So what you’re looking at is the law view business portal. This is our customizable, um, space for legal teams to create that front door to legal. So we’ve got this configured for our imaginary building company, a MC building, but you could really configure it anyway that you wanted. So let’s start here.

So we are in the business portal, and this is the perspective of that sales person who needs to submit that urgent request through to the legal team. So as a sales rep with a third party NDA for review, I can just add the request here into the lobby business portal. I can fill out this configurable form and then I can go ahead and attach the file.

Everything on this form will be saved back into law view, so that all of the context that you’re creating here, all of that organizational administrative data will be available for the legal team. So you can track it, you can review it, and you can also report on it. That data’s gonna become accessible within Law view, and once this request is submitted, it’s gonna route through Black Boiler and be redlined before the legal team even sees it.

Having this AI integrated. Into your workflow. We talked about it a little bit earlier, but in integrated into your workflow and configured to rhyme automatically without anyone having to think about sending it anywhere really helps with adoption because no one really has to do anything for it to work.

It’s just gonna work. There’s no sort of added burden on anyone to make sure that the request is being sent through the AI redlining tool. You could use this to self-serve, and so you could be putting contracts through, um, your tool without them ever going to legal at all. But today we’re gonna show the workflow where you do have a lawyer reviewing.

So you have that lawyer, a lawyer in the loop or human in the loop. So I’ll go ahead and switch over to the perspective of an in-house lawyer who has just received this request for a contract review. So I wanna be very clear about what we’ve just done. We’ve just used that business portal to create a contract record in law view, and by creating that contract record, we sent that contract over to Black Boiler for review automatically.

So now we are looking at the law view, legal workspace from the perspective of an in-house legal user. So someone who’s a lawyer at a business and has come here to review as in-house lawyers. AI has so much potential to automate some of the more mundane parts of our roles like reviewing NDAs, but we really need that consistent and shared way of working to be able to leverage that.

So on the left hand side, um, we’ve got the intake queue and you can see that the contract that we’ve just submitted has landed right at the top of that queue. So I’m gonna go ahead and open that new request or matter. Um, and you can see that within the matter, all the data that we captured on the form has, has been pulled into here.

And along with a place for the contract and any files associated with thematic can all come and live in this, this spot as well. So it’s one place to have all of the context and all the information about that contract review. So I’m gonna go ahead and assign it to myself to review, and then if we click to open the contracts tab, we’ll see that the contract is there and there’ll be a status update to indicate that it’s already been sent for AI review.

So it’s already been redlined. So scrolling down, you can see that the first version is the one that was submitted through the law view form. And then the second version that we can see here is the version that Blake Boilers already reviewed, redlined, and returned into law view. This has been reviewed against our standard NDA Playbook.

So Blake Boiler has taken examples of edits that we’ve made before and has redline the contract according to that prior work. They’ve used that data that we’ve already created to help redline the contract and know what to do next. If we’re happy with the red lines, then we could at this point start a conversation and then our colleague and sales know that it was okay to be returned to the other party or we could open it.

If we wanted to make some more tweaks, we could open it with Word online or we could download and open it in Word. We use black Boilers word plugin to review. So we’re now in Word. We’ve opened it and I’m gonna switch over and let Dan talk us through how the Black Boiler plugin, um, operates.

So here we’re looking at the document, and if you go to the upper right hand corner, you’re gonna see the show Black Boiler Plugin.

Just go there, you’ll click on it and it’s gonna log you in and you’re gonna see a couple things tell you which playbook. You might have multiple playbooks, so you could say, this our standard NDA playbook, or it could be something else. And then you’re gonna tell you how much of that contract black weather was really able to understand.

So about 78% of that document. There are some things we try not to understand, like addresses and people’s names. You’re also gonna have a clause library. These are the clauses that you’re repetitively inserting into contracts, particularly counterparty contracts in this case. So when you click on one of those provisions, you’ll see your standard language that you might want to insert, and then it’s a one click insertion into the document in the location where you want to have it inserted.

Again, completely customizable to you. We also then have your playbook. These are your rules that you are using for negotiating and marking up your contract. They’re all right here. Again, these are completely customizable. You can create your own model with this, but when you click on one of these sections, when you click on one of these sections, you’re gonna see what your rules are that you would be editing to towards, or what Blackboard that would be automatically marking up the contract with.

