Spellbook on r/legaltech

Every mistake that AI makes in legal work product will be scrutinized publicly--and rightfully so. But I think it is under-discussed how many human errors AI can detect. Lawyers have accepted 1.3m suggestions from Spellbook so far this year. It's picked up on definitions issues, missing language, oversights and negotiation opportunities. It's virtually impossible for a human to review a 50 page contract perfectly. We have run Spellbook against hundreds of public company contracts in EDGAR and find errors and oversights in the vast majority of them.

Real lawyer + AI as a second set of eyes = best result!

We launched the first GenAI copilot for lawyers in Word 4 years ago. We love "being where lawyers work". But we are seeing other professions like Software Engineering move beyond the "Editor", to higher level delegation to agents. It was very surprising when this began to happen in software around December, with professional engineers now fully delegating work to agents. I think this will come to law as well.

We are about to launch a fully Word-compatible document editor within Spellbook's desktop/web app to enable these kinds of workflows.

We also have a Google Docs add-in in Beta, and are seeing more in-house teams move there.

We use 12+ models in Spellbook across the major providers. We find the highest performing models through constant quantitative and qualitative testing. Lawyers accepted 1.3m suggestions from Spellbook so far in 2026. We look at things like: Suggestion acceptance rate Thumbs up/thumbs down rate Internal evals based on our own benchmarks Benchmarks against public datasets

We aim to have new models in production within 24-48 hours of launch.

As soon as a user hits "thumbs down" or rejects a suggestion this will show up on our dashboards, along with any qualitative feedback given. We focus every day on driving up our "suggestion acceptance rate".

I will note: I think contract review is very subjective, so it's not just about "wrong", but about being relevant.

We also have other monitors in place to detect hallucinations or user frustration.

We see this too!

Counterintuitively, the hype around these tools has helped create massive awareness and urgency to adopt AI in law. In December we were booking 200 meetings per week with lawyers, now after the hype around Claude for law we're booking close to 400/week.

But yes: legal AI providers need to move very quickly to continue adding deep, domain-specific value beyond what the model providers do. We aim to be 2 years ahead, and have a lot of new things we're in the process of launching to our customers.

We also have unique functionality like Market Data Grounding, Playbooks, One-Click Reviews, Agents for Multi-Doc Revision, and more.

Love this question--we are always trying to think 2+ years ahead.

1. End-to-end commercial workflows: from intake, to triage, to historical risk monitoring. I think review tools will expand to integrate across in-house flows and to absorb context from many tools. We see customers rolling out Spellbook far beyond legal into procurement, sales and so-on. I think when you get into multi-department workflows, there is a lot of value to add. 2. Risk and regulatory monitoring: I think passive agents that monitor an organization's portfolio of engagements in real-time to surface risk are a no-brainer. 3. Proactive Agents: Generally I think we'll move towards having agents that work with us more like coworkers, in email and chat.

We've used a number of these, but honestly contract review is very subjective, and we've found it most helpful to look at these metrics for the suggestions/edits we surface:

User suggestion acceptance rate User Thumbs Up / Thumbs Down Rate * Qualitative user feedback

We have driven our suggestion acceptance rate from 5% when we launched in 2022, to 55%+ today. This has been a great North Star.

We deeply focus on contract workflows at Spellbook, particularly in enterprises. This calls for a specific set of capabilities:

• Grounded in realtime market data about what is normal in contracts--by industry and jurisdiction
• Playbooks for highly repeatable automated redlines
• Cross-department workflows (eg. procurement, sales)
• One-click contract reviews that learn preferences over time
• Historical insights from contract portfolio
• Multi-document revision for complex transactions
• Soon: Automated intake and triage for contracts & 24/7 risk monitoring

Differentiation seems low now because so many tools adopt a generic "Chat" user interface.

When you get beyond chat, there are so many unique workflows that our customers want that wouldn't fit into a generic tool.

For instance, we allow lawyers to compare contracts to realtime market data from 80 countries, to determine what terms are "above" or "below" market, and to actually explore that data deeply.

Also: when you consider cross-department contract workflows, triage, approvals and so-on -- these things really don't fit into a chat box.

100% agree, we see this all the time. I think CLM was premature in how useful it was, because we didn't have Large Language Models to ingest and understand agreements. It forced people to work in a structured way and to do data entry, and often business users just ended up going around the system.

I think the value of CLM will be realized with new systems built from the ground up for the AI age, which hook directly into Email, Microsoft Teams, Slack, etc. to automatically absorb context and take first passes on your behalf. This is where we are going at Spellbook.

Deep end-to-end workflows for contracts, that work across departments. From intake, to triage & review, to historical insights. Backed by real market data about what is "normal". There's more in some of my other posts but don't want to repeat too much!

We have raised nearly $100m from investors who don't believe this 😄.

A lot seems similar today because there are so many chat-shaped tools. I think we need to be more creative. When you get beyond chat, there is so much that lawyers want built, that won't make sense for OpenAI or Anthropic to build.

Just as one example: we capture realtime market data about contracts, allow you to compare it against your own agreements, and to explore the data manually. I don't foresee model providers launching specific tools like this when they are trying to get billions of users onboard.

