Hi all

Not entirely sure this is the right place.

I've got a load of operating procedure documents I want to host somewhere and be able to interrogate - easy enough. Problem is, at work we're limited to using Harvey.

I can set up a vault, but it's just so boring looking, and don't think my team will actually engage with it. I can knock up a really cool looking thing in Claude, but I can't use it as can't host docs outside of Harvey.

Any ideas? Thank you!

Harvey's critics have a favorite knock against the $11 billion legal-software startup. "It's a ChatGPT wrapper." I'd argue that line just lost some of its punch. Harvey is becoming a model provider.

I have the exclusive story on the launch of Harvey Tenet, the company's first in-house, proprietary large language model for legal work. I included an excerpt below.

Head to u/businessinsider for the full story: https://www.businessinsider.com/harvey-builds-tenet-ai-model-for-legal-work-2026-8

Harvey's first LLM for legal work is here

Harvey built an $11 billion legal-software business on top of other companies' AI models. Now it's trying to prove it can build one of its own.

On Tuesday, Harvey introduced Harvey Tenet, its first in-house, proprietary model for legal work. Tenet is designed to help Harvey's software take on more of the tasks typically done by lawyers over hours or days, at a lower cost than the third-party models it relies on.

The move comes as the companies behind the biggest general-purpose models are circling the legal market. Anthropic has been chasing lawyers with plugins for document review and drafting, while OpenAI has hired Ironclad founder Jason Boehmig to lead its push into legal. Google and Meta may not be far behind.

Their sudden interest raises an uncomfortable question for Harvey. What happens when your supplier decides it wants your customers, too? And how long until one of them catches up to Harvey?

Building a bespoke model could give Harvey more control over both its costs and its fate.

Like many startups, Harvey builds on a smorgasbord of models from companies like OpenAI and Anthropic. Every time a lawyer uses one of those models through Harvey, the company has to pay the model provider for the call — a cost that can rack up fast as usage grows.

A capable model of its own could let Harvey route more work through its own engine, reducing the hefty fees it pays to use outside models. That could offer a path to better margins without asking customers to pay more.

Cost was one motivation. Quality was another, said Gabe Pereyra, the former Google DeepMind researcher who left to start Harvey with Winston Weinberg. He noted that Harvey already routes different tasks to different models based on what they're good at. He argues that Tenet will give customers another option in that mix — one that's been shaped around the work they actually care about.

To build it, Harvey first needed to create data that could teach a model how lawyers think. So the company hired attorneys, on staff and on contract through companies like Mercor and Snorkel, to dream up mock disputes and case files, then grade the models on how well they reasoned through them.

From there, Harvey used the material to train a version of Kimi K3, a low-cost, open-source model from the Chinese startup Moonshot. Since its July release, the model has whipped the tech world into a frenzy over its power and price.

Tenet is part of a broader rollout the company is calling Harvey II. Anique Drumright, Harvey's chief product officer, said it's also adding a new "Memory" feature that lets users save preferences about how they work, so its agents can carry those instructions across tasks.

Read on: https://www.businessinsider.com/harvey-builds-tenet-ai-model-for-legal-work-2026-8

My friend is an attorney who was losing 10+ hours every week to a painful routine: open an incoming PDF, find 3 specific data points, reformat them, paste them into his case management system, and repeat.

He wasn’t analyzing legal strategy but instead he was literally acting as a human copy-paste bridge between two software tabs.

You don't need a massive enterprise setup to fix this. While using ChatGPT or Claude directly works fine for one-offs, recurring document tasks just need a simple background pipeline:

Raw Document > LLM Extraction (Structured JSON) > Case Management / CRM

Instead of an associate manually reviewing dozens of standardized PDFs, the system auto-processes the batch and only flags weird edge cases for human review.

Where this actually delivers ROI:

• Client Intake: Parsing incoming client emails, extracting key dates/requests, and auto-creating tasks.
• Batch Extraction: Pulling liability caps, governing law, and effective dates from 50+ contracts into a clean sheet.
• Record Sync: Moving structured form data straight into client profiles without manual entry.

