Building the Fair Term Checker App Part 4

How to build a well styled, automated document in legal tech

Welcome to episode 4 of Checklist Legal’s series on building a fair terms checker using document assembly technology and chatbots created with the legal tech platform Josef.

In the first episode, we talked about mind mapping the project, while the second episode covered why it’s not recommended to build an app from scratch within the app builder itself. The third episode showcased Verity’s Automation Mapper Tool, which is an excellent way to plan the context of an app, especially if you want to focus on thinking and delegate building to someone else. In this episode, we’ll discuss how to build a well styled, automated document.

Checklist Legal’s Fair Term Checker is an excellent tool to assist in-house lawyers in complying with the unfair contract terms laws set to be implemented in Australia in 2023. The user goes through a chatbot, answers questions, and at the end, receives a document with their responses. However, the document must retain its style and formatting, which is essential to users like the Checklist Legal team, who put a lot of time and effort into creating their documents. 

The app also includes a scoring element, where answers to questions, some of which are ranking questions, are scored out of 35. The scores are then chunked into three categories and represented visually with graphics. If a user scores below a particular point, they receive an “unfair” graphic, while those doing better get a different one. Depending on their answers, users get reminders and helpful hints to improve their document. There’s also a section that calls out potential problems to remind users to make their contracts fair and transparent.

The readability metrics feature is also important to the Checklist Legal team, who aim to provide content that’s easy to read and find. Making terms easy to read is critical in ensuring transparency, which is key to fairness. Understanding the business is crucial, as it allows legal advisors to avoid including unnecessary indemnities.

We hope you enjoy Verity’s video and hope that it inspires you to start some legal innovation of your own.

Click here to watch the full YouTube playlist or watch part 3 below

Need help mapping your next automation or legal tech project? Check out our Automation Mapper tool

Want to take a sneaky peak at Checklist Legal's Fair Term Checker App? You can do that below

Transcript from video

Hello and welcome to episode four. Looking at how I built the fair terms checker in document assembly technology using kind of chat bots. So episode one was the planning Episode two was why I don't recommend building in the app. Episode three showed you a template track kind of. Also, another way of planning out some of the context of the app, especially if you don't like doing the building in the app, you just want to do the thinking and then kind of palm it off to someone else to do the building.

And now we're looking at the document output. So this could be if you were building a contract, if you were building any kind of report that you want. Someone's gone through a chat bot, they've answered questions, which is great. But then do you want to have a record of that at the end? Do you want to ascend a document at the end?

So what I like about one of the things that's really crucial for me whenever I'm using any kind of document assembly technology is the style is it's got to keep my word, document styles intact. I put so much love and attention into my word document styles. They have to stay intact. Really important for me. And so you can kind of see this is not your typical kind of boring.

So it’s all Checklist Legal branded, of course. And the output here you can see for this particular app all of the variables in the template version are highlighted pink. Hopefully that's not how it looks when it gets when the actual document comes out. The other end of the assembly platform. So then you can see here the comments pop up as the potential the potential or optionality and the logic that's been applied here.

Otherwise, it's kind of just spitting out the different elements. Then there's a scoring element here as well, because based on the answers to some of these questions, some of them are text field, some of them are questions that have a ranking. Then the different scores are given out kind of out of 35. So because the platform so what I've also done based on that scoring is kind of chunk it into three and created these little graphics to visually illustrate, hey, this is what your score is.

So it's a little tricky to say in the template version, but when I show you the output version, it might be a bit clearer. So if the score in the as the person scoring through the chat bot ends up in a certain kind of role waypoint or under a certain point, they're going to get the unfair graphic. Then if they kind of doing a little bit better, they're going to get a different graphic.

And then if they're doing okay, they're going to get again, another graphic then. And you can see here because all of these conditional and it depends on the answers to the questions on whether or not that will end up in their ultimate report that they get. So they'll get some reminders and some helpful hints on ways that they can improve their document based on the answers that they've given.

And then there's a section here calling out potential problems to just say, hey, remember this is with consumers or hey, this is a small business then, because we don't want to be all doom and gloom. If they've done something really well, we want to call out this is working really well, by the way. You're always negotiating or you're always doing this.

So that's really good. Then one of my favorite also important, I do like to put in my readability metrics whenever I do a template or when I'm kind of providing some kind of content, I try to include readability metrics so that I'm practicing what I preach on readability. And this goes to this is exactly what we need to start thinking about for unfair contract terms, because one of the key issues of fairness is transparency.

What makes a clause transparent? It's easy to read and easy to find. So if you're doing any kind of work to make your terms easy to read, doing some of this readability checking, then that's really going to kind of be a great start for your unfair contracts, term contracts, reviews. And when you've done your unfair contract term review firm, you get this checked terms tracker.

So why I like this? I mean, I made it so of course I like it. But why I think it's important is some of the columns here the legitimate business reason. Why do you need this clause in your contract? Really important to understand from a legal, you know, advisors perspective. If you understand the business, you you can't just be including indemnities

Now in your contracts with small businesses. You've got to have a legitimate business reason and it can't be more than reasonably necessary to protect that legitimate business interests. So really important to capture that when you're doing your reviews. Also having this idea of an overall assessment at the end. So, yes, we think it's all maybe maybe we're doing okay on each of those areas.

But overall, are we balancing this particular clause? So I think that's really important as well. You can see some of the other factors that I've included here who checked this clause, when was it checked, when to recheck really important. And then we'll put the score in here again. And so those are the main things that I built out for this.

So you can see it's kind of quite a it looks like a longish document here. And now I'll show you one that I prepared earlier that's been filled out so you can see what it kind of ends up looking like whenever you say test to test it in. That is my test name. So we can see what was filled out here, the scores there, and then we've only got the things that relate to that school that have been put into the report then we can see our terms track there at the back. So you could go through and do the rest of the courses that that has been an overview of the actual document output from the Fair Terms Tracker. And stay tuned for the next episode where I will take you through the actual chat bot process. Bye for now.