April 11, 2025 • 16 mins
I spoke with Laurie Ehrlich, the chief legal officer, and Farah Gasmi, co-founder and chief product officer at Dioptra, an AI-powered contract redlining and playbook generation tool. We discussed the difference between RAG and Agents in AI, the challenges associated with AI adoption, and how lawyers can secure buy-in for new AI tools more effectively.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Welcome to Reinventing Professionals, a podcast hosted by industry analyst Ari Kaplan,
(00:07):
which shares ideas, guidance and perspectives from market leaders shaping the next generation
of legal and professional services.
This is Ari Kaplan, and I'm speaking today with Lori Erlich, the Chief Legal Officer
and Farragasmi, co-founder and chief product officer at Dioptra, an AI-powered contract
(00:29):
redlining and playbook generation tool.
Lori, Farragasmi, great to see you both.
Likewise, thanks for having us.
I'm looking forward to this conversation, so Farrag, tell us about your background and
the genesis of Dioptra.
I've been building AI products for the past 10 years in an above different industries,
some more regulated than others, and then most recently I was building the internal
(00:51):
AI infrastructure to have secure and safe AI tool sets, body-fying, and I've been teaching
at Columbia at the Business School for an above years and AI product management class.
To take a little bit about Dioptra, we actually started doing a lot of consulting work for
an above different enterprises and helping them with a lot of different AI applications.
(01:14):
By doing that, we were negotiating contracts with all those large enterprises, and none
of us founders are lawyers, and so we were constantly trying to understand what every
one of those terms or contracts were saying and not doing.
Because we're part of the white combinator community of founders, we're constantly chatting
with other founders, and we realized this was bigger problem than just us.
(01:36):
A lot of the small, medium, or growth and companies were having the same challenge, the business
owners were having those challenges when they negotiated contracts, and so we realized
that they had to be a better way to do that, and that was coming at about the same time
as TGBT came out, and we also had a very strong understanding of what the technology was
capable of doing, the new technology was capable of doing, and that's how we decided to
(02:00):
get started in the contracts space.
Laurie, tell us about your background and how you leverage your extensive legal experience
to enhance the contract redlining and playbook generation process.
I came out of top law school, went to big law, like everybody else does, as an IP associate,
realized that did not want a partner life, and so I looked to go in-house, and I went to
(02:25):
a large outsourcing company and did contract after contract after contract to the point
where I was so bored, I stopped reading contracts before I got on the phone to negotiate
them because they already knew what all the arguments were going to be, and I was bored,
and I needed to figure out what I wanted to do.
And I went on a self-discovery journey and realized that what I'm really passionate about
(02:46):
is transformation.
And so I pivoted while I was still a cognizant from doing, as the chief counsel for our insurance
vertical, to heading our, like, really, founding and heading our legal ops department, and
started working on data, metrics, and centralizing intake and knowledge management, and I really
(03:07):
loved that, but I didn't love not being close to the business.
And so I went to data dog, and I got to do commercial, as the chief commercial counsel,
and I got to run it from a kind of operational standpoint.
So it's not, like, just doing the contracts and law, but how do we do contracts really
efficiently?
How do we make it scalable?
(03:27):
How do we spend our time on what matters and don't spend time on things where we're not
providing really legal value?
And loved it, and it was, like, totally fulfilling and satisfying, but I wasn't as close to
the businesses I wanted to be.
And I was close to the business.
I talked to sales every day.
I talked to our partnership team every day.
I talked to all the vendor teams, but I didn't feel like I was at the core of the decision-making
(03:52):
for the company.
So I came into Diopter Room, part of every decision, which is wonderful.
But I really leveraged that combination of contracting expertise and ops expertise to help
both Diopter's development and our customers in their journey, because I know why we need
playbox.
I know the benefit of standardization on how we negotiate contracts.
(04:14):
I know how to understand when I'm looking at a red line, what matters and what doesn't
matter and help our customers to the way I think of it is when you use a tool like Diopter
Room, you can spend a lot more time on the strategy of contracting and a lot less time
on the grunt work of contracting.
So when you don't have to spend all of your time taking, you're fine, that precedent that
(04:36):
has that clause that you really like to one time and you spend two hours looking for
it and then you find it and you're like, oh, that Ashley isn't quite right.
That's not a good use of our time.
