Legal intelligence, AI systems, and what the evidence supports.

Editorial track

Revenue Leakage & Intake Performance

Inquiry ownership, response, follow-up, consultation conversion and source-to-revenue evidence.

Editorial track

AI Governance, Ethics & Compliance

Professional-responsibility guidance, vendor diligence, permissions, verification, auditability and escalation.

Editorial track

AI Visibility & Reputation

AI-mediated discovery, search, reviews, citations, entity consistency and the path from visibility to intake.

9% Live, 75% Paused: Why Intake Agents Need a Real-Time Test Before Launch

A preprint reports one frontier agent passing 9.0% of minigames in real time but 75.0% when paused. Here is how to run that contrast on a firm's live intake.

Web-Security Preprint: Interface Moved Attack Retention 39% to 95%; Intake Test

A web-security preprint found interface design affected exploitability more than agent choice. At least one firm has described an MCP server for clients' agents; here is an intake test.

Could Your Intake Handoffs Drop Facts? A 30-to-50-Inquiry Trace Test

A public legal-AI integration shows one way a system can pass case facts to another. Here is a fact-level test to learn whether your own intake handoffs lose anything.

Two AI Interfaces, One Intake Question: Test for Different Answers Before Acting

MyCase now exposes intake data to Claude through an MCP connector, while its native AI runs on different models. Here is a test plan for trusting any agent's intake and leakage numbers.

What a Medical AI Benchmark Can Teach Firms About Testing Intake Agents

A medical-research preprint offers an evaluation pattern to adapt—not a legal AI performance claim. Here is a practical, source-backed test plan for law-firm intake agents.