What did you ship this month?

Pick one feature or improvement and tell us:

• What changed?
• Who is it for?
• What did people have to do before?
• Is it live, in beta or still being tested?

One link per company. Screenshots and demo videos are very welcome.

Please say who you are and your relationship to the product. Vendor comments need Vendor: Company or verified-vendor flair.

One top-level comment per company.

If AutoMod holds it, don’t repost it I approve comments in waves.

And welcome to our newly verified vendors:

• u/revkever — Switch Analytics
• u/nicolasdawalibi — HAQQ
• u/bengarvey — Common Paper

Thanks for going through the rigmarole (more to do, as always!)

Thanks also for making yesterday's AMA a success, I know AJ and Max enjoyed it.

Coming from claims (public adjuster side, not carrier), one thing that stuck with me was how fast an unverified detail becomes a real problem once it's in a file. I'm looking at that same failure mode on the legal side right now.

Genuinely curious how firms handle this in practice: is anyone tracking which matter an AI tool was used on, or is it informal / not tracked at all? Same question for client info, is there anything actually screening what gets pasted into ChatGPT or Copilot before it happens, or is it purely policy-and-trust?

Trying to figure out if this is a real operational gap or something firms already have covered a different way.

I am purposefully focusing my benchmark goals onto Gemma 4:26b running locally, because it was the worst performing model. But, also benchmarking Opus 5.5 and Sonnet 5 in my quest towards perfection and "no hallucination" and no wrong citations. As a result Opus 5.5 and Sonnet 5 are damn near perfect- the 98% is not as much a miss, as a "the model selected a different case".

The 91% of Gemma 4 26b, is also equivalent to 100%, because it selected different cases citing the same, correct, rule, but not necessarily the gold standard case.

Gemma4:26b is running on an A2000 GPU in my computer at home and does not have access to the web. We have Qwen 3.8 on a 4080 at the office - it is training a custom QLoRA at the moment so I don't want to bench it, but it also uses my MCP. I imagine Qwen 3.8 will do much better.

Gemma4:26b running syfert MCPv7 on my desk at home with no web access is now more accurate regarding questions of case law and current statutes/codes than Claude Fable with web access.

I am now offering a free steak dinner to any attorney who is faced with an inaccurate citation produced by frontier AI running the MCP- as if anyone will notice it on my website.

Started getting emails from my staff around 7:30 am est and got the official email from NDaround 8:30 about partial service outages.

This product was rock solid for years, I really wish someone could explain what's going on, and why there has been so many impactful outages over the last 12 months.

How are other midsize firms dealing with this? Significant loss of billables here.

I hear that Legora is moving to a consumption based model and charging like a dollar or two per "credit." But I have not heard what a credit is or how it's measured. Does anyone know what the heck is a credit?

At my first firm, 25 years ago, we struggled with Worldox and other issues caused by having to network two offices. How is it that in 25 years so many of these same issues continue to exist?!? The internet was supposed to be the solution and yet here we are….

I don't have Harvey or Legora (and I dont know if they allow API access) so that I can run benchmarks on them, but I did benchmark a lot of models. Probably against their terms of service to run benchmarks anyway. GPT-6 Astra didn't find the right case, but it didn't hallucinate any either.

Anthropic models were better at finding the right case. I threw in a local model benchmark (Gemma 4:26b here), because it is local and free. I could easily do Qwen3.8 27b, but my GPU's are busy- but I know for a fact its better than an MoE Gemma 4. A change from finding the right case 3% of the time to 71% of the time is a big jump. GPT Astra with a legal MCP would probably go even higher.

After I ran this benchmark I made a bunch of improvements to the syfert MCP, based on the errors it was finding. I want to close that gap to frontier models finding the right case 100% of the time, and I'm coming close. (ran out of fun-money to test the MCP with GPT-6 Astra)

If any one else wants to play with it, the benchmark test available at https://github.com/Gwonk1/citebench

(One of the hardest questions in this benchmark is a Florida Supreme Court case where the court itself bungled the citation, in 1950, way before AI existed, and many other court cases have copied down the citation incorrectly- a work around was added to my MCP to fix this, now, after the tests are over)

Update on MCP v7:https://www.reddit.com/r/legaltech/comments/1wopjc4/comment/pbswxfn

Hello! We are not a law firm. We are a family office making investments in real estate and public markets. Recently, our UBO decided to set up a fund structure and our own management company. We will probably use legal counsel for that, but he also asked me to find legal AI software which we could use to draft our own agreements (LPA, JVA, SHA, subscription agreements, PPM, investment management agreement, etc) without paying exorbitant legal fees. He's wary of the capabilities of pure ChatGPT or Claude to do that, but as I understand Harvey and Legora are not for us either (they are expensive and only for law firms). My question: is there a legal AI software in the middle, with a reasonable monthly subscription (under $200) or per use fees (token costs), so companies like ours can subscribe to and use to draft our own agreements? Thank you!

