Our office currently uses the following tools in day to day legal work:

Clio

Everlaw for E-discovery

Outlook and Teams for Communication

LexisNexis for research

Pacer

I am discussing within the firm that we should sign up for a team plan on Claude but want to present a fully built pitch to show the areas where we can use it. Just want to get use cases from the rest of this sub on what big areas they are utilizing the tools

Hey,

I've been helping a lawyer friend look at different AI note takers and there are way more options than I expected. The problem isn't getting a transcript anymore. It's finding something that actually makes it easy to go back and find what a client said a month later. Tried Bluedot on my side and liked that it records without a bot joining the meeting, creates transcripts, summaries, action items, and keeps everything searchable afterward.

What are you all using? Are AI meeting notes good enough for legal work? Do you still end up writing everything yourself afterward? Any feedback is highly appreciated.

Not a perfect analogy, but self-hosting a 700B+ parameter open-weight AI model feels a bit like building your own power plant because you need electricity.

AI is obviously more nuanced. There are valid considerations around privacy, governance, compliance, and model control that don’t exist with electricity.

But here’s what I don’t quite understand.

For decades, enterprises have trusted Microsoft and Google with their emails, documents, contracts, and other highly sensitive business data. Yet when it comes to OpenAI or Anthropic, many suddenly draw the line on privacy.

Is the concern really about data privacy, or is it more about familiarity, procurement processes, and the perception of control?

A few things that caught my attention:

- Kirkland just committed $500M to train own Ai models

- Harvey is running POCs with firms to post-train open source models on firm knowledge (again 👀 ) and partners with 5 AI labs for research

- Post trained open source models reach Opus level on fraction of the costs

- Data from specific practice areas are very underrepresented in publicly available training data

What do you think?

The talk here gets overwhelmed with the big expensive players like Harvey and Legora. I'm wondering about those of you who took the plunge into developing your own agents. What exactly are you using? n8n, copilot studio, or are you actually coding in Python and something like langgraph?

And how are you dealing with the fact that these things seem to screw up formatting in Word documents?

I'm thinking mostly redlining here, but any other use cases welcome. Mostly trying to get a feel for which agentic frameworks people like (or hate) now.

Hi everyone, wanted to see if there were any legal engineers on here that could share their experience.

From what I can tell from job descriptions, a lot of the work is presenting to lawyers on how to use the platform whether it be Harvey, Legora or otherwise.

Appreciate it is part sales and part integration, but is there actually any ownership of any part of the business or are legal engineers just there to support sales?

Was looking to potentially pivot into a role like this but also don’t want to be sucked in by the marketing for the role and not realise what the actual realities are.

Saw somewhere on Reddit a post about building a small business CRM with a meeting recording and note-taking feature baked in. This specific app relied on another SaaS to join the calls with a bot like Otter.

How many builders look into the legal side of recording people — because every major AI notetaker SaaS is watching the Otter lawsuit very closely right now, and most indie builders should pay attention also.

TLDR: Otter ai got hit with a class action (now a consolidated case in federal court in California). The plaintiffs claim two things. One, the app recorded them without their consent. Two, their conversations got used to train Otter's AI models, which they also never agreed to. The plaintiffs mostly aren't Otter customers, but the OTHER people in the meetings. The ones who never installed anything, never clicked I Agree, never saw a ToS, and not necessarily noticed the recording bot either. Claims include federal wiretap (ECPA), California's CIPA, and Illinois biometric law for the voiceprints. Still just allegations at this point, no ruling yet, but it's the first real test of whether wiretap laws apply to AI notetakers today. Fireflies caught a separate biometric suit right after, from someone who also wasn't a user.

For context, Otter is the kind of notetaker that adds a transcription bot to your Zoom call. So there's at least SOMETHING in the participant list telling people a recorder is present. An app like Granola can be completely silent. Some apps post a chat message when transcription kicks in. But it's inconsistent across platforms, and plenty of tools opt to show nothing at all to the other side. And almost all of them, Otter included, handle the legal question the same way: a line in the docs telling the account holder "make sure you have permission where required." That exact move — shifting consent responsibility onto the user — is one of the specific things the lawsuit is attacking.