Many of our users "go live" in the first call. The main reason things delay is generally stakeholder alignment across large teams that have diverse priorities.

Yes. Generally the cost for tokens (of the same quality) is going down over time. Engineering advances ultimately are bringing the cost of intelligence down over time. Sometimes things move in the opposite direction, but this has been rare.

Anthropic 😆. I use their tools daily and they've been a fantastic partner.

Our biggest users are law departments in enterprises, however they are rolling us out more and more to procurement, sales, HR and more in order to get a second set of eyes on contract flow across their companies.

I'm glad that model providers continue to push us all forward and to create broad awareness. Yes: there is lots of pressure to stay ahead and differentiate. Our 4,500 customers come to us for a lot of unique functionality: Grounded in real market data Playbooks for enterprise contract review One-click reviews that learn preferences over time Insights from historical contracts

And lots more (current and coming soon).

Our ICP is enterprise in-house legal teams, but we also service SMB teams, solo lawyers, small firms, mid-sized firms. The only customer we tend not to service is BigLaw. We think the billable hour incentives in BigLaw are not ideal for AI innovation.

You can connect with us at spellbook.com!

We are deeply optimized end-to-end for enterprise contract workflows. 1. Grounded in real market data 2. Surgical redlining capabilities that we've worked on for 4 years 3. Playbooks for routine reviews 4. Cross-department workflows 5. Insights and risk monitoring for historical contracts 6. Learns your review preferences over time

Good insight! One of the things we encourage our customers to do is to encode their own preferences over time. I think the times of "one size fits all" AI contract review are ending, and both manual encoding of preferences + learning individual preferences automatically is the future. That will help mitigate this.

Speed of deployment is incredibly important to us at Spellbook.

• Many users are able to get onboarded on first call (or you can just sign up on our website instantly!)
• You can create a negotiation playbook from existing materials in sub 2 minutes and refine right in app
• We have a Customer Success and Legal Solutions Architect team for to drive ROI in weeks, not months

We build everything except the foundation models. We think that model training is generally a bad bet, and everyone who has tried to train custom models in legal has failed to outperform generalist models longterm. The value comes from unique data grounding (eg. market data), unique workflows, and UI that goes far beyond chat.

1. Ground Answers in Reliable Data: We integrate realtime market data and 100+ legal research sources to ground AI in citations that can be trusted. Any time you are asking a model to use its "longterm memory" it's risky. The main way to reduce hallucination is to give models direct citations.

2. Give suggestions, not end answers: At Spellbook we focus on giving lawyers suggestions that they can review, accept or reject. We think this way of presenting information is important. Lawyers accepted 1.3m suggestions from us so far this year.

Very good question. I think "ownership" of data is going to get very fuzzy as AI makes it so easy to connect systems and to migrate data. I think that absorbing full context of deals (email, chat, doc history) is essential to do our job well. Our plan is to support both models and to allow our customers to choose.

Separate profiles per entity, separate doc libraries, separate playbooks.

We think that contract market data is very opaque, and are trying to fill this gap ourselves with a "give to get" statistical data model.

Tools like ChatGPT are heavily biased towards US public contracts that were available for training. But if I am a hospital in the UK reviewing a SaaS agreement, I am going to want AI that understands the norms in that jurisdiction and industry.

AI should allow us to make contract market data much more transparent to everyone, so we can much more easily determine norms.

The answer to this is deep and long. We have lots of information on our trust portal about privacy and security standards: https://www.spellbook.legal/security

We service multinational law firms, Fortune 100 companies, hospitals and many other privacy sensitive customers across many jurisdictions. 4,500 customers in 80 countries. So there is a good chance we can meet what you need.

Spellbook, because we were the very first to launch in 2022 and have the most functionality. We have 4,500 customers and a highly mature end-to-end AI tool for commercial legal work.

We have heard a lot of horror stories about CLM implementations from our customers. I think the old model was inherently flawed as it forced users into rigid systems, that they would end up going around.

We are built with AI in mind, and plug into where users already are. Word, Google Drive, Sharepoint, Email and so on. We think AI-first tools that sit directly on top of existing workflows will have much higher adoption rates and provide more proactive value.

Grounded in realtime market data, and deeply tuned for end-to-end contract flows.

No network drives currently in Spellbook, but we support Sharepoint, Google Drive, iManage and are always adding new integrations.

Where in my workflow do you actually reduce time in a way I can charge more or work less?

Spellbook identifies issues in agreements that humans miss most of the time. Our suggestion acceptance rate is 55% and lawyers accepted 1.3m suggestions from us this year. This allows reviews to be more thorough, gives you more threads to pull on, to deliver a better service and ultimately charge more.

What’s the highest-value use case you’ve seen for fractional GC work specifically?

Routine redlines with automated Playbooks

I had 5 $8–10k/month clients, where would your tool materially change my margins? If you are charging a flat rate, some of our customers report contract reviews being 50-70% faster. If you are billing hourly, we can surface many risks and threads that are easy to miss. What are people underpricing because they’re not using your tool properly? I think people generally underestimate the value of having a second set of eyes on every single contract to surface issues. We have reviewed thousands of public company contracts in EDGAR. Most have errors and issues.