So here’s one about. Confidential information has to be information provided on or after the date of the agreement. It can’t be information provided before, and you’re gonna see down below, you’re gonna see an example of other language that you’ve previously reviewed and marked up a contract towards that matches that rule.

So we’re trying to take a little bit of the black box scenario over ai. We’re trying to show you exactly how we’re learning. We’re gonna tell you how similar the text is that it learned from, and then you’re gonna be able to jump right to the edit that black boiler made. By learning from that rule and then applying that rule.

And then maybe the last example I’ll show you is deletion of fee shifting language. So that’s a prevailing party attorney’s fees being deleted out of the agreement. Again, we’re showing you that FSD, that’s what it was deleted and how similar the other language was that you have previously deleted. Once you’re done, you can go up and just hit save it to to law view, and you’re gonna be able to save it directly as the next version of that contract.

That was uploaded into Law view previously.

From here, we can use law view’s functionality to manage the contract through any approvals it needs to execution. So let’s click into the workflow management and then I’ll have the option to move this contract from draft all the way through the approval stage through signing, and then to execute it before we’ve run Zebra’s contract AI over the document to extract those key legal concepts.

So now we’re ready to sign. We integrate with DocuSign and so you can easily execute the contract directly from Law View. Clicking here, it sends it straight to DocuSign for signing, and then once the contract’s executed, we’re gonna automatically start processing the document through Law through Zebra’s.

Ai, similar to Black Boiler, Zu is deeply embedded into the workflow in Norview so that you’re not having to consciously think about this. It’s just happening behind the scenes. We have a notification that the data extraction has started up in that top right hand corner. So Zu uses supervised machine learning models, so that’s not generative ai, although they do have some generative AI now baked in as well.

But they’re each designed, each of their models is designed to identify and extract. Differently or concepts from contracts. So when you set up Law View, you can configure the concepts that you want extract to by agreement types. So you can see now that extract complete and we can click into that contract and see what’s been extracted.

So the value in running a tool like Zu is that you can see on the right hand side, we’ve extracted all of these clauses. Um, and by doing that, you’re helping to create that repository that’s gonna give you that visibility and that oversight into what’s in your contracts. So if I wasn’t using AI for this.

I probably first, I probably never get to that task of extracting that data and storing it anywhere. But also I just spend a lot of time scrolling through retyping, copying, text out, that sort of thing. So it’s really helping with the more mundane aspect of creating that CLM or recruiting that repository.

I. So to close this demo out, and we can shut that one, we can take a quick look at the reporting in law view. So on the left hand nav bar, you can see we have the reports. So we track all of those key metrics that you generate from just using Law View to manage your contracts. So you can see what sort of agreements you’re putting through, how many, and scrolling down you can see that we are tracking each of the agreements and the the key data points around stuff like turnaround times.

Who’s working on what and what stage the agreements are sitting at for the longest. So looking here, you can see which contracts by type is sitting at which stage for the longest time, so you can identify where bottleneck is and where you should focus your AI efforts to get the most impact.

Um, um, quick takeaways from the demo starting with you, ally.

Some tips for folks to think about.

I, I think the key, the key thing, and we’ve talked about this a a lot throughout the demo and before the demo, but is really that focusing on the, the super mundane stuff. Don’t, don’t try and use AI to do the complex stuff. Focus on the low risk, the low complexity tasks like data extraction, um, that are really,

they’re really gonna be good for ai.

And Dan, for the second two, do you mind giving us a quick example or reminding us, uh, how these might be applied to what we saw in the demo?

Like, like I said, I think starting early in the workflow chain is really critical. Try to get it routed to the AI automatically if you can. Particularly if there’s a place to insert it between the business unit and the legal team.

That’s probably the optimal place to put it. Uh, and if you can use prior work, if you have done what Allie recommended, which is to have ground truth, what you want the system to do, to have a playbook in place, and you’ve been operating on that playbook for a while. You like to say, if black boy put your work to work, don’t look at a contract like you’ve never looked at an NDA before.

If you’ve looked at a couple hundred over the last few years, use all of that information to make your processes faster as you go forward.

Right. And last but not least, Allie, do you wanna comment on this last tip and how people can think about that?

Yeah, I, I’d say take that high level view and keep coming back out to focus on where you can have the most value because.

There is no point implementing AI at the places where there’s no problem,

right? So starting with something that makes a lot of sense, solving the initial problem, and then incrementally rolling out over time, or updating as your business changes should be easy if you’ve chosen, uh, the right tools as well.