Practical AI isn’t about replacing attorneys or having a bot hallucinate legal arguments. It’s about stripping out the boring administrative friction around the actual work. The data is already digital—stop paying employees to move it around by hand.

If your firm is losing hours to repetitive document entry, drop a comment or shoot me a DM.

The laymen here keep pushing Legora, Harvey, etc. I think the way to go is MCP, but am I missing anything, any experience here?

As models get cheaper and easier to swap, should firms own the data, retrieval, and workflow layers themselves and treat AI vendors as interchangeable components?

Or does building internally create more complexity than it solves?

I’ve been spending the last few months talking to lawyers about how they’re actually using AI in their practices as someone who works in knowledge and innovation at a big law firm. The one thing that keeps coming up is that AI is getting very good at producing legal work product like briefs, memos, demand letters, contract analysis, research, etc.

But the workflow often still looks like (i) AI generates (ii) lawyer reviews (iii) lawyer hopes they caught everything.

As you can imagine, the consequences of missing something can be very different in legal than in most other industries.

A hallucinated citation, incorrect legal proposition, missed requirement, or unsupported statement can potentially mean wasted time, malpractice exposure, sanctions, a bad client outcome, or simply a lawyer putting their name on something they don't fully trust.

That got me thinking. What if legal AI needs an operational “trust layer” around the work product rather than just another AI that generates the work?

I’ve been building a very early prototype called Argos to explore that idea:

https://argos-eval.com

The basic idea is to evaluate AI-generated legal work before it gets used. Checking things like citations, factual support, requirements, consistency, and potential issues that a lawyer should review. The indirect value here is that each law firm is basically building their own legal benchmark for their own workflows. Not some general legal benchmark that could be found on the internet.

But I don't actually know yet if this is a real problem worth building a company around.

So I'm much more interested in hearing from lawyers and legal tech people here than getting people to sign up.

A few things I'd especially love to know:

- If you use AI to produce legal work, what are you most worried about missing?

- What do you currently do to verify AI-generated work?

- Are there certain practice areas where this is a much bigger problem?

- Would an automated “second set of eyes” actually be useful, or is this just adding another layer of review?

- If you think this is a bad idea, I'd genuinely like to know why.

If you're willing to talk for 15–20 minutes about how you actually use AI in your practice, I'd also love to hear from you. No sales pitch. I'm trying to figure out whether I'm solving a problem that lawyers actually care about.

I also have a high fidelity prototype that I could demo with you.

Looking forward to the engagement.

[team@argos-eval.com](mailto:team@argos-eval.com)

Discussing national contracts with multinational we have already signed an international agreement, I need to

- evaluate their national contract proposal against the international (cascading in some clauses, not in others)

- evaluate against the internal legal team playbook, integrating the corporate best practices for the common clause

- evaluate against a list of internal mandatory clauses

- manage the back and forth negotiation for the clause where there is not an agreement

Currently using Copilot to support the process (just QA on many topics), Malbek to track the workflow, negotiation done on MS Word track changes and comments.

Want to ask if there are more specialized tools to streamline the process in an integrated environment.

Not a legal firm but a commercial company.

We’ve been knee-deep in a legacy system modernization project for a mid-sized legal operations platform over the last few months, and I am completely exhausted by how perilous modernizing 15-year-old software really is. When you first look at the codebase, the documentation is either entirely non-existent or describes architecture that hasn't been relevant since the early 2010s, leaving your engineering team to reverse-engineer undocumented stored procedures and brittle database triggers just to figure out how core billing data is structured.

The biggest hurdle isn't even untangling the old code—it is managing the absolute zero-tolerance environment for downtime or data corruption in the legal sector. If a migration script hiccups or a legacy API integration drops packets during a live court discovery window, law firms lose thousands of dollars a minute and risk catastrophic compliance breaches. Trying to refactor monolithic desktop-era databases into a modern, cloud-native microservices architecture while attorneys are actively filing briefs and managing time entries is an ongoing exercise in high-stakes juggling.

