975 Madeleine Lambert:

Wings of Inspired Business Podcast EP975—Host Melinda Wittstock Interviews Originality AI Co-Founder Madeleine Lambert

Melinda Wittstock:

Coming up on Wings of Inspired Business:

Madeleine Lambert:

The only solutions that are going to be accurate are the detectors that, like, started early. It would take a tremendous amount of work to build a solution at this point based on the, like, millions and millions and millions of data sets that exist out there. You would have a super high processing cost, and it would just take a really long time to build an accurate solution. And the same goes with text detectors. We came out like as soon, the week before, actually, ChatGPT was officially launched. We were already building. And so, we have kind of like those original foundational data sets that we created throughout the early process. And so, there’s just like no mathematical way that people or anybody would be able to be as accurate as us unless they were willing to invest a lot of money and time and research into building that foundation the way we have.

Melinda Wittstock:

Timing is everything in entrepreneurship, and it’s hard to predict when or if your vision will arrive at the very moment people are looking for a solution to the problem you are solving. Originality AI made its debut the week before ChatGPT, and as the AI slop problem grows apace, it was ready with the solution to detect it. And as Originality AI co-founder Madeleine Lambert says, the more sophisticated generative AI becomes, the more sophisticated the tools needed to track and validate it. Today she talks about the race to stay ahead, the challenges of patching vulnerabilities, how humans can best partner with AI, and how businesses can manage editorial risks.

Melinda Wittstock:

Hi, I’m your host Melinda Wittstock and this is your 40 minutes or so to let go of all the anxieties and chaos swirling around us and grab some uplifting inspiration. If you’re new to Wings of Inspired Business, this is the place where we share the inspiring entrepreneurial journeys, epiphanies, and practical advice from successful female founders … so you have everything you need at your fingertips to build the business and life of your dreams. I’ve learned more than I can verbally quantify along the ups and downs building five businesses as a serial entrepreneur, and I started this podcast to catalyze an ecosystem where women entrepreneurs mentor, promote, buy from, and invest in each other. Because together we’re stronger, and we all soar higher when we fly together and lift as we climb. If you’ve been listening to any of the past 974 episodes, please help us get the word out about the show. Please subscribe so you never miss an episode. Tell your friends and colleagues, share the episode and leave a quick 5-star rating and review on Apple, Spotify or wherever you get your podcasts. We really appreciate it. Thank you! 

Melinda Wittstock:

Today we meet an inspiring entrepreneur who co-founded and exited the marketing company Content Refined before diving into the generative AI world with Originality AI in 2022. Together with Jon Gillham, Madeleine Lambert was early to see the need for an AI solution that helped brands and businesses navigate the brave new world—maintaining ethics, authenticity and integrity in a world flooded by AI generated content. Madeleine says the spark for Originality AI came from an early understanding that AI would disrupt her previous content marketing business, and seeing that others would need tools to help them navigate thorny issues like copyright, data privacy, bias and accuracy. So today we tackle all the hot-button topics plus and the difficulty of legislating and regulating AI in both Canada and the US. Plus, some valuable insights on how businesses and creators can responsibly integrate — or detect — AI-generated content in their work.

Melinda Wittstock:

Let’s put on our wings with the inspiring Madeleine Lambert.

[INTERVIEW]

Melinda Wittstock:

Madeleine, welcome to Wings.

Madeleine Lambert:

Thank you so much for having me.

Melinda Wittstock:

I’m so excited to talk to you about AI, particularly the ethics around it and this whole mission of publishing with integrity in an AI world. So, tell me, what is Originality AI doing? How does it work?

Madeleine Lambert:

Yeah, so Originality AI is a tool that gives publishers, you know, teachers, students, anybody really writing content, the ability to do it with integrity in a world that is like saturated with AI content. We offer various solutions like AI content detection software, plagiarism software, grammar and spelling, autotype playback detection. So, lots of things that help people understand the origins of content.

Melinda Wittstock:

Right. So, is this a tool for consumers as well? So, they can kind of figure out was that AI or was that not AI?

Madeleine Lambert:

Definitely. And a lot of journalists use it in their investigations. So definitely used in a number of different ways by many different sort of customer avatars.

Melinda Wittstock:

Yeah, I can really see the use case for journalists. I am a recovering journalist, among other things, a former media correspondent of the Times of London in my early 20s. And you know, my first business was a media business. And for journalists, right now it’s really tough and it’s confusing for consumers, but for, for journalists, you know, there’s just so much AI generated content. Whereas AI can be kind of a pretty good research tool. It can also hallucinate, you don’t really know, and is sourcing…

Madeleine Lambert:

Right.

