BETTER RESULTS THROUGH BETTER AI
With today’s exponential increase in data volumes. You need AI-enabled eDiscovery that stays ahead of big data.
The Lineal suite of AI-enabled applications for various industries leverage the power of Lineal’s AI platform. They are turnkey, easy to use, and fast to implement. No AI expertise required.
Artificial Intelligence Models (AIM)
Utilising Lineal’s next-generation AI platform, the technology develops a conceptual understanding of data that continuously learns context and sentiment, allowing for deep cognitive analysis. The response is reported back to the fact finders in the form of “story engines.”
These models are commonly being used for litigation, investigations and compliance purposes; however new applications are being realised daily.
CURRENT USES INCLUDE:
Corporations – Corporate litigation and compliance teams are building tailored libraries of models that iteratively learn, constantly developing a greater understanding of the company’s data. This ever-expanding knowledge is then repurposed, allowing the fact-finders to quickly identify the most crucial documents/information upfront, minimising the necessity for expensive document review, and thus dramatically mitigating costs.
Law Firms – Case teams and practice groups are developing model libraries across discipline that continuously learn from the teams’ analysis processes to expedite search/retrieval across matters-repurposing knowledge. Further, teams are marketing their libraries as a competitive advantage over other firms and creating new solutions for audit and compliance that are proactive, as opposed to reactive.
SAVE YOUR TIME – SAVE YOUR MONEY
Whether you’re a litigator, transactional attorney, in-house, or legal ops, AI can enable you to make better, quicker decisions and help generate more value for your clients or organisation.
AIM by Lineal
Taking data from a representative selection of past matters, AIM will begin to contextually learn from your data, seeking behavioural patterns, nuisances, and sentiments.
02. Build AI Models
Lineal builds a custom AI engine model from what is gathered/learned from your data.
03. Apply Models to Data
The AI engine is applied to your data, detecting language patterns, communication analysis, heat-mapping, sentiment, etc.
04. Confirm Success
Lineal’s team will confirm what has been learned with what has been gathered in the application.
05. Story Engine Reporting
Reports are exported showing communication analysis, responsive documents, emotional sentiment, heat mapping, and core document identification.
Modifications to the model will be applied based upon reporting criteria. The process repeats, with learning being codified for future utilisation, and potentially shared across all other models in a library.
Early Case AI Application (ECAi)
Our mission is to deliver advanced technology to our clients and make them very simple and practical to use, so our clients can simply see whether the documents should be reviewed or not.
The Lineal Early Case AI Application enables you to quickly identify potentially responsive data prior to collection by exposing contextual relationships between custodians and content. Through the use of Lineal’s artificial intelligence models take advantage of advanced algorithms pre-defined filters and best practice workflows.
Bringing Cutting-Edge AI to Threading
Reducing the Complexity of Document Review
“Our custom developed LTAi application has reduced the overall document count required for review. Our clients have been getting over 100% suppression on communication threads compared to traditional threading algorithms. This is another example of how Lineal delivers cutting-edge AI tech in easy to use way and implements solutions.” – Damon Goduto – Partner at Lineal
Lineal Threading AI Application (LTAi)
The Lineal Threading AI Application is custom-built at Lineal to improve the productivity and workflow around threading.
LTAi provides a simple field in Relativity to designate the documents that should be reviewed. It also offers three different variations of grouping, from less conservative to more conservative, allowing the review team to alter threading strategies depending on time and cost pressures.
Moreover, it identifies important situations that occur after an incremental load, and it flags thread groups that have new members, so tagging can be propagated or the groups can be re-reviewed, carrying the analysis across incremental loads.
- Ensure increased review speed
- Reduce the documents needed for review
- Increase efficiency in delivering consistent documents for production and review
Lineal AI applications can save considerable time throughout the review process. Typically clients will apply numerous iterations of filtering to reduce the scope of data for review, AIM can be applied upfront and allows legal teams quick access to relevant documents and key insights.
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