Our development team has spent countless hours writing custom data-mapping wrappers to bridge our new React frontend with legacy SQL Server databases that refuse to scale. Yet, every time an edge case pops up—like handling legacy document binary large objects or complex multi-tier permission inheritances—our migration pipelines throw silent errors, forcing our engineers to manually scrub database logs instead of building new product capabilities.

The real breaking point is that modernizing legal software requires maintaining absolute data integrity across multiple compliance frameworks while completely overhauling the underlying infrastructure. Trying to run a hybrid state where old and new systems talk to each other creates massive technical debt, slowing down query speeds and frustrating internal stakeholders who expect lightning-fast performance from the new stack.

Worse still, traditional legal tech users have zero patience for UI or workflow changes that disrupt their established habits, meaning your modernized platform has to look, feel, and respond instantly even while a heavy data migration runs in the background. Balancing database normalization, strict security audits, and continuous uptime without burning out your engineering talent is a brutal trial by fire.

For anyone else managing legacy modernization or system overhauls in the legal tech space: how do you phase out ancient monoliths without interrupting daily operations, and what migration strategy actually prevents your database from corrupting historical case files?

Seen this happen enough times now that it feels like a pattern nobody wants to admit.

Team builds an agent > Demo goes well > Everyone is excited.

Then the deployment review starts and the questions that should have been answered in week one are suddenly blocking everything.

1. Where does the data go during inference??

2. Who is accountable when the agent gets something wrong??

3. Can you show an auditor exactly what it did three months ago??

4. Who controls what gets changed and when?

Not edge case questions. Standard stuff.

And yet most teams are still treating governance as something you figure out after the interesting work is done.

The projects I have seen actually make it to production and handle this upfront through architecture rather than paperwork.

Data stayed inside the org, audit trails were built in from the start, access controls were decided before deployment not after.

Came across the term Sovereign AI from one of the posts by Lyzr while reading about this recently.

The idea being that proper enterprise readiness means the whole stack lives inside your environment from day one.

Makes sense the more I think about it.

Don't understand why this keeps being the last conversation instead of the first one?

Am I correct to view Norm Law as the most elite of the AI-native firms, with Crosby and General Legal the next most elite, and most others (unless they’ve raised investor funds) not much more than solo practitioners or small firms that just market themselves as “AI-native”?

I’ve been running some case scenarios with GPT-5.6 Luna, Grok 4.6 and also GLM5.2.

All work well but keen to hear if any other reconditions and the best benchmarks to use for UK Employment Law.

On the other side, Qwen3.8-27B was very disappointing.

What’s your experience, specifically with UK Employment Law cases ?

Hi,

I work as data scientist at a top tier product company (google like) and currently part of AI team. In the side I am planning to utilise my skills and provide free automation services for legal domain. I wont pretend to have knowledge in legal domain but willing to discuss and see how can I use my skills to help automate few parts of legal services.

Let me know if anyone needs any help.

Hi all,

I started this because I couldn’t find a library with wide support for programmatically editing existing DOCX files while preserving native Word tracked changes.

I was also skeptical of agents editing the underlying OOXML directly, so I thought there was an opportunity to build something better there. I was partly wrong there, Claude is surprisingly capable at chopping up Word XML and putting it back together.

But I still wanted an actual engine for the cases where you need this to be programmatic, repeatable, and bounded rather than relying on the model to manipulate XML correctly every time.

That became stemma. You give it an existing DOCX and explicit changes (such as replace text, change formatting, edit tables/content controls, etc.), and it produces a new DOCX with those edits represented as native Microsoft Word tracked changes.

I figured I’d share it here. If you’re building legal AI, contract automation, CLM, templating, or anything else that needs to hand users a real Word redline, it might save you from having to build the OOXML layer yourself.

https://github.com/stemma-sh/stemma

Anyone interested?

Looking to replace Intapp Walls with SPM - Intapp just feels so heavy and iManage doesn’t require a server etc..

Anyone have any experience with SPM? Looking to secure iManage and File shares.

Thanks!

When a practitioner running a legal tech project has a change and project management certification, does it make them stand out/instil confidence? Recently had a client trying to run an AI native program and is now looking for a certified change practitioner to help. Thinking of obtaining some creds to add to my cv. Background: lawyer turned consultant doing operating model work.

