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Lexbe is the Killer App for Small to Medium Sized Firms, Recorded Demo

Customer Success Stories

A single attorney was recently able to load 2 Terabytes worth of data (millions of pages), perform a privilege review across the entire document set and export a production within 2 weeks, start to finish, in our platform. This solo-practitioner leverages the speed, accuracy, and efficiency of the Lexbe platform to compete against better funded and fully staffed opposing counsel.

Another customer, a medium sized firm, recently realized the power of the Lexbe Uber Index℠ when opposing counsel, an AMLAW 100 Firm, was unable to view mountains of evidence contained in the production they sent over. Opposing counsel’s eDiscovery index was simply not able to dig into the data the way the Uber Index does, giving our client a significant advantage over an imposing adversary.

Key Takeaways from the demo:

DIY– Attorneys choose Lexbe because of the flexibility we offer in self-administering eDiscovery 100% in-house. The DIY capability frees our clients from the delays and costs many of our competitors impose on carrying out various functions.

Free Upload– While our DIY model allows you to upload your own data without added cost, we also offer to upload your data for you as part of an annual plan. By allowing our experts to handle your standard upload at no additional charge your documents are loaded correctly and quickly using our full-speed servers. Moreover, out technical team will identify any deficient productions at that juncture, early in the eDiscovery process, and assist you in requesting the necessary repairs from opposing counsel.

Uber Index– Only Lexbe offers the Uber Index. A concatenated index which includes an OCR index of uploaded files, a text-based native extraction of all text characters, meta-data and translations into a single, searchable database.

Enhanced Search Features– Lexbe has built a number of search tools into the platform to help speed review. Combine saved search, filter results and deploy specialty tools to zero in on hot docs. For example, our Profanity Search Tool was built specifically to help firms engaged in employee or harassment disputes quickly find all instances where inappropriate language was used in a document or email across your entire data set.

Time Lining– Code facts and issues as part of review, keep notes and annotate documents as you go. Our robust time lining tool allows you to reports to see, at a glance, how evidence is building to support your case.

Speed– Lexbe offers a completely scalable solution, meaning that our servers respond with greater server speed when your caseload grows.

Cost– Our price structure offers straightforward, low cost per GB rates. We provide flexible pay-per-month plans or offer discounted rates with an annual plan. You will not find hidden fees- no user fees, per case charges or download costs.

Lexbe’s Erin Derby, Published in Paralegal Today

Erin Derby, Certified eDiscovery Specialist (CEDS), and a member of the Lexbe technical services team was recently published in Paralegal Today. Her article, Finding the Needle in a Data Haystack, featured Erin’s expertise on advanced search methodology and offers techniques on culling data, constructing quality search queries, uncovering personally identifiable information (PII) and provides instruction on how to keep records of search processes.

Erin has presented 2 webinars for Lexbe Best Practices: eDiscovery Search and Best Practices to Avoid Missing Key Evidence in Large Doc Review (Uber Index).

You can read Erin’s article here.

Preservation Strategies and Data Collection from a Forensic Expert’s Point of View

Best practices on executing preservation and collection protocols with emphasis on forensically sound methods. Time is of the essence once the duty to preserve electronically stored information (ESI) arises. Failure to preserve can lead to loss of evidence, increased costs, and even sanctions. Unfortunately, lack of technical expertise and improper planning can have disastrous consequences, including spoliation claims. This webinar, led by computer forensics expert Matt Danner, will provide you with the steps you should immediately execute upon receiving a preservation request as well as best practices formulating an effective preservation and collection plan. Mr. Danner will offer insight on uncovering data blind spots as well as collecting ESI using forensically sound methods.

Key Points

  • Common Practices and Terminology
  • Evidence Preservation Triggers
  • Forensic & Other Collection Methodologies
  • Planning and Executing Collection Plans
  • Handling Phones and Devices
  • When to Stop and Call an Expert
  • Case Examples
  • Practical Takeaways

About the Speaker

Matt Danner has experience conducting digital forensic examinations and consulting for private and public clients to include corporations, law enforcement, and law firms. Mr Danner regularly provides presentations on digital evidence collection and analysis to legal organizations and law enforcement. He also frequently testifies as an expert witness related to the analysis of computers and mobile devices. His background in criminal investigations makes him a specialist in both criminal defense and prosecution cases related to digital evidence.

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Understanding your eDiscovery Index and how it finds (or misses) evidence

How your eDiscovery platform parses and organizes your electronically stored evidence can be the difference between finding or missing that smoking gun. Or worse, unwittingly handing a smoking gun to opposing counsel. Pulling back the curtain on how an eDiscovery platform ingests electronically stored documents and makes the text within documents searchable reveals hidden places where evidence may be hiding. This article explains indexing and breaks down the types of search indexes used in eDiscovery software platforms, discusses the pros and cons of each, and offers solutions to ensure that you never miss crucial evidence.

Indexing occurs during the upload of your documents to your eDiscovery review platform. A number of processes run which separates and organizes your data. The text, in particular, is extracted from your documents and filtered into a database or index. When you enter a search query your software does not review each document searching for the word; that could take hours or days. Rather your software refers to the index (just as you would in a textbook) in order to quickly pull the relevant documents for your review. The process by which the text is extracted from your documents to be placed into that index is critical to the quality of search results.

There are 2 basic indexes used in eDiscovery software platforms, an OCR Index or a Text-based (also called Native extraction) Index.