But if you can take your precedence, like we have people be able to do them, no, let me
just need a couple and synthesize them into a playbook, then you spend where I think the
real value of the capital, what lawyers bring to the table, which is looking at those
(04:59):
rules that come out of your precedent saying, I do want that in my contracts or I don't
want them in my contracts or I want this in my contracts, but actually I want it with
this variation.
And that's, to me, that's the value.
The value isn't the red line.
The value is what you decide matters to be redlined in the contract.
And so I'm able to help our customers on that journey of the self discovery of what you want
(05:23):
your contracts to be.
That's part of what you do when you say, okay, I'm not going to do the red line.
I'm going to have someone else do the red line in the AI, do the red lining, but I need
to tell the AI what to do.
>> What's the difference between rag and agents when we're talking about AI?
But lawyer was describing is a very good example of what agents do well, right?
(05:46):
So our agents are able to go through those precedents, just still the intent, and then
come up with that plan, if you will, to actually be able to build that playbook and implement
it consistently and accurately, right?
And so agents, and I know there are multiple definitions out there, but
(06:09):
agents are, or AI agents are those entities that we be able to take more than one step.
We'll be able to take multiple steps, usually we use different models to achieve every
single one of those steps.
As opposed to rag, rag is more, you have a database and, or you have some amount of data,
(06:29):
even if it is contracts or database and so on and so forth.
And you just want to search and find something from that collection of information.
And so the approach is fundamentally different.
In one step, you're focusing on a process that you're trying to achieve on the other step,
it's more like a search or like retrieval of information, which is what rag stands for,
(06:49):
right?
And so being able to search and retrieve information from a collection of data that you already
have somewhere.
>> When you want to stop having legal be directory assistance in your in-house, instead you want
people to just go to a source for that information that they're looking for, rag is fantastic
for that because it's taking the information that you've created and it's a library and you
(07:12):
really just want people to get the right answer from the library.
Whereas for RedLine, in contract, it's an action, not information.
You need that, you want a red line that is like a lawyer with red line.
And rag alone can't do that.
>> I'm always amazed by seeing all the excitement about AIH and
(07:34):
today when we started doing this, we launched our product, it was May last year.
And we launched it with, we sent Sincini back then and when we started talking about agents,
everybody was like, what are you talking about?
What are agents?
And it's like nobody, it wasn't a thing.
It was like, everybody was like into rag and everything.
(07:54):
And it was like that agents is so much more powerful because it allows you to do more complex
things.
And now everybody is asking about agents and these agents in every single operational
and enterprise and industry, right?
It's just in the legal space, they are extremely powerful when dealing with complex,
multi-step processes.
>> Lori, what challenges are you seeing around the adoption of AI?
(08:19):
>> I think there are two big challenges.
One is trust.
When nobody really knew how to use GenNRVI when it first came out.
And so people made a lot of mistakes because they thought it would be like Google search,
it's pulling things that exist, but no, it's generating, that's why it's called GenNRVI,
right?
And so we didn't use it right.
And a lot of people got hung out to drive because of errors that they made.
(08:43):
And so there's fear around thinking AI can't be trusted.
I think that's not the case anymore.
Most of the tools that are out there have conquered the hallucination issue to a large
degree.
It still happens, but it's not the Wild West anymore.
And I think the other big issue is there's just so much noise, right?
(09:05):
You can't.
No one's going to the market without saying they have AI today.
Everybody has AI.
And in some cases it's really good AI.
And in other cases it's maybe not such great AI.
And it's finding out, figuring out, like, and I'm sorry, I'll just, a lot of the in-house
departments and firms have this mandate adopt AI.
And we're seeing a huge sea change in that before it was experimented.
(09:27):
And now it's adopt.
But that's not how you solve problems.
On the lunch day, Meredith talked about how you need to figure out what your problem is.
And then go look for the solution.
And a lot of people are coming to the table saying, "I need AI."
And they haven't figured out what their problem is.
And so they're not necessarily selecting tools that are the right tool to solve their problems
(09:47):
because they're not starting from what's my problem.
And I think that's true.
It's not new to Jeremy AI, right?
That is a problem with technology adoption that it's timeless.
It's just that there's more ways, I think, with AI than there was with other tools.
>> Parallel, what type of AI are buyers most interested in?
>> It's only that I was actually reading and listening to a podcast, another podcast from
(10:10):
the OIC community.
And there was a lot of talk about AI agents and specifically in the enterprise space,
like the B2B, not SAS, B2B agents software in the enterprises.