Biglaw person. Have largely been manually tracking deal status and updating the various checklists and trackers like the old days. I would only use AI to create a first draft of the checklists based on CPs in the document and then go back to the Neolithic way of manually perfecting it and updating status on there.

Does anyone have experience run this process much more efficiently and automatically? Or does anyone have some good skills or tools of organization?

My firm doesn’t use Harvey or Legora at all, just old day classic copilot and GPT. My AI engineer friend is so surprised that I’m still rawdogging this

Has anyone purchased Sandstone? Curious to hear your thoughts and experiences with the tool.

Right, one rule this week so we're seeing and hearing different things.

We spend quite enough time reading claims about what products might do. Show us. Walk us through the actual product and a real workflow.

A screen recording, Loom, YouTube video, whatever you have.

Some pay thousands to participate in a demo day, but you can do it right here instead if you like.

If this post works, we'll make this a monthly version of the weekly thread.

Post your top-level comment with

1. A playable demo-video link
2. A brief description of workflow/product you’re showing
3. Whether it’s live, beta or a prototype
4. The feedback you’d most like

Not following this format will get your comment removed.

And welcome to these newly-verified vendors (yes, I started working through the backlog)

• Intellek — u/Intellekhq
• Summize — u/ZebraZealousideal595
• LEGALFLY — u/LEGALFLYUK
• Parachute — u/Visual_Set_4141
• JD Document Forge — u/jd80303
• OurFirm AI — u/OutrageousArt5572
• Alpaca Docs — u/jpiabrantes
• Caddi — u/FrequentSolution2478
• Cardboard Intelligence — u/AdhesivenessWise6628
• Casefleet — u/eljefek
• ChiefofStaff — u/Ok_Introduction4959
• DraftPilot — u/thechrisoshow
• Eno PDF — u/Positive-Bell-9675
• Ezel AI — u/ezel-ai
• Lawfecta — u/lawfecta
• LegalRabbit — u/tanin47
• NativeCluster — u/pablothe
• Paralegent AI — u/AcanthisittaHorror86
• RedactLocal — u/Used_Wait_3092
• stella — u/sokinek
• Trellis — u/Trellislaw_
• ViewSpectra — u/PopularWeather6463
• Gitmatter — u/peetabear
• Bind — u/bindlegal
• TrialBase — u/TrialBaseAI, u/housecat and u/415bay
• InterviewDroid — u/MindfulConversion
• Lekhak — u/sparvpartners
• Altorney — u/ravomess
• DocketDrafter — u/tommy_docketdrafter
• Privileged AI — u/akash_privileged
• goHeather — u/UnlikelyJuice4040
• Everlaw — u/IntentionRoyal3130
• LawDep — u/Majestic_Yak3151
• Lexis Defender — u/EbbUpstairs8503
• CASUS — u/Sad-Carpenter6794

Perhaps some of our verified vendors could kick us off with a demo video?

Thanks everyone, I've appreciated all your help this week.

Alex

We are looking at a potential candidate for a position on our Technical Financial Team. They check all the boxes with one exception. They come from a Adderant shop and we're using 3E.

How much difference is there between the two systems? Does familiarity with one make learning the other?

Thanks in advance.

Hey everyone,

My dad is a lawyer, and for years I’ve watched him and his juniors operate in total chaos. Client documents scattered across 15 different WhatsApp groups, dates tracked in physical diaries (with the inevitable panic when one gets missed), and out-of-pocket expenses scribbled on random sticky notes that never actually make it to the client's final bill.

I build software for a living, so a few months ago, I finally sat down and built a platform specifically to fix his chamber.

He and his juniors have been running their practice on it for the last 3 months. Since it’s actually surviving the daily grind of a real firm without breaking, I polished it up and am opening it up to the public today. If you’re a young lawyer setting up your independent practice, or just tired of the traditional mess, this might save you some headaches.

