u/rohasnagpal

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The case was about a healthcare clinic group and a software vendor who were mediating a commercial dispute over a failed software implementation, disputed invoices, alleged billing defects, and competing settlement demands.

I ran the same mediation prep workflow across multiple AI models. The cost difference was significant.

What it cost me for one full run:

OpenAI GPT-5.5 Pro: $30.00

OpenAI GPT-5.5: $5.00

OpenAI GPT-5.2: $2.08

Anthropic Claude Fable 5: $9.05

Anthropic Claude Sonnet 5: $1.81

What I uploaded:

Both mediation briefs, statement of claim and defense/counterclaim, MSA excerpts, SOW, email bundle, invoice bundle, prior offer letters, expert summaries, and the relevant procedural order.

What the AI generated:

• Neutral case summary and factual chronology

• Key legal, factual, commercial, and emotional issues

• Party positions vs underlying interests

• BATNA/WATNA and possible ZOPA

• Strengths, weaknesses, uncertainties, and risk allocation

• Settlement levers and information gaps

• Caucus questions, impasse points, and bridge proposals

• Private prep note and one-page session plan

The results were quite similarly good across models.

AI Law and Legal AI sound similar, but they solve two very different problems.

AI Law is about the law governing AI. It deals with questions like "Is this AI system compliant with the EU AI Act?" or "What disclosures are required when AI is used?", etc.

Legal AI is about using AI inside legal work. It deals with using AI for legal research, contract review & drafting, litigation prep, translation, etc.

Both fields will grow rapidly, but they need different skills.

AI Law needs lawyers who understand technology, regulation & risk.

Legal AI needs lawyers who understand legal workflows, prompting, document systems, retrieval & APIs.

Which side are you more keen to build expertise in: AI Law, Legal AI, or both?

I ran a first-pass review of a 72-page Stock Purchase and Merger Agreement.

Raw API cost:

DeepSeek R1 USD: $0.0511

Gemini 2.5 Pro USD: $0.1575

Claude Sonnet 4.6 USD: $0.3038

OpenAI GPT-5.5 USD: $0.5475

The outputs were all usable for a first-pass legal review.

I've built an open source Legal AI solution.

Is ProductHunt a good place to launch it?

Any other platforms?

Moritz, a Y Combinator-backed law firm/software startup, is building tools for its own lawyers to draft and review documents like NDAs, offer letters and sales contracts.

Investors include founders from Reddit, Dropbox, and HuggingFace.

If you don't have an AI strategy for your law practice, you are unprepared for what's next.

If every lawyer uses AI the same way...
...then no lawyer has an AI advantage.

That's where we're heading. And most people haven't thought through what it means.

When everyone is pasting contracts into ChatGPT or Claude and asking the same questions, the output converges.

Same summaries. Same risk flags. Same blind spots.

The differentiation is not in using AI. It is in how you run it.

A few things that actually move the needle:

1. Chunking strategy
How you split documents before retrieval determines what the AI sees when it answers. Bad chunking = missed clauses, broken context, wrong conclusions. Most lawyers have no idea this variable exists.

2. Temperature and model behaviour
A contract review needs low temperature so its precise, conservative, consistent. A brainstorming session on negotiation strategy needs high temperature so there is room to creativity.

Same task, wrong setting = garbage you can't trust.

3. Using multiple AIs for prep
Run your arbitration timeline through Claude, stress-test your positions with ChatGPT, check jurisdiction-specific angles with a specialised model. Single-model prep is single-perspective prep.

4. Matching the model to the task
Drafting, research, translation, risk scoring, cross-examination prep are very differetn. Routing them to the right model changes the quality of the output materially.

A US court just sanctioned two lawyers after briefs were filed with fake cases, wrong quotes, and real cases used for points they did not actually support.

The court’s point was simple: using AI was not the problem. Filing unchecked legal work was the problem.

This is the danger with AI hallucinations.

Sometimes AI invents a case.

That is easy to catch.

The bigger risk is when AI cites a real case, but says it supports something it does not.

That can fool a busy lawyer.

So stop thinking of AI as one chatbot.

Legal AI needs a multi-agent workflow:

• one agent researches

• one agent checks whether the cases exist

• one agent checks whether the case supports the point

• one agent challenges the argument

• the lawyer makes the final call

AI can speed up legal work.

But one unchecked AI answer can damage a case, a client, and a lawyer’s reputation.

And they're far more AI-positive than many people would expect.

The draft has a clear principle: AI should be adopted wherever it can improve access to justice, reduce delays, or improve efficiency.

But it also draws some very clear red lines.

AI may assist with:

• legal research

• precedent retrieval

• translation

• transcription

• case management

• court administration

• litigant assistance

AI may not:

• act as a judge

• determine judicial outcomes

• decide bail eligibility

• predict recidivism

• score witness credibility

• predict future behaviour of litigants or accused persons

Human judgment remains supreme.

The draft also requires:

• human oversight

• explainability

• audits

• incident reporting

• disclosure of AI-generated content filed in court

• compliance with privacy and cybersecurity requirements

Agentic means AI that can do a task through multiple steps with some independence.

In simple terms:

Normal AI use:

You ask one question. It gives one answer.

Agentic AI use:

You give it a goal. It plans the steps, uses tools, checks information, drafts output, revises it, and completes the task with less hand-holding.

Example for lawyers:

Non-agentic:

“Summarise this contract.”

Agentic:

“Review this contract for risk.”

The AI then:

1. reads the contract

2. identifies clauses

3. checks missing protections

4. compares it with a playbook

5. flags risks

6. drafts negotiation points

7. prepares a client-ready summary

In 2024, researchers compared GPT-4 with junior lawyers on contract review tasks.

The task: identify legal issues in contracts and locate where those issues appeared.

Issue-spotting accuracy: GPT-4: 87.1% Junior lawyers: 86.0%

Time taken per contract: GPT-4: less than 5 minutes Junior lawyers: 56 minutes

Cost per contract: GPT-4: $0.02–$1.24 Junior lawyers: $74

The result: GPT-4 slightly outperformed junior lawyers at spotting issues and completed the work far faster and at a fraction of the cost.

However, humans still performed better on some tasks involving precisely locating and contextualising issues within the contract.

Way back in 2018, 20 experienced American lawyers reviewed 5 NDAs against 30 predefined legal issues.

Average issue-spotting accuracy:

LawGeex AI scored 94%

Human Lawyers scored 85%

Time taken for all 5 NDAs:

LawGeex AI: 26 seconds

Human Lawyers: average 92 minutes