Introducing E-Discovered AI

It’s no doubt that task based and Agentic AI is here to stay in all facets of business and litigation technology is no exception.  The power of the latest models is ever growing and reaching into E-Discovery.  The reality is though, not all products are designed with specific types of review in mind.  Plaintiff or receiving side document review contains an ever moving target of nuance and the best designed AI products aren't flexible enough to handle the review. Generic Q&A document prompting against large collections isn’t cost effective yet and gets users lost in mountains of citations to verify, throw out and re run every time. This costs time and trust in the product and workflow, and all process and work product is removed from the document collection and not retained for future needs. 

Introducing E-Discovered AI.  A process, not a product.  Using private, offline and non-learning models, we extract real, usable insights from rolling productions your team can actually use for the life of your project.  Upon ingestion into your review software, inferences are done at document level allowing you to ask questions of your incoming data. Just like your favorite chatbot summarizes and speeds up your work, we can now ask questions of incoming data and save extracted answers in your review tool for faceted search, text search, tagging and many other uses.  Prompts and tools used to infer new data points are NEVER saved, trained on, and production data remains offline during this step.

Incoming ESI is plagued still by errors and omissions from the producing party.  You’ve likely dealt with incorrect custodian information across a production dataset, missing authors, dates or other key information or metadata in your incoming datasets.  E-Discovered AI can infer and discover these datapoints and much more. E-Discovered AI can:

-          Capture missing Metadata usually gathered in the course of ESI collection and production (Dates, Authors, Key Parties or Custodians)

-          Identify relationships to RFPs of inbound ESI and connect document sets with their likely relationship to your requests for production. 

-          Tag documents with likely relevance to issues

-          Enhance document type or categorization.  Not just “Word Document” “Presentation”, etc, but Contracts, proposals, clinical studies, technical drawings or any other category

-          Link documents to your expected timeline.  This helps identify time lapses in production and potential gaps.

All data points extracted in the E-Discovered AI process are retained in fielded format, for use with any E-Discovery review database and their unique visualization and categorization tools.  These new inferences can be a super power for visibility into rolling productions constantly plaguing the plaintiff’s bar and receiving side of large data productions. 

Contact the plaintiff specialists at E-Discovered Consulting for a demo of E-Discovered AI today.