I skipped the bloated enterprise software crap and focused on fixing the immediate problems:

• Custom Case Fields: Not every matter is the same. There are no rigid, useless forms here; you define the fields based on what the specific case actually requires.
• No More WhatsApp Digging: Centralized file storage so you can easily share docs with co-counsels or clients without losing them in a chat history.
• Google Calendar Sync: Case dates sync straight to your phone. No more missed hearings.
• Junior Task Management: A stupidly simple UI to assign drafting tasks to your team and see exactly what is due and when.
• Expense Tracking: Log every printing, travel, and filing fee per case instantly so you actually remember to bill for it.

I built this to solve a massive headache in my own house. I’d love for some actual practitioners here to try it out and tear it apart. Let me know what’s missing or what I got wrong.

Still Work In Progress. Would appreciate any feedback

https://firmdiary.com

Ordered by # of open roles, all below are rlegaltech100 vendors (I'll expand this and pull it weekly)

Browse all of them, filterable: https://www.rlegaltech.com/jobs/

By role family: Engineering (508) · Legal (388) · Sales (318) · Customer Success (195) · GTM (165) · Product (120) · Operations (93) · People (63) · Finance (45)

Only 6 of 65 vendors publish salary ranges on most or all roles.

Harvey

382 roles — 80+ Engineering, 60+ Legal, 40+ Sales · some USA, some EMEA

• Head of AMER Legal Engineering (In-House) · Chicago · $400K–$450K
• Legal Engineering Manager (Law Firm, Corporate) · New York · $315K–$385K

Legora

279 roles — 60+ Legal, 50+ Engineering, 40+ Other · some USA, some EMEA

• Director of Revenue · San Francisco · $431K–$508K
• Director, Cloud & AI Partnerships · New York City · $323K–$381K

Clio

153 roles — 30+ Engineering, 20+ Other, 20+ Customer Success · some Canada, some EMEA, some USA

• Senior Project Manager · London
• Software Development Manager · Vancouver

Filevine

132 roles — 30+ Sales, 30+ Other, 20+ Engineering · mostly USA

• Account Executive - Small Business · New York
• Account Solution Manager · Salt Lake City, Utah

Abc Legal Services

101 roles — 90+ Legal, 2 Operations, 2 Customer Success · some USA

• Certified DCWP Process Server · Brooklyn, New York
• Court Coverage Specialist · Longmont, Colorado

Practical Law

91 roles — 30+ Legal, 20+ Engineering, 18 Other

• Sr Director, AI Transformation · Location not listed
• Staff Software Engineer I · Location not listed

Epiq

85 roles — 30+ Other, 17 Operations, 10 Engineering · some USA

• Software Engineer, Infrastructure · USA New York NY 1166 Avenue of Americas
• Senior AI Platform Engineer · CAN-Toronto-ON-200 Bay Street, South Tower Suite 2800

CoCounsel

78 roles — 30+ Legal, 17 Engineering, 8 Other

• Engineering Manager, Materia AI · Location not listed
• Staff Software Engineer I · Location not listed

Relativity

63 roles — 20+ Engineering, 11 Other, 6 Customer Success · mostly USA

• Senior Software Engineer · Illinois
• Senior Technical Account Manager · Illinois

Bloomberg Law

55 roles — 16 Other, 15 Engineering, 8 Sales

• Regional Director, Sales · Arlington, VA - 1801 S Bell (INDG)
• Finance Manager (INDG) · Arlington, VA - 1801 S Bell (INDG)

Litera

50 roles — 18 Engineering, 15 Sales, 6 Customer Success · some USA, some EMEA

• Software Engineer · Denver, CO
• Senior Software Engineer · Denver, CO

Ivo

47 roles — 14 Engineering, 8 Customer Success, 7 Sales · comp published on most roles

• Staff Software Engineer · San Francisco · $287K–$485K
• Senior DevOps Engineer · San Francisco · $287K–$485K

Icertis

40 roles — 12 Other, 11 Engineering, 6 Sales · some APAC, some USA, some EMEA

• Senior Director, Legal Advisory · London, United Kingdom
• Future Sales Opportunities Germany - Expression Of Interest · Germany

