r/legaltech

We're drowning in contract reviev work and I need to figure out how to actually fix that

Our firm handles mostly coporate work. NDAs, vendor agreements, commercial leases. Nothing exotic. But the volume has exploded over the past few months and we are suddenlly processing hundreds of these things every week. The associates are buried. Not in actual legal work, just the grunt stuff. Drafting the same clause for the hundredth time, checking and rechecking boilerplate, manually copying data from one document to another. It's gotten to the point where maybe half our billable time goes to administrative busywork instead of actually lawyering.

We tried the obvious fix first. Template tools, shared drives, some basic automation scritps. It fell apart fast. The momnt a client wants a custom tweak or updates their playbook, every template breaks. And version tracking across multiple active deals became a nightmare. We had a couple of close cals where old language almost slipped into final execution copies. That's the kind of mistake that keepme up at night.

The real problem isn't finding software. There's plenty of legal tech out there. The problem is that none of it fits. The big platforms are too rigid. They don't accommodate how we actually review and negotiate documents. The lightweight no-code stuff is too fragile. Upload a PDF that's formatted slightly differently and the whole thing throws errors.

Our own dev team tried to bild something custom. Spent weeks on text extraction scripts and database wrappers. But they keep getting pulled away to fix broekn integrations and permission bugs, so nothing ever ships. The tools we have are brittle and half-finished, and maintaining them eats more time than they save.

So here's what I'm wondering, especially for anyone who's dealt with legal ops or workflow automation in a law firm seting. How do you actually scales this? Document procesing, template generation, the whole pipelines. Without ending up buried in custom code\risking client data confidentiality.

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u/Zenator_Biden — 14 hours ago

Are you still doing copy paste work that can be done with chatgpt ?

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.

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u/ur_piyo_a_hoe — 19 hours ago
▲ 0 r/legaltech+1 crossposts

Useful Harvey Agents

What prompts have you used to create useful Harvey agents? Looking to avoid starting from scratch in building agents. (Litigation Agents preferred)

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u/ChinoA1_ — 15 hours ago
▲ 2 r/legaltech+1 crossposts

Why does AI governance always end up being the last thing teams think about?

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?

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u/Many_Audience7660 — 18 hours ago

Is there any way to make Harvey more engaging?

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!

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u/ZutroyZephyr — 21 hours ago

Harvey announces Tenet, its first in-house, proprietary LLM for legal work

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

u/meliarobin — 1 day ago

I’m trying to figure out if AI-generated legal work needs a “trust layer”

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

u/FirstStringPM — 1 day ago

Has anyone used MCP to Connect Claude to inhouse data like NetDocuments or some other respoistory?

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

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u/MMuter — 1 day ago

How much of a law firm’s AI stack should actually be owned and controlled by the firm itself?

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?

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u/nicolasdawalibi — 2 days ago
▲ 3 r/legaltech+1 crossposts

What AI tools are actually helping law firms and legal teams right now?

There’s obviously a lot of hype around AI for law firms, in-house legal teams, and legal ops right now.

Some of it feels genuinely useful. Some of it feels like “AI for legal” being added to every product page without much substance behind it.

I’m curious what people here are actually finding helpful in day-to-day legal work.

A few areas I’ve been looking at:

  • contract review and clause analysis tools
  • legal research assistants
  • client or matter intake automation
  • document generation and template automation
  • eDiscovery and document review
  • internal legal knowledge search
  • workflow automation for approvals, routing, reminders, and handoffs

A few tools/platforms that seem relevant depending on the use case: Harvey, CoCounsel, Spellbook, Legora, even Microsoft Copilot, and workflow automation platforms like Zenphi that has a special AI for law firms offer — but this is only when the need is less about “chat with a document” and more about building a serious governed process around intake, review, approvals, document generation, and follow-up.

For those already testing or using AI in legal workflows:

What has been genuinely useful?

What looked promising but did not really work in practice?

Where do you still feel human review is absolutely non-negotiable?

And are you mostly using standalone legal AI tools, general AI assistants, or AI inside broader workflow/process automation platforms?

u/AngleHead4037 — 3 days ago

Modernizing a legacy legal tech stack without breaking active client matters is a massive engineering risk

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?

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u/punith25 — 3 days ago

Any LLM model recommendations for UK Employment law

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 ?

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u/bwelton — 4 days ago

AI engineer providing free automation services

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.

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u/FishCrafty1677 — 4 days ago

I built an open-source Word redlining engine

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

u/andreasscherman — 5 days ago

Self-Promotion Fridays (Demo Videos Only)

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

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u/alexdenne — 6 days ago
▲ 83 r/legaltech+1 crossposts

The 4 things I actually use AI for every week

I've been using AI daily in my work for about a year now. There's so much hype that I wanted to give you my list of what works for me:

  1. **First-draft demand letters and client update emails.** I give it the facts and my bullet points, it hands back 80 percent of a draft in 30 seconds. I still edit every line, but it kills the blank-page problem.
  2. **Summarizing long records.** Drop in a 60-page deposition transcript or a stack of medical records and ask for a timeline of key events with page references. Saves me an hour, every time.
  3. **Reformatting and cleanup.** Messy notes into a clean outline, a dense statute into plain English for a client, a bloated paragraph tightened up.
  4. **"Explain this to me like I'm not a specialist."** Fast orientation in an unfamiliar area before I go dig into the real authorities.

Where I stopped:

  1. **Legal research that needs real citations.** Too many fake cases. I use it to find the concept, then verify everything in an actual database.
  2. **Anything I'd file without reading closely.** That is exactly how people get sanctioned.
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u/Mysterious_Chef7417 — 7 days ago

Unpopular opinion: most Legal AI tools are not worth paying for.

I've tried more than 50 Legal AI tools and wrappers. Most of them add a nicer interface, a few predefined workflows, and a legal label on top of capabilities that are already available in general-purpose AI.

For a large part of everyday legal work, I think there is a simpler alternative:

Use the right models of ChatGPT, Claude or Grok, with web search, strong skills / plugins, and the right prompts.

So before you spend on Legal AI solutions, try the simpler alternative.

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u/rohasnagpal — 7 days ago
▲ 22 r/legaltech+1 crossposts

Built a case management tool to stop my dad's firm from losing docs and missing dates. It survived 3 months of daily use, so I'm making it public.

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

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u/HackStrix — 6 days ago

Reasons to reject Harvey / Legora

Hey all. I need a bit of help here.

I’m part of a full service firm with about 100+ lawyers. We’ve been trying out a bunch of legal AI tools the past couple of years and our Technology Committee have more or less decided to subscribe for Claude enterprise firm-wide and a smaller local AI tool specifically for research.

However, some of the older Partners are keen on us looking more into Harvey / Legora. Personally, I think they’re just caught up with the hype of Harvey and Legora being the shiny new toys that a lot of international firms are using.

We actually tried Harvey early last year but its usefulness didn’t justify the cost back then. Legora, admittedly we haven’t trialed yet but I’m personally not expecting much.

Anyway, if any of you have been using Harvey and Legora, could you let me know your experience?

I know a lot of redditors in here have said they’re basically ChatGPT / Claude wrappers - and that’s why I’d rather just get Claude too. But then I think not everyone is gonna be able to know how to prompt Claude as efficiently with skills and plugins to maximise legal work usage, whereas (I imagine) something like Harvey and Legora are more user friendly.

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u/TheNoirMan94 — 7 days ago

iManage SPM - Thoughts?

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!

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u/auenway — 5 days ago