Checkbox Adds New AI Tools for In-House Legal Teams

May 12, 2026
Checkbox has introduced new AI features for its Legal Front Door platform, including AI Agent Actions, AI Corrections, and Intelligent Status Update, to automate intake, capture institutional knowledge, and improve cycle time reporting for legal departments.

Checkbox announced in a press release the addition of new AI capabilities to its Legal Front Door platform. The update introduces AI Agent Actions, AI Corrections, and Intelligent Status Update, designed to help in-house legal teams automate intake, capture institutional knowledge, and improve operational visibility.

AI Agent Actions allows legal teams to convert business requests from messages or emails into structured legal matters automatically. The system identifies context from the request and initiates the appropriate workflow without manual triage.

AI Corrections enables attorneys to make direct edits to AI-generated responses within the Checkbox dashboard. These corrections take effect immediately, ensuring future responses reflect the updated information without retraining or IT involvement.

The Intelligent Status Update feature automates matter status changes based on configured rules. It monitors incoming communications and context to keep cycle time data accurate, giving teams a clear view of where delays occur and improving reporting reliability.

All three tools are now available to Checkbox customers and were presented at the CLOC Global Institute in Chicago.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Legal AI Weekly or Daily AI Brief.

Also, consider following us on social media:

Free newsletter

Legal AI Weekly

The source for the Legal AI software news, analysis, emerging applications: contract review, e-discovery, research.

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more
Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.