OCR stands for Optical Character Recognition. In this process, your electronically stored documents could be originally scanned or saved from a native document through a virtual print driver. Specialty OCR software recognizes alpha-numeric text patterns. For example, a Word doc uploaded would be “printed” within the software engine and the text that appears on that virtual print would be lifted off the page and indexed.

Text-based Indexing is also called Native Extraction Indexing because instead of processing the document as a printed page it rather looks at all of the underlying code and data within a document. Where OCR sees the document as a print, Text-based indexing lifts the hood and extracts all of the computer-embedded text in a file and additionally will capture the data that you do not see, such as comments.

The pros of one indexing approach are the cons of the other and vice versa. Specifically, an OCR-based index may miss hidden fields, such as hidden columns on an Excel spreadsheet, while a text-based index would not. Conversely, a Native extraction-based index will not read (index) the text on an image, including scanned or PDF’d documents, where an OCR index will.

This is an example of a native PowerPoint document. When you receive this doc as a .ppt file an OCR-based index would create a virtual print of each slide and lift any text that appears on that print for indexing. The embedded images with text, like this chart titled “Load Growth Model”, would have all text that appears on the chart indexed. Speaker notes, however, like this one regarding “November Data”, could be missed as notes do not normally show on a print, by default.

Conversely, a native extraction-based index would only recognize the .jpg title of the image of the chart and index that file name as text. It cannot “read” an image (as OCR can) and so none of the text appearing on the chart would be indexed. It would, however, pick up the speaker notes regarding November Data. When you search for the company name “CAISO” an OCR-based Index would retrieve this document but a Native Extraction-based index would not. When you search for “November Data” the Native Index would retrieve this document, but an OCR index would miss it. If you were to perform a Boolean search for “CAISO AND November Data” neither index alone would return this document as responsive as it would only see one term or the other.

Some modern eDiscovery software providers will offer both indexes, however, they are siloed and so you would have to run your entire search twice, once through each index. This not only doubles your search time but still leaves you vulnerable to miss evidence when you are using Boolean searches to narrow results. Some eDiscovery vendors will instruct you to write additional language into your ESI order in an attempt to mitigate the loss of potential evidence. Unfortunately, the more complex an ESI request the more likely that mistakes will be made and evidence missed.

Lexbe has solved this false ‘index dilemma’ by creating the first concatenated eDiscovery search index, our Uber-Index℠. At ingestion, documents are run through both OCR and Native extraction indexing simultaneously. Then the OCR and Native-Extracted indices are compiled into one single, searchable database. All text is captured by these two complementary processes, and all evidence is searchable.

Additionally, Lexbe offers an integrated translation feature which is also included in our Uber Index for seamless search in either language. Whether you opt for Lexbe to perform your document translation or upload your own translated docs, our software will tie the original doc to the English translated one for integrated search and document review.

Finally, Lexbe also performs an advanced metadata extraction at ingestion for precision searches. Details such as the author of a document are extracted and will be searchable.

Features OCR Index Text-Based Index Lexbe Uber Index
Embedded Text
Charts
Budgets
Scanned Docs
Hidden Cells/Sheets
Comments
Tracked Changes
BCC Field
Meta-Data Extraction
Translated Text

With the Lexbe eDiscovery platform, your search is faster and more complete than with any other index on the market. For more information on how indexing works watch our webinar Best Practices to Avoid Missing Evidence in Large Document Reviews, part of the Lexbe eDiscovery Webinar Series.

Exploring FRCP Rule 37(e) and Avoiding Spoliation Sanctions

In a recent eDiscovery webinar, Avoiding Spoliation Sanctions in 2017 Under New FRCP Amendments, the Honorable Xavier Rodriguez spoke with Lexbe CEO, Gene Albert regarding the intricacies of Rule 37(e). Judge Rodriguez offered insight into how courts are interpreting Rule 37(e), and how the amendment has changed the landscape for attorneys with regard to data loss and sanctions.

Prior to the FRCP’s 2015 amendments to Rule 37(e) courts were inconsistent in how they imposed sanctions due to lost data. As a result, many attorneys, fearing consequences, opted for a “save it all” approach when a preservation letter landed in their laps. With the growth of data and the high cost of storage, the “save it all” approach became prohibitively expensive.

Given the expense of managing preservation combined with the inconsistent application of sanctions, the FRCP’s 2015 amendments to Rule 37(e) aimed to address the “excessive efforts and money” spent on preservation and also provide a framework in which to evaluate actual damages resulting from lost data.

In the webinar discussion, Judge Rodriguez explained that the FRCP advisory committee suggests that courts consider “proportionality” across the entire spectrum of Rule 37(e). The committee notes for the rule suggest that courts look at the parties’ technical sophistication, their resources, and the weight of the ESI to the claim or defense when considering the appropriate and proportionate remedy.

With proportionality in mind, Judge Rodriguez suggests that a court needs to ask three questions before determining whether there is cause for prejudice (see infographic).

In making this determination, a court will seek to determine how relevant the data loss is to the case and a proportional remedy to the party experiencing prejudice. Remedies could include requiring additional depositions at the spoliating party’s expense or the preclusion of evidence (preventing the spoliating party from entering evidence).

In the webinar, Judge Rodriguez emphasized that intent to deprive must be deliberate. Negligence, even gross negligence, does not necessarily meet the strict requirement of actual intent. If, however, intent is found, the court has three options from which to choose: (A) “presume that the lost information was unfavorable to the party;” (B) “instruct the jury that it may or must presume the information was unfavorable to the party;” or (C) “dismiss the action or enter a default judgment.”

For more information, watch the recorded webinar on-demand: Avoiding Spoliation Sanctions in 2017 Under New FRCP Amendments.

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