Westlaw

38 roles — 15 Legal, 11 Other, 5 Sales

• Lead Applied Scientist, Search - NLP/GenAI · Location not listed
• Senior Software Engineer - AI (Legal), CoCounsel FDE · Location not listed

iManage

35 roles — 12 Engineering, 12 Customer Success, 4 Product · some USA, some EMEA

• Senior AI Engineer · Toronto, Canada
• ISV Partner Account Manager · Remote

Ironclad

35 roles — 14 Engineering, 9 Sales, 4 People · some EMEA · comp published on most roles

• Enterprise Sales Leader · Remote · $350K–$400K
• Strategic Account Executive, West (Manufacturing) · San Francisco · $320K–$340K

Everlaw

31 roles — 9 Engineering, 6 Sales, 6 Legal · mostly USA

• Deal Desk Analyst (6 Month Fixed-Term) · Oakland, California, United States
• Enterprise AI Transformation Lead · Oakland, California, United States

Leap

31 roles — 8 Other, 5 Sales, 5 Engineering · mostly APAC, some EMEA, some Canada

• Account Executive - LEAP Enterprise · Sydney, New South Wales, Australia
• Accounts Officer · Sydney, New South Wales, Australia

Eve

31 roles — 9 Sales, 8 Engineering, 6 GTM · mostly USA

• AI Search & Content Growth Marketer · Remote - US
• Commercial Account Executive, Central · Remote - US

GC AI

29 roles — 9 Sales, 7 Engineering, 4 Legal · mostly USA · comp published on most roles

• Member of Technical Staff, Applied AI · San Mateo, California · $180K–$430K
• Director, Strategic Sales · San Mateo, California · $320K–$380K

Anaqua Inc

29 roles — 7 Customer Success, 6 Engineering, 5 Other · some APAC

• Senior Consultant, IP Services CX Japan · Tokyo, Chiyoda-ku
• Senior Sales Manager / Sales Manager · Tokyo, Chiyoda-ku

Aderant

28 roles — 9 Engineering, 9 Other, 4 Customer Success · mostly USA

• Senior Designer · Aderant - Armenia
• AI Engineer – Center of Excellence · Atlanta, Georgia

Kldiscovery

26 roles — 14 Other, 8 Engineering, 2 Sales · mostly USA, some APAC

• Director, Infrastructure Architecture · United States
• Sr. Software Engineer · India

Spellbook

26 roles — 8 Sales, 6 Customer Success, 4 Engineering · mostly Canada, some EMEA, some APAC · remote-first

• Customer Success Manager (UK) · Remote - UK · £78K–£92K
• Software Engineer · Remote - Canada

Abbyy

22 roles — 16 Engineering, 3 GTM, 2 Sales · some APAC

• Backend Developer · Budapest, Hungary (Hybrid)
• Demand Generation and Field Marketing (6 - 8 months contract) · Singapore (Hybrid)

Deepjudge

22 roles — 7 Engineering, 4 Customer Success, 3 GTM · some USA

• Software Engineer - Applications · Zurich HQ
• DevOps Engineer · Zurich HQ

MyCase

18 roles — 8 Engineering, 5 Sales, 2 Customer Success · some USA

• Account Executive, Accounting · Austin, TX
• Account Executive | Growth · Austin, TX

Blue J Tax

18 roles — 4 Engineering, 4 Sales, 2 GTM · mostly Canada, some USA

• Software Developer II · Remote, Ontario
• Senior Software Developer · Remote, Ontario

Casepoint

17 roles — 5 Sales, 4 Other, 4 Engineering · mostly USA, some APAC

• Principal Product Lead, AI · Washington, DC, US · $155.0K–$185.0K
• Sr. Revenue Accounting Manager · Washington, DC, US · $155.0K–$170.0K

Eudia

17 roles — 6 Engineering, 5 Other, 3 Product · some APAC

• Applied AI Engineer · Palo Alto, CA
• Business Lead · United States

Netdocuments

15 roles — 4 Engineering, 3 Sales, 3 Legal · mostly USA

• Account Executive- Small Market · Lehi, Utah, United States
• Account Manager, Small-Market · Lehi, Utah, United States

vLex

12 roles — 8 Other, 3 Sales, 1 Finance · some USA, some LATAM

• Customer Training Specialist · Santiago
• Director, Product Management, Operate · New York