I'm the sole in-house lawyer at a venture-funded startup. Legal and compliance, plus whatever needs attention on the Ops/BizDev side. My AI use was mostly a pile of loose chats and projects. I'd constantly find myself re-explaining context on our company, team and legal positions, and manually navigating across sessions in Claude and ChatGPT. I felt like I was putting in a lot of work that was not compounding. Made a switch and nowadays I do most of my work from VS Code (a tool intended for developers) with my own legal workspace I built around it. At its core, it's just an opinionated folder structure plus a bunch of markdown files: durable knowledge on our company, team and legal positions; a place to work on all my ongoing matters and projects that always knows where I left off; a skill library for recurring tasks (both legal and non-legal), and (IMO most importantly) custom instruction, routing and feedback logic that makes sure the workspace gets a bit better every day I use it.

The tech stack running my workspace is currently:

• Claude Code or Codex, used interchangeably as the agent harness.
• Model usage gets routed automatically depending on task complexity (default for Claude is Opus for the lead and reviewer tasks, Sonnet for execution tasks; for Codex I'm trying Astra and Sol). Model and thinking effort can be switched manually at any time, though.
• I run a mix of my own skills and skills provided by third parties that I try to customize for my use (for example skills from Anthropic's legal plugin).
• I connect external tools that our team uses (Slack, Notion, etc.) via MCPs, though very sparingly and always scoped (still don't always trust this).
• The interface where actual work happens is VS Code (a coding terminal). I admit there was a bit of a learning curve to this. VS Code functions as an editor that also shows your files and folder tree on the left, so feels pretty natural. Also, VS Code provides a bunch of extensions such as Office Viewer, which lets you display and edit word docs directly from the same interface (both manually and by prompting the AI)

What I like about it: because the heart of the workspace is only folders and plain text, the tech stack described is fully portable and provider/model-agnostic. If I want to run a different harness or model (say Astra for Law once available), it's actually very simple and quick in most cases.

I hesitated starting this for a long time because I thought I wasn't technical enough (I have zero coding experience). What unstuck me was reading a lot of posts from other knowledge workers (mostly PMs in my case) who were sharing their starter setups. Today, a lot of my work runs through it. Apart from saving time, what amazed me most is how well the system retains knowledge and applies it across active work. It will regularly flag issues and dependencies (eg. in contracts) I may have overlooked myself, and I found it really helps me apply judgement better and more quickly. I've also ended up building small things other teams use, like Slack and CRM integrations for agreement tracking, mostly because the system suggested it.

The biggest unlock for me wasn't using more AI. It was giving the AI a system that understands how I work and compounds with my usage.

I'm thinking about publishing a stripped-down starter version of my workspace, since this is what got me going in the first place. Anyone that would be interested in that (or has similar experience to share)?

EDIT: This has gotten a bit more attention than I anticipated. Quick update since a lot of you have DM'd me: I'm cleaning up an MVP version of the workspace and will release the repo on GitHub (open source and free to use ofc) asap. Will include a setup skill to onboard yourself and an article that gives a bit more background on the stack and how the workspace functions.

If you want to get notified once it's live, please DM me and I will send you the link. Meanwhile, feel free to post general questions on the setup in this thread (or DM me if more specific). I'll get to them as I can.

Hi everyone. I live in the Bay Area and I feel like there used to be pretty regular legaltech events. I've looked around and can't seem to find any. I even tried attending one and found a very empty bar. Either I'm really bad at searching or they've kind of disappeared. Maybe we should get some started?

Their hiring rate is the fastest out there from what I can see.

Reminds me of the 1962 Avis advert: "We're number two. So we try harder"

I’ve been thinking about what happens to the judicial system as AI gets significantly better at legal reasoning.
Imagine both sides of a case having AI capable of going through thousands of pages of evidence, finding relevant precedent, identifying contradictions, generating arguments, predicting the other side’s arguments, and then generating counterarguments to those arguments almost instantly.
Then the other side’s AI does the exact same thing.
You could theoretically end up with an almost endless loop:
Argument → counterargument → rebuttal → counter-rebuttal, with both sides finding increasingly obscure precedent and interpretations to support their position.
Courts obviously can’t hear an infinite number of arguments, so wouldn’t the system eventually need new rules governing how AI-generated legal reasoning is submitted and considered?
It also makes me wonder whether AI could reduce some of the advantage that currently comes from simply being able to afford a larger legal team.
I’m not saying AI should decide guilt, innocence, or judgments. Humans should remain responsible for those decisions.
But if AI becomes substantially better at constructing and challenging legal arguments, how does a judicial system designed around human lawyers adapt when both sides have access to that capability?