And that is the range of applications that we hear about is tremendous.
It is any type of operational task or process, which is exactly the sweet spot of agents, right?
(10:36):
Any operational process that enterprises have to do again and again is now being automated
or semi-automated.
There's always a human interloop that is supervising what the agent is doing.
Then imagine every single process that enterprises have to do, right?
And every single one of those are now getting agents for.
(10:59):
And there's a huge amount of adoption and demand for those tools if the tool is actually working.
And I think that's a challenge.
Like Neuys said, there's a lot of noise out there today.
And it is difficult, often here customers that come to me.
And they're like, I tried XYZ and I tried ABC and I tried, and it's not working.
And why would you be able to do that differently?
And I get it, right?
(11:19):
I get it, everybody is playing with AI.
It's hard to understand what is actually going to be working from a biased perspective.
But if the tool works, I've seen a significant adoption.
Neuys is what comes, like what you're doing is has nothing to do with what I've seen.
And it's extremely different and the quality is different.
And I think that is true for not just the legal space.
(11:41):
I think that's true for all the different verticals where agents are being used.
Laurie, what's the challenge for lawyers to secure buy-in for new AI tools?
>> I think this is different from any other legal technology because AI is not a legal tool.
AI is everything tool.
It's like computers, right?
(12:01):
When companies adopt computers, it was only like lawyers had to make a business case for
computers.
It was the company saying to lawyers, and now you have to use computers.
And I think that's the same thing happening with AI that lawyers are being told you have to use AI.
And I think the problem with adoption is and buy-in is really a mix of any time there's change.
It's scary and AI specifically taking on work is scary because it's is this my job?
(12:27):
And then some tools really require lawyers to become prompt engineers, which I think lawyers
can be very good prompt engineers because it's manipulation of language, which is what we all do as lawyers.
But I compare it to the first time I tried to write a patent claim.
I met who are laughed at me because it was so bad it was written in English.
It just wasn't right for a pen.
(12:49):
And so it's the same thing.
If you could write a contract that's and then you try to write a rule for a contract that's
a prompt, it's not going to be quite right.
You have to learn how to do it.
And so the tools that require their customers to master that art without giving them assistance,
I think is asking lawyers to learn a brand new tool set.
(13:09):
How does diopter differ from other contracting tools in the market?
I've been building AI products for the past 10 years and the challenge is always being
the trade-off between how to navigate your tool is so how much can it handle right versus
what is the accuracy that you can achieve right?
And there's a trade-off because the more analyst your AI is less accurate it is at doing every
(13:34):
single thing which is understandable right.
We have been focusing on that trade-off since day one because we knew we needed to be able
to tackle a number of different types of contracts right.
And we knew also that accuracy matter like the quality of the red lines that land on that
I'm not going to say piece of paper but on that contract is important.
(13:56):
It's because we have started from that we have been able to build an agent in a way that
is extremely accurate as compared to a lot of tools out there when building the red lines
or when writing the red lines in a way that it is surgical in a way that it is elegant
and then similar to what humans would do right.
(14:17):
And then we use all those playbooks to guide and make our agent generalizable right.
And that's how we are able to tackle different types of contracts by helping our customers
build those are people from their presidents and distilling that knowledge from those presidents.
And I'll say our agent is smart enough that it keeps schooling me.
(14:39):
Laurie how do you see contracting evolving?
Oh it's going to be so different.
So right now I would say the majority of companies contracts go through every contract goes
to the legal team or contracting team and it's a lot of tasks switching.
So you have to look at the playbook or look at the guidelines or look at the checklist and
(15:01):
look at the document and go back and forth and back and forth and send it to the team and
send it to that team and there's just a lot of administration around the contracting process.
And I think that AI is going to allow us our CTO of the other days that the gift of AI
allows us to elevate our thinking.
We don't have to be in the weeds anymore.
We can think strategically spend more time thinking strategically.
(15:24):
And I think that's what we're going to be able to do.
We're going to be able to spend a lot more time thinking about the strategic aspects of
contracting and a lot less time doing the mundane administrative aspects of contracting.
This is Ari Kaplan speaking with Laurie Erlich, the chief legal officer and Farragasmi,
the co-founder and chief product officer at Deoctra, an AI powered contract redlining
(15:47):
and playbook generation tool.
Laurie Farrah thank you so very much.