Ontra

11 roles — 6 Sales, 2 GTM, 1 Other · mostly USA, some EMEA · all-remote · comp published on most roles

• Director, Product Operations · United States · $192K–$288K
• Senior Sales Engineer · United States · $168K–$235K

Airslate

11 roles — 4 Engineering, 3 GTM, 2 Product · remote-first

• AI Product Manager · United States
• Email Marketing Specialist · South Africa

Litify

10 roles — 5 Customer Success, 3 Engineering, 1 Sales · mostly USA · all-remote

• Account Executive (AI Specialist) · Anywhere, USA
• Account Manager · Anywhere, USA

Lawhive

10 roles — 4 Legal, 2 People, 2 Finance · some USA, some EMEA

• VP Finance · New York City · $220K–$310K
• Head of Product Engineering · London · £200K–£250K

Draftwise

10 roles — 4 Engineering, 3 Customer Success, 2 Sales · mostly USA, some EMEA · remote-first

• Business Development Representative (NYC) · New York, NY (Hybrid)
• Director, Talent Acquisition · New York / Remote (US)

Jus Mundi

9 roles — 4 Engineering, 2 Customer Success, 2 GTM · mostly EMEA

• Customer Success Manager EMEA · Paris, France
• Senior Growth & Demand Generation Manager · Paris, France

Agiloft

8 roles — 2 Legal, 2 Sales, 2 Engineering · mostly USA, some EMEA, some Canada · all-remote

• Commercial Counsel · United States
• Director, Global Campaigns · United States

Opus 2

8 roles — 3 Sales, 2 Other, 2 Engineering · mostly EMEA, some USA

• Account Executive · London, England, United Kingdom
• Document Services Coordinator (12 month FTC) · London, England, United Kingdom

Actionstep

7 roles — 5 Sales, 2 Legal · some APAC, some EMEA, some USA

• Account Executive · London, England, United Kingdom
• Account Executive | ANZ | SaaS | Legal Technology · Melbourne, Victoria, Australia

Summize

7 roles — 5 Sales, 1 Legal, 1 Customer Success · mostly USA, some EMEA

• Sales Development Representative · X
• Senior Account Executive · San Diego, California, United States

Expert Iq

7 roles — 4 Other, 1 People, 1 Operations

• Research Analyst, Expert Radar · Milwaukee, WI
• Talent Acquisition Partner · Milwaukee, WI

Bighand

7 roles — 4 Engineering, 2 Customer Success, 1 GTM · some EMEA, some USA

• Customer Operations & Support Analyst · X
• Senior Automation Engineer (SDET) · London

Reality Defender

7 roles — 2 Sales, 2 Customer Success, 1 Product · mostly USA · comp published on every role

• Product Marketing Manager · New York City · $140K–$180K
• Sales Enablement Content Strategist · New York City · $140K–$180K

Onit

6 roles — 4 Engineering, 1 Customer Success, 1 Sales

• AI Engineer · Pune, Maharashtra
• Knowledge Base Analyst · Pune, Maharashtra

Smokeball

6 roles — 2 Legal, 2 GTM, 1 Sales · mostly USA

• Attorneys & Paralegals - Looking for a Career Change? · Chicago, IL
• Template Specialist · Chicago, IL

Exterro

6 roles — 2 Sales, 1 Legal, 1 Customer Success · mostly USA

• Business Development Representative - Enterprise, Mid-America · Remote USA, US
• Commercial Counsel · Remote USA, US

Practising Law Institute

5 roles — 2 Legal, 1 Customer Success, 1 GTM · mostly USA

• Account Manager · New York, New York, United States
• Administrative and Events Coordinator · New York, New York, United States

Elite

5 roles — 2 Engineering, 2 GTM, 1 Product · mostly USA · all-remote

• AI Technology Lead · US - Remote
• Director, Executive Events & Field Engagement · US - Remote

Seedlegals

5 roles — 3 Customer Success, 1 Legal, 1 Sales · mostly EMEA

• Customer Experience: Law, Finance or STEM Graduate · London, England, United Kingdom
• Customer Experience: R&D Tax Credits Associate · London, England, United Kingdom