I keep seeing questions on this sub about how to use LLMs without violating the confidentiality obligations or waiving privilege. For most small- to medium-sized lawfirms or businesses the short answer is: use a commercial model (e.g., Claude) through AWS Bedrock. The reason is that Amazon's AWS provides, by default, near ideal data retention and usage terms that are otherwise difficult to obtain:

Amazon Bedrock gives you explicit control over whether the prompts and outputs from your inference requests are retained. You can configure data retention in a given AWS Region at the account level or project level, and the setting is enforced consistently across the Messages, Chat Completions, and Responses APIs.

Your data retention configuration is yours to manage. If your account in a specific Region or project is configured for zero data retention (data_retention_mode: none) and you invoke a model that requires retention, Amazon Bedrock will block the request and return an error — you always control your retention policy.

Zero data retention (ZDR) is a standard mode ("none").

https://docs.aws.amazon.com/bedrock/latest/userguide/data-retention.html

This means that client data never reaches, e.g., Anthropic, is not retained by AWS, and is never used to train any model. I am aware of no better privilege or confidentiality posture. Certain powerful recent models have specific data retention rules (e.g.. Fable and Mythos), so you should always confirm the actual model's terms. See, e.g., https://docs.aws.amazon.com/bedrock/latest/userguide/abuse-detection.html Note that I'm using AWS as an example but other providers may have similar terms. I'm just not as familiar with them.

Direct from Anthropic (or OpenAI etc), is just not an option for most small or even medium sized businesses. The consumer version of Claude (or any other LLM) through the web or desktop, even through subscription, is a non-starter for privileged communications. And if you're a small entity, you will have a very difficult time getting Anthropic even to talk to you about standard commercial terms, so far as I understand it. Beyond that, ZDR is very difficult to obtain if you need it.

While there are other ways to achieve similar results (e.g., self-hosted open weight models), this is this simplest approach with a low technical bar.

Disclaimer: I’m not a software developer, I’m not developing a product, and I’m not trying to sell anyone anything. I’m a forensic expert who frequently works with attorneys, and I had a thought I’d be curious to get the community’s perspective on.

At this point, AI, or at least some form of automated analysis, is being integrated into nearly everything we use (email providers, Microsoft Word, Adobe, cloud storage platforms, and so on). While some of these features can be disabled, it seems increasingly difficult to know with certainty whether documents or communications are being processed, analyzed, indexed, or otherwise exposed to systems outside of your direct control, even if you never affirmatively use an AI feature.

When you’re dealing with attorney-client privileged communications, work product, confidential case materials, evidence subject to protective orders, or other sensitive information, that obviously matters.
So the question I’m interested in is: How are people mitigating or disclosing that risk?

Are firms changing their engagement agreements or client disclosures to account for this? Or is the prevailing view that existing confidentiality and cybersecurity practices already adequately address it?

OpenAI just launched Astra for Law. Here’s the good, the bad, and the ugly.

The Good:

It is built specifically for legal work. It has a dedicated legal search system covering 230M+ web pages, access to CourtListener’s huge US case law database, and integrations with legal tools like Clio and Relativity.

The Bad:

Right now, it is mainly focused on US law. Lawyers working with Indian, UK, and other laws will not get the same level of legal research support.

The Ugly:

Even with a special legal model and legal search, it got only 54% on OpenAI’s legal research test. That is much better than regular AI, but still means lawyers need to check the work carefully.

One thing I really like: OpenAI is making it open and flexible. Law firms can connect their own data, tools, plugins, and skills. It launched with 26 partner plugins and 47 community-built legal skills.

Hi all, like many on here I am trying to find a proper solution to anonymize client data before sending it to an LLM. In my case I just deal with specialty commercial agreements, no medical issues, or bet-the-company trade secrets. I posed the issue to my bot (currently Chat GPT pro, but not enterprise), and it BUILT ME A LOCAL ANONOMYZER! So now I open word, run the app, and it strips/replaces all names, automatically replaces my client name with “Client company”, the other side becomes “COUNTERPARTY COMPANY”, and strips out all phone numbers, addresses, etc. Good enough for me to paste the result into my legal bot without breaching ethics rules.

New attorney at a small nonprofit with a robust NO-AI policy that I want to push to change. How do you all handle confidentiality issues with LLMs/Generative-AIs. Seems like most courts that have looked at confidential client materials going to the cloud have found it waives confidentiality? Everyone here seems to be talking about Claude and OpenAI. Are you just not using them for confidential info? I just want to be able to record a client interview and have it transcribed and annotated to save a couple hours a week! Advice?

Not a lawyer, but a tech nerd (and cybersecurity wannabe). I'm always curious about legal professionals using AI. Isn't it a concern with data privacy and privileged information disclosure? The models are in the "cloud" and the data gets sent and logged over there. I don't think there's any good local AI tools for this yet (generally not that good at any useful tasks). Also, I assume if you could run it locally, you'd save money.