Thank you Ari.
Thank you.
Thank you for listening to the reinventing professionals podcast.
Visit reinventingprofessionals.com or Ari Kaplan advisors.com to learn more.
Welcome to Reinventing Professionals, a podcast hosted by industry analyst Ari Kaplan,
(00:07):
which shares ideas, guidance and perspectives from market leaders shaping the next generation
of legal and professional services.
This is Ari Kaplan, and I'm speaking today with Lori Erlich, the Chief Legal Officer
and Farragasmi, co-founder and chief product officer at Dioptra, an AI-powered contract
(00:29):
redlining and playbook generation tool.
Lori, Farragasmi, great to see you both.
Likewise, thanks for having us.
I'm looking forward to this conversation, so Farrag, tell us about your background and
the genesis of Dioptra.
I've been building AI products for the past 10 years in an above different industries,
some more regulated than others, and then most recently I was building the internal
(00:51):
AI infrastructure to have secure and safe AI tool sets, body-fying, and I've been teaching
at Columbia at the Business School for an above years and AI product management class.
To take a little bit about Dioptra, we actually started doing a lot of consulting work for
an above different enterprises and helping them with a lot of different AI applications.
(01:14):
By doing that, we were negotiating contracts with all those large enterprises, and none
of us founders are lawyers, and so we were constantly trying to understand what every
one of those terms or contracts were saying and not doing.
Because we're part of the white combinator community of founders, we're constantly chatting
with other founders, and we realized this was bigger problem than just us.
(01:36):
A lot of the small, medium, or growth and companies were having the same challenge, the business
owners were having those challenges when they negotiated contracts, and so we realized
that they had to be a better way to do that, and that was coming at about the same time
as TGBT came out, and we also had a very strong understanding of what the technology was
capable of doing, the new technology was capable of doing, and that's how we decided to
(02:00):
get started in the contracts space.
Laurie, tell us about your background and how you leverage your extensive legal experience
to enhance the contract redlining and playbook generation process.
I came out of top law school, went to big law, like everybody else does, as an IP associate,
realized that did not want a partner life, and so I looked to go in-house, and I went to
(02:25):
a large outsourcing company and did contract after contract after contract to the point
where I was so bored, I stopped reading contracts before I got on the phone to negotiate
them because they already knew what all the arguments were going to be, and I was bored,
and I needed to figure out what I wanted to do.
And I went on a self-discovery journey and realized that what I'm really passionate about
(02:46):
is transformation.
And so I pivoted while I was still a cognizant from doing, as the chief counsel for our insurance
vertical, to heading our, like, really, founding and heading our legal ops department, and
started working on data, metrics, and centralizing intake and knowledge management, and I really
(03:07):
loved that, but I didn't love not being close to the business.
And so I went to data dog, and I got to do commercial, as the chief commercial counsel,
and I got to run it from a kind of operational standpoint.
So it's not, like, just doing the contracts and law, but how do we do contracts really
efficiently?
How do we make it scalable?
(03:27):
How do we spend our time on what matters and don't spend time on things where we're not
providing really legal value?
And loved it, and it was, like, totally fulfilling and satisfying, but I wasn't as close to
the businesses I wanted to be.
And I was close to the business.
I talked to sales every day.
I talked to our partnership team every day.
I talked to all the vendor teams, but I didn't feel like I was at the core of the decision-making
(03:52):
for the company.
So I came into Diopter Room, part of every decision, which is wonderful.
But I really leveraged that combination of contracting expertise and ops expertise to help
both Diopter's development and our customers in their journey, because I know why we need
playbox.
I know the benefit of standardization on how we negotiate contracts.
(04:14):
I know how to understand when I'm looking at a red line, what matters and what doesn't
matter and help our customers to the way I think of it is when you use a tool like Diopter
Room, you can spend a lot more time on the strategy of contracting and a lot less time
on the grunt work of contracting.
So when you don't have to spend all of your time taking, you're fine, that precedent that
(04:36):
has that clause that you really like to one time and you spend two hours looking for
it and then you find it and you're like, oh, that Ashley isn't quite right.
That's not a good use of our time.
But if you can take your precedence, like we have people be able to do them, no, let me
just need a couple and synthesize them into a playbook, then you spend where I think the
real value of the capital, what lawyers bring to the table, which is looking at those
(04:59):
rules that come out of your precedent saying, I do want that in my contracts or I don't
want them in my contracts or I want this in my contracts, but actually I want it with
this variation.