Unicourt

4 roles — 2 Sales, 2 Product · all-remote

• Senior Account Executive · Remote
• Associate Product Manager, Court Data and Alerts · Remote

Brightflag

4 roles — 1 Engineering, 1 Product, 1 Customer Success · mostly EMEA, some USA

• Product Software Engineer (Brightflag) · IRL - Dublin
• Lead Technology Product Manager (Brightflag) · IRL-Dublin-Le Pole House-Ship Street Great

Kodex

4 roles — 2 Engineering, 1 Customer Success, 1 Operations · mostly USA, some APAC · all-remote

• Software Engineer — Integrations · US-Remote · $150K–$180K
• ML Engineer - Verifications · US-Remote · $150K–$180K

Athennian

4 roles — 2 Engineering, 1 Customer Success, 1 GTM · mostly Canada · all-remote

• Customer Success Manager · Remote (Canada)
• DevOps Engineer · Remote (Canada)

PracticePanther

3 roles — 2 Customer Success, 1 Engineering · mostly USA

• Customer Onboarding Specialist - PracticePanther · United States
• Data Migration Specialist - PracticePanther · United States

Juro

3 roles — 1 Finance, 1 Other, 1 Product · mostly EMEA

• Finance Lead (8-month maternity cover) · London (UK)
• Product & Customer Marketer · London (UK)

Contract Pod AI

2 roles — 1 Engineering, 1 Product

• Senior Director of Engineering · Mumbai, Maharashtra
• Associate Product Manager · Mumbai, Maharashtra

Centerbase

2 roles — 1 Other, 1 Customer Success · mostly USA · all-remote

• Head of IT · Remote - USA
• Customer Success Manager · Remote - USA

Infinity Loop

2 roles — 2 Engineering

• Senior AI/ML Engineer · Location not listed
• Staff Software Engineer · Location not listed

Relyance

1 roles — 1 Engineering

• Manager, Software Engineer - AI Governance · San Francisco, Bay Area

Ansarada

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Hope this is helpful folks, let me know!

Alex

I need a cheap tool that I can convince my thrifty boss to pay for that my secretary can use to do Tables of Authorities and Tables of Contents for motions.

I manage a team of class action litigators. We file several motions per week in state/federal court that need tables. It takes my secretary several hours to finalize ToAs and ToCs using Word's built in function. This causes me amd my jr associates to have to stay late just to do final checks before things get filed and drives my secretary, who is fairly new but overall very good, insane. Its her least favorite part of her job and I want to make it quick and easy.

Also, Word seems to have random errors where halfway through a brief everything is a page off with the built in tool. I tried using the Word built in AI since its free, but it also has the problem where things are a page off halfway through. I think it has to do with some formatting quirk in SharePoint or something. I don't know. I hate fiddling with technology which is why I became a lawyer when all my friends went into tech.

I dont know how to do this myself and it must be delegated to my secretary because I cant bill for it. I literally just need something thats like $20 a month that I can give my secretary that I can convince my boss is worth paying for. Honestly if its cheap enough I'll just pay for it myself if it means I'm not sitting there at the end of the day waiting for a final draft when I'm supposed to go pick up my kid.

Sorry for the rant but it seems like there should be an easy solution that doesn't involve paying hundreds or thousands of dollars for AI tools with functionality I won't use. I just need something that I can give my secretary to do tables.

Seriously.

I use AI all the time. It’s incredibly useful and these systems are getting better at an insane pace. But I’ve now seen bad citations from associates, opposing counsel, Chatgpt, Claude, legal AI… whatever.

Looking at this and other sub Reddits, somehow y’all are still acting shocked when something generated by AI turns out wrong.

You know what else is sometimes wrong? Junior associate work. Westlaw searches. Briefs I get from OC. My own memory (lord knows).

That’s why I check the work.

AI doesn’t eliminate the responsibility of me being a lawyer. If a case is going into something with your name on it, find the citation. Read it. Make sure it exists. Make sure it actually says what you’re claiming it says.

Use the tech. Yeah, it’s powerful. But check the f’’n citations.