We deliver predictive audience insights, both for film and TV and streaming content from scripts. So this changes everything for the industry that we work in, we're primarily focused on entertainment industries. We work with large streamers, large studios and networks. And I'll tell you a little bit about why it's so impactful to this industry.

So currently when a studio, for example, acquires a script, they might pay, for example, in this situation, $2 million and then $100 million. This can range honestly from 20 million to 200 million. But let's say in this situation, $100 million is spent to produce it. It's after that, that you first start getting audience feedback.

So they can do early audience feedback and understand how is this really landing? Is the audience enjoying what we've built? Do they understand what we've made? After that, maybe 50% of the budget can be spent then to fix it, to change it, to improve it. And even with all of that work, most of the time it ends up in a loss.

In this particular instance, John Carter, it was a 200 million dollar loss on paper, but this was supposed to be a franchise, so this was an $87 million potential loss. We use this example because our COO was working there at the time and working on that project, but also cuz we can look at the script and see a lot of the issues that are there.

So where we come in, if you can imagine, we pull that audience feedback before any of the money is spent. So from the moment they get the script, we're able to run that script through our technology using AI, natural language processing. We're able to deliver specific information that lets them know how it's gonna land with the audience and what things they can do to improve.

So this is incredibly valuable, but really a change to the system. It's very disruptive to put this information earlier. However, it's relatively inexpensive to make a change to the script and very expensive to make a change after the fact. So this leads to much more on budget, on time productions, limits the reshoots, but also helps them to reach the intended audience.

So we see a huge both savings but an increased profitability. So whenever I'm talking about this, I get the response of, is that even possible? How does that work? This is a new application for this type of AI. And so, it's not surprising if it sounds new. Not just a few models that say, hey, it's good or it's bad, but we've built hundreds of models to answer really the why.

So whereas we have models that will predict the size of the audience, whether it can win an award, whether it's good for particular audiences. More important than that, for the creatives that we work with, is they wanna know why. And so we have very specific models that are built on, for example, whether the character worries a lot or is clever, or the ups and downs of the emotional impact scene by scene.

So this really puts data in the hands of creatives, something they've never had before that can support the risk taking that they have to do to succeed. We know from measuring that there's an expectation level of the audience that these scripts need to hit, but there also needs to be a level of surprise.

I'm gonna compare it to even this panel right now. We're all behaving in ways that you expect. No one's jumping around doing cartwheels, we're all taking our turns speaking, right? There's a level of expectation for this genre, but if we don't say something interesting or surprising or unusual, it's also not a very interesting panel.

And so that's the same thing that we're working with in stories and that we're measuring. Is how do you both meet the audience where they are? That how do you give them something that's memorable and exciting as well? I have just a few examples cuz think I know, well, from talking, I know it's kind of hard to even imagine what this looks like.

But for example, in this case we're tracking the suspense in a particular show. And showing them how they really nailed the pilot, but they don't see the same kind of spikes and lifts in the episode 2. And so we're then comparing them to Breaking Bad, just to put it into context.

So what we're able to do, because we can measure these scripts, is we've measured thousands of scripts. So when we're telling them that a character is weak or strong or recommending changes, we can show them very clear examples of what that looks like in another show. We can do complex character networks.

For example, this is Game of Thrones that show the strength of these characters from the very beginning of the show. We can also show, for example, their characters that are on the left compared to some of their comp characters. The characters that they believe they're similar to, to help them understand where they have room to grow.

That's the last one, I did want you to see just a few because I think it helps to understand what we're looking at. A couple of lessons trickled in. One is that you have to talk to a lot of people. So, I was expecting that going into series A is looking at, and I think I pitched to 200, I think 250 people for my seed.

That's a lot of pitches. And so going into that with that expectation was good. I would say the lessons I learned is to pitch a lot and not worry about the rejections because you're literally gonna have to pitch, however many times anyway. So to just to keep going.

Other thing I like, and I feel like I'm looking forward to this in future rounds. Move from kind of the angel through the seed and then series A is that it just gets a lot more numbers based and a lot more specific. And so I feel like the seed is often, here's my number so far and here's what I'm planning to do.

But you're still selling a lot on your future same with series A, here's what I've done and here's what I'm going to do. But the further you go along the more I think numbers based and less what I found early on with the angels is you're kind of making an emotional plea in a bit each way.

And so, you're a little bit doing back bends for each different angel because they can be so different. And now speaking to seed it narrowed and then I would say to series A it's so focused like they're either interested in this industry or not. They're interested in a particular growth curve or not.

They're interested in a certain revenue model or not. So it becomes just a lot more clear in that first conversation, do we even wanna have another conversation? So I appreciate that and I do think the seed is just training for series A, which is just training for series B.

You learn a lot along the way. And I also think just the work with the series A, seed, sorry, investors is important. I think that what I've learned even after closing seed, working with seed investors is what I'm trying to say, can help you set the groundwork better for series A.

I mentor with the Techstars group, both on the Austin group and the Comcast in Philadelphia, and I just always try and look out for the women just a little bit more. Just make sure that if I have time to give, I've offered it to them first and make sure they know I'm available.

And part of that is even if they don't need me, at least it sends the message that I'm looking out for them and available. When I went through Techstars I was the only female CEO in the group and now when I go into the groups it's usually half half and there's just much more diversity in these groups.

I say that for all accelerators I'm seeing that. And so that really I think helps just build that supportive environment and I think just the concerted effort that I see it and I've seen it in every accelerator I've been a part of now. That concerted effort to really bring in teams and groups that are more diverse but both gender and racially just makes a big difference.

So, I think that's all I can do is just try and make sure I pay attention and I just try and make sure I pay attention when a woman comes through and asks for help and help however I can. That's my small part of trying to give back.