No AI. No Guesswork.
Deterministic automation you can audit.

The Problem with AI in Legal Workflows

AI is everywhere in legal tech right now. Large language models draft contracts, chatbots handle client intake, and machine learning classifies documents. The marketing is compelling. The risks are real.

For intake and case management — where accuracy is non-negotiable and ethical obligations are absolute — AI introduces problems that no amount of prompt engineering can solve.

Why AI Is Wrong for Legal Intake

Hallucinations Are Malpractice Risks

AI models generate plausible-sounding output that is sometimes wrong. In contract drafting, that might be embarrassing. In conflict detection, it could be catastrophic. A missed conflict creates an ethical violation. A fabricated conflict wastes attorney time and could cost the firm a client. Neither is acceptable.

Black Box Decisions Can't Be Audited

When a state bar asks why your firm took a case despite a potential conflict, "the AI said it was fine" is not a defensible answer. AI models cannot show their reasoning. They produce outputs, not audit trails. Disciplinary boards, courts, and insurers expect deterministic, documented decision processes.

Your Data Leaves Your Control

Most AI-based legal tools send your client data to third-party APIs for processing — OpenAI, Anthropic, Google's Gemini API, or proprietary models. That means privileged client information, adverse party names, case details, and contact data leave your firm's infrastructure and pass through systems you don't control, can't audit, and may not even know about.

Non-Deterministic Output

Ask an AI the same question twice and you may get different answers. Run the same conflict check on the same data tomorrow and the result could change. Legal workflows demand consistency. The same inputs must produce the same outputs, every time, for every attorney.

Training on Your Data

Many AI vendors use customer data to improve their models. Even when they claim not to, the terms of service often contain carve-outs for "service improvement" or "safety." Your client's privileged information should never become training data for someone else's AI.

What LitiGator Does Instead

LitiGator is deterministic automation — not artificial intelligence. Every action it takes follows explicit, auditable rules written in code. There is no model, no training, no inference, and no guesswork.

Capability	AI-Based Tools	LitiGator
Conflict detection	Probabilistic matching	5-layer conflict rules: identity, household, adverse entity, past witness, and recognized witness
Decision reasoning	Opaque neural network	Auditable code path with full log trail
Same input, same output?	No — stochastic	Yes — always deterministic
Data processing	Third-party AI API	Your Google Workspace — all case data stays in your account
Explainability	"The model predicted..."	"Row 47, column C matched row 12, column C"
Training on your data?	Usually yes	Impossible — no model exists
Regulatory compliance	Unclear, evolving	Standard data processing — no AI regulation applies

What "Deterministic" Actually Means

Every action LitiGator performs is a defined rule:

• New submission arrives → create a row in your spreadsheet.
• Client name matches existing record → flag as conflict.
• New submission arrives → case folder created in Drive, PDF generated, attorneys notified.
• Attorney replies "Accept" → case status moved to Accepted.
• 48 hours with no response (default, configurable per firm) → send a reminder.
• Attorney has a conflict → exclude from the case notification, apply the dashboard filter, log the reason.

No probability. No confidence scores. No "it depends on the model's mood." If-then logic, executed the same way every time.

What LitiGator Will Never Do

• Send your client data or intake information to any AI model or third-party data processor
• Use machine learning to "predict" case outcomes or client fit
• Generate or draft legal documents with AI
• Train any model on your firm's data
• Make decisions that can't be fully explained in an audit
• Produce different results from the same inputs

The Regulatory Landscape Is Moving Against AI

State bars across the country are issuing guidance on AI use in legal practice. Courts are requiring AI disclosure statements. The ABA has published formal opinions on attorney obligations when using AI tools. Insurance carriers are adding AI exclusions to malpractice policies.

None of this applies to LitiGator. Because LitiGator is not AI, it falls outside current AI regulations. There is no disclosure obligation. There is no additional supervisory requirement. There is no insurance risk.