And that's, to me, that's the value.
The value isn't the red line.
The value is what you decide matters to be redlined in the contract.
And so I'm able to help our customers on that journey of the self discovery of what you want
(05:23):
your contracts to be.
That's part of what you do when you say, okay, I'm not going to do the red line.
I'm going to have someone else do the red line in the AI, do the red lining, but I need
to tell the AI what to do.
>> What's the difference between rag and agents when we're talking about AI?
But lawyer was describing is a very good example of what agents do well, right?
(05:46):
So our agents are able to go through those precedents, just still the intent, and then
come up with that plan, if you will, to actually be able to build that playbook and implement
it consistently and accurately, right?
And so agents, and I know there are multiple definitions out there, but
(06:09):
agents are, or AI agents are those entities that we be able to take more than one step.
We'll be able to take multiple steps, usually we use different models to achieve every
single one of those steps.
As opposed to rag, rag is more, you have a database and, or you have some amount of data,
(06:29):
even if it is contracts or database and so on and so forth.
And you just want to search and find something from that collection of information.
And so the approach is fundamentally different.
In one step, you're focusing on a process that you're trying to achieve on the other step,
it's more like a search or like retrieval of information, which is what rag stands for,
(06:49):
right?
And so being able to search and retrieve information from a collection of data that you already
have somewhere.
>> When you want to stop having legal be directory assistance in your in-house, instead you want
people to just go to a source for that information that they're looking for, rag is fantastic
for that because it's taking the information that you've created and it's a library and you
(07:12):
really just want people to get the right answer from the library.
Whereas for RedLine, in contract, it's an action, not information.
You need that, you want a red line that is like a lawyer with red line.
And rag alone can't do that.
>> I'm always amazed by seeing all the excitement about AIH and
(07:34):
today when we started doing this, we launched our product, it was May last year.
And we launched it with, we sent Sincini back then and when we started talking about agents,
everybody was like, what are you talking about?
What are agents?
And it's like nobody, it wasn't a thing.
It was like, everybody was like into rag and everything.
(07:54):
And it was like that agents is so much more powerful because it allows you to do more complex
things.
And now everybody is asking about agents and these agents in every single operational
and enterprise and industry, right?
It's just in the legal space, they are extremely powerful when dealing with complex,
multi-step processes.
>> Lori, what challenges are you seeing around the adoption of AI?
(08:19):
>> I think there are two big challenges.
One is trust.
When nobody really knew how to use GenNRVI when it first came out.
And so people made a lot of mistakes because they thought it would be like Google search,
it's pulling things that exist, but no, it's generating, that's why it's called GenNRVI,
right?
And so we didn't use it right.
And a lot of people got hung out to drive because of errors that they made.
(08:43):
And so there's fear around thinking AI can't be trusted.
I think that's not the case anymore.
Most of the tools that are out there have conquered the hallucination issue to a large
degree.
It still happens, but it's not the Wild West anymore.
And I think the other big issue is there's just so much noise, right?
(09:05):
You can't.
No one's going to the market without saying they have AI today.
Everybody has AI.
And in some cases it's really good AI.
And in other cases it's maybe not such great AI.
And it's finding out, figuring out, like, and I'm sorry, I'll just, a lot of the in-house
departments and firms have this mandate adopt AI.
And we're seeing a huge sea change in that before it was experimented.
(09:27):
And now it's adopt.
But that's not how you solve problems.
On the lunch day, Meredith talked about how you need to figure out what your problem is.
And then go look for the solution.
And a lot of people are coming to the table saying, "I need AI."
And they haven't figured out what their problem is.
And so they're not necessarily selecting tools that are the right tool to solve their problems
(09:47):
because they're not starting from what's my problem.
And I think that's true.
It's not new to Jeremy AI, right?
That is a problem with technology adoption that it's timeless.
It's just that there's more ways, I think, with AI than there was with other tools.
>> Parallel, what type of AI are buyers most interested in?
>> It's only that I was actually reading and listening to a podcast, another podcast from
(10:10):
the OIC community.
And there was a lot of talk about AI agents and specifically in the enterprise space,
like the B2B, not SAS, B2B agents software in the enterprises.
And that is the range of applications that we hear about is tremendous.
It is any type of operational task or process, which is exactly the sweet spot of agents, right?