Here's the part I'm certain about: most users have no clue how the law actually applies. In most US states, one-party consent means if you are a user is in the conversation, your own consent is enough. But around a dozen states (California, Washington, Illinois, Pennsylvania, Florida, Massachusetts, and more) require consent from EVERYONE on the call. Europe is a mixed bag — a couple of countries (Germany, France) make unconsented recording an actual crime, and GDPR piles a whole extra layer on top for business use. Your user can't tell where the other participants are sitting, and neither can your app, so one person dialing in from California puts the whole recording under the strict rule. Which means your user, who trusts your product, might commit a crime in some states without knowing it. 

The notion "well that's their problem" is being tested right now — courts have held the software vendor can be liable as the party doing the intercepting. Not just the user. The damages are statutory and per violation: $5k a pop under California law, $10k or $100/day under federal law. 

If you're building something similar, review the court filings and how that applies to your app. If you are using sub-processor, make sure to review their ToS and privacy policies and have a good grasp on how they handle privacy and consent. Talk to a lawyer and don’t rely on Reddit for legal advice, obviously.

If you are a user, do you care about consent and that your Zoom calls may be recording without you knowing?

Question for people building or evaluating routed/blended model systems for Legal Analysis

When you route from one model to another, or blend one model with another, do you risk giving up the distinctive strengths of the first model?

For example, if an input that would otherwise go to Opus is routed to Sonnet, Fable, GPT, etc., do you lose some or all of Opus’s advantages on the legal tasks where Opus is especially strong?

And if you blend Opus with those models, does the blend tend to preserve Opus’s strengths, or can it dilute them?

Does the framework matter? For example, is there a meaningful difference between:

• a worker/advisor setup,
• two workers with an adjudicator,
• router-first selection,
• or some other ensemble/blending architecture?

This is a genuine question for building Legal AI. I don’t have anything to promote.

​

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.

Hi everyone,

I founded a small solo business in the IP law space. I've already received interest from a few people, and several attorneys have told me they find the product genuinely useful.

I'm not a native English speaker, but I've spent the past year deeply researching IP law. While I'm not an attorney, I understand the workflow, the terminology, and the problems practitioners face. What I don't have is sales experience.

I'm looking for someone with experience selling to law firms or legal departments, ideally in legal tech or professional services. Since the business is still in its early stages, I'm currently looking for a commission-only sales agent. That said, I'm prepared to offer a very generous commission structure for the first 10 deals.

Do you think this is a good and feasible approach? Thanks!

My spouse is a solo practitioner who summarizes a lot of medical records. We are looking for an AI solution that does not share the information or use it in anyway. I know Co counsel does this, but it also has a lot of other functionality that we don’t need.

I’m frankly looking for something that’s more affordable and just does the task that is needed. Suggestions for tech and approximate cost appreciated.

We currently have 100+ users on desktops joined to a domain, though most of the infrastructure is now in Azure. We are debating moving to Laptops, but wondering what everyone else is doing?

If laptops are they domain joined or entra? How are you handling mapped drives, or are you using sharepoint, egnyte, etc.

Title says it all. If LLMs go for verticals, will moats actually hold?

I am in house counsel at a tech company and have to go back through 450 MSA's to review the tagging that we have done, and supplement any gaps.

I was trying to use Claude to do this by setting up a folder on Sharepoint and having Claude read through all of the MSA's in the folder and then populate the spreadsheet for us (with the required tags set on the column pages of the spreadsheet), but I am not having much success with the accuracy of Claude's analysis.

Has anyone used Claude or other AI tools for a similar project and seen success?

We do not have any money for vendor spend and so I can't engage an outside party to do this for us. I am hoping there is a better way than having to spend 10 weeks going through each MSA manually.

Do any law firms use AI? And if they do what do they use it for?

What challenges do you think it might solve?

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?

Hi everyone,

I've been thinking about building a legal tech product that uses AI to help founders review legal documents faster and reduce legal costs.

However, I've heard from a few people that this could be considered unauthorized practice of law and might create legal or regulatory issues.

Is that actually true? Where is the line between an AI tool that assists with legal work and one that gets into legal advice? Are there startups successfully doing this today?

I'd appreciate any insights, especially from lawyers or legal tech founders.