayerCake™ to learn more:
REQUEST DEMO 