(10:36):
Any operational process that enterprises have to do again and again is now being automated
or semi-automated.
There's always a human interloop that is supervising what the agent is doing.
Then imagine every single process that enterprises have to do, right?
And every single one of those are now getting agents for.
(10:59):
And there's a huge amount of adoption and demand for those tools if the tool is actually working.
And I think that's a challenge.
Like Neuys said, there's a lot of noise out there today.
And it is difficult, often here customers that come to me.
And they're like, I tried XYZ and I tried ABC and I tried, and it's not working.
And why would you be able to do that differently?
And I get it, right?
(11:19):
I get it, everybody is playing with AI.
It's hard to understand what is actually going to be working from a biased perspective.
But if the tool works, I've seen a significant adoption.
Neuys is what comes, like what you're doing is has nothing to do with what I've seen.
And it's extremely different and the quality is different.
And I think that is true for not just the legal space.
(11:41):
I think that's true for all the different verticals where agents are being used.
Laurie, what's the challenge for lawyers to secure buy-in for new AI tools?
>> I think this is different from any other legal technology because AI is not a legal tool.
AI is everything tool.
It's like computers, right?
(12:01):
When companies adopt computers, it was only like lawyers had to make a business case for
computers.
It was the company saying to lawyers, and now you have to use computers.
And I think that's the same thing happening with AI that lawyers are being told you have to use AI.
And I think the problem with adoption is and buy-in is really a mix of any time there's change.
It's scary and AI specifically taking on work is scary because it's is this my job?
(12:27):
And then some tools really require lawyers to become prompt engineers, which I think lawyers
can be very good prompt engineers because it's manipulation of language, which is what we all do as lawyers.
But I compare it to the first time I tried to write a patent claim.
I met who are laughed at me because it was so bad it was written in English.
It just wasn't right for a pen.
(12:49):
And so it's the same thing.
If you could write a contract that's and then you try to write a rule for a contract that's
a prompt, it's not going to be quite right.
You have to learn how to do it.
And so the tools that require their customers to master that art without giving them assistance,
I think is asking lawyers to learn a brand new tool set.
(13:09):
How does diopter differ from other contracting tools in the market?
I've been building AI products for the past 10 years and the challenge is always being
the trade-off between how to navigate your tool is so how much can it handle right versus
what is the accuracy that you can achieve right?
And there's a trade-off because the more analyst your AI is less accurate it is at doing every
(13:34):
single thing which is understandable right.
We have been focusing on that trade-off since day one because we knew we needed to be able
to tackle a number of different types of contracts right.
And we knew also that accuracy matter like the quality of the red lines that land on that
I'm not going to say piece of paper but on that contract is important.
(13:56):
It's because we have started from that we have been able to build an agent in a way that
is extremely accurate as compared to a lot of tools out there when building the red lines
or when writing the red lines in a way that it is surgical in a way that it is elegant
and then similar to what humans would do right.
(14:17):
And then we use all those playbooks to guide and make our agent generalizable right.
And that's how we are able to tackle different types of contracts by helping our customers
build those are people from their presidents and distilling that knowledge from those presidents.
And I'll say our agent is smart enough that it keeps schooling me.
(14:39):
Laurie how do you see contracting evolving?
Oh it's going to be so different.
So right now I would say the majority of companies contracts go through every contract goes
to the legal team or contracting team and it's a lot of tasks switching.
So you have to look at the playbook or look at the guidelines or look at the checklist and
(15:01):
look at the document and go back and forth and back and forth and send it to the team and
send it to that team and there's just a lot of administration around the contracting process.
And I think that AI is going to allow us our CTO of the other days that the gift of AI
allows us to elevate our thinking.
We don't have to be in the weeds anymore.
We can think strategically spend more time thinking strategically.
(15:24):
And I think that's what we're going to be able to do.
We're going to be able to spend a lot more time thinking about the strategic aspects of
contracting and a lot less time doing the mundane administrative aspects of contracting.
This is Ari Kaplan speaking with Laurie Erlich, the chief legal officer and Farragasmi,
the co-founder and chief product officer at Deoctra, an AI powered contract redlining
(15:47):
and playbook generation tool.
Laurie Farrah thank you so very much.
Thank you Ari.
Thank you.
Thank you for listening to the reinventing professionals podcast.
Visit reinventingprofessionals.com or Ari Kaplan advisors.com to learn more.
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