AI-Powered Predictive Coding in Intella Connect
Reduce Review Costs & Improve Accuracy
As eDiscovery datasets grow, identifying relevant documents efficiently becomes critical. Intella Connect’s Predictive Coding leverages AI to:

• Prioritize high-relevance documents in dynamically updating review queues
• Train models using human reviewer input, validated by statistical confidence metrics
• Apply predictions across unreviewed documents, slashing manual review time
• Flag coding inconsistencies (e.g., mismatches between AI predictions and human tags) to ensure accuracy

The result: faster, consistent and defensible review, whether the case ends in court, with a regulator or in a disciplinary hearing.

Intella Assist delivers AI-powered efficiency to every stage of your investigation, whether you’re reviewing individual items or managing large-scale document sets. With Intella Assist Chat, simply ask natural-language questions like “Summarize this item” or “Detect anomalies” to gain instant insights. For broader reviews, Intella Assist Tasks can automatically tag, flag, or summarize batches of items based on your plain-language instructions. Enjoy maximum flexibility with support for OpenAI, Azure OpenAI, and locally deployed OpenAI-compatible models.

Purpose-built for analyzing emails, chats, and calls, the Communication Tab enables targeted searches, such as displaying all emails exchanged between specific individuals during a chosen timeframe. Powerful features include interactive social graph visualizations, email thread grouping, and direct tagging, making it easy to uncover communication patterns and key connections at a glance.

Easily import and export load files to and from your Intella cases. Intella and Intella Connect offer intuitive, visual tools that empower your team to create custom load file templates, streamlining your workflows and enhancing efficiency over time.