An AI Legal Research System That Doesn't Hallucinate
Andrew Eichen
An AI-powered legal workflow for statutory analysis, designed by a practicing attorney to enforce interpretive discipline in AI tools.
AI Gets the Law Wrong in Predictable Ways
These are the most common failures identified while supervising AI-assisted legal analysis.
"It Learned from Summaries"
AI is informed by its training, and most online legal sources are general summaries, not statutory text.
Skipped Elements
Confirms definitions are met, then calls it a violation without analyzing the actual prohibition.
Invented Standards
Reads "knows" and silently applies "should have known," creating obligations from thin air.
"It's Trained to Help, Not Hedge"
AI fills gaps instead of flagging silence.
Sycophancy
When challenged on one point, abandons the entire analysis instead of defending what was correct.
Fabrication
Fills gaps in a statute's silence with common-sense reasoning that has no statutory basis.
"It Assumes Every Law Applies"
AI skips the threshold question a lawyer asks first.
Overbroad Conclusions
Assesses scope so broad it would sweep in every company in the industry.
Skipped Applicability
Determines what a statute requires without first checking whether it reaches this entity at all.
The Fix Isn't Smarter AI.
It's Encoded Discipline.
Hard rules that every agent enforces. Legal reasoning discipline that is architectural, not aspirational, derived from cataloging and correcting real errors across dozens of engagements.
From Question to Verified Analysis
Purpose-built AI workflows with built-in quality controls.
Interpretation Notes
Principle
"Knows" means actual knowledge. The statute does not say "knows or has reason to know."
Consideration
State-level trigger language varies by jurisdiction; compliance timelines are unresolved.
The requirement to activate protections for minors applies only when the operator has actual knowledge (not constructive) that a specific user is a minor, which requires the operator to know, not merely suspect. The statute is silent on whether a provider is considered to know if the user mentions their age to the chatbot.
One sentence for clear-cut conclusions. Full discussion for ambiguous applications. The depth of treatment is proportional to the analytical uncertainty.
The System Doesn't Look at What It Has.
It Looks at What the Question Requires.
Issue spotting in isolation prevents the library from biasing what the system looks for.
The Difference Discipline Makes
"The company operates an AI chatbot that interacts with users, including minors. Under the statute, the company must comply with all minor protection provisions because its service is accessible to users under 18. The company should implement age verification and obtain parental consent."
"The minor protection provisions activate only when the operator has actual knowledge that a specific user is a minor. The statute says 'knows,' not 'should know.' Without actual knowledge of a particular user's age, the obligation is not triggered."
Conditional trigger identifiedThe first analysis creates an obligation that doesn't exist. The second tells the client what the statute actually says.
Used on Real Engagements, Not Built as a Demo
Statutes tracked and analyzed across AI, privacy, and biometric law
Active AI litigation cases tracked and synthesized
Parallel research tracks covering statutes, case law, and terms simultaneously
Independent quality checks on every analysis
Connected to live legal databases across federal, state, and EU jurisdictions
Every design decision was made by a lawyer solving a problem encountered in client work.