Melinda Wittstock:

…all of that kind of stuff. So, say if you’re in a newsroom or you’re a content marketer even, or anybody who needs to interact with information, this is basically like just a dashboard that you have that you run something through it or just, just, just describe the product.

Madeleine Lambert:

Yeah, so it can be accessed in a number of different ways, but the most popular way is just through the like, app interface. And that allows you to manually, like paste a block of text in there and then do a number of things to it. You can detect whether AI content or AI played a major role in the creation of that piece of text. You can see if it was plagiarized from a different source, and then you can actually run a fact checking analysis on it to see if there’s anything within that block of text that might be factually incorrect. And so that is a piece that journalists in particular really like because they’re able to do a super robust analysis on content to see if it’s factually correct or not. So, it’s pretty, pretty handy.

Melinda Wittstock:

I could Imagine just even in the context of elections and things too, with all these politicians, particularly in the US increasingly using AI, like, was it really them that was speaking like, does this work for video, I guess, and audio?

Madeleine Lambert:

Right. We do not do video or, or image analysis. We’re sticking mostly just to text analysis.

Melinda Wittstock:

Okay. Is that something that you would eventually do? I. I suppose it’s harder.

Madeleine Lambert:

So, here’s the thing with these technologies. It’s the people who started right out of the gate when all of these, you know, image, these AI image software started to come out. The only solutions that are going to be accurate are the detectors that, like, started early. And so, it would take a tremendous amount of work to build a solution at this point based on the, like, millions and millions and millions of data sets that exist out there.

Melinda Wittstock:

You’d have a pretty high processing cost.

Madeleine Lambert:

You would have a super high processing cost, and it would just take a really long time to build an accurate solution. And the same goes with text detectors. So, we were one of the first solutions that came out. We came out like as soon, the week before, actually, ChatGPT was officially launched. We were already building. And so, we have kind of like those original foundational data sets that we created throughout the early process. And so, there’s just like no mathematical way that people or anybody would be able to be as accurate as us unless they were willing to invest a lot of money and time and research into building that foundation the way we have.

Melinda Wittstock:

Right. And so, is this something that could work for businesses? Is it like an API that people can layer into something that they’re doing as well?

Madeleine Lambert:

Yes. Yeah. So that’s another way that you can interact with our technology is you can plug it in, it has an API, and you can plug it into your, like, whatever content management system you’re using or whatever like LLMS you’re using. 

Melinda Wittstock:

That’s very interesting. So technically your tool could work for understanding whether a podcast is AI, because you could analyze the transcripts of those podcasts, is that right?

Madeleine Lambert:

You could, yeah, it would be less, less accurate because a transcript is. If it’s being created using AI, there’s sort of like a layer there that would make it a little bit trickier. But yes, it’s theoretically, theoretically possible. That could be a use case for sure.

Melinda Wittstock:

Yeah, we get into all the debates going on right now about watermarking and things like that and like, just identifying this was AI just even whether it’s accurate in audio and video is becoming a real issue. And it’s also becoming an issue for content creators too, in the sense that their content is being, is being scraped and used in other people’s content, and they’re not necessarily being paid for it. Right? Like how, how we figure out the provenance of that from a content creator’s perspective. So at least if they get mashed up in somebody else’s work, they can track that, right?

Madeleine Lambert:

I think OpenAI is feeling this deeply right now because I think they’re being sued a lot because of this, right? So, I think we will see in the coming months and years the sort of like fallout from what they’ve created from, from a legal standpoint, because it is a huge copyright question mark who owns that content?

Melinda Wittstock:

It makes copyright, like all the lawyers and politicians and whatnot that make laws about copyright are completely caught on the back foot because they, they don’t even understand, like, it’s moving so fast. It’s hard for them to even catch up and understand what they should be doing from like a legislative standpoint. And I mean, and really, when it comes down to it, just, you know, beyond your business and just in this debate as a whole, does it make more sense just label original content.

Madeleine Lambert:

Right.

Melinda Wittstock:

Original, and then just assume that everything else is AI…

Madeleine Lambert:

Yeah. You know, I spoke at a, at a conference last year, a book publishing conference, because they were grappling with this issue so severely that they needed like AI, content expert to come in and talk to them about like, the implications and, and, and how difficult it is to differentiate between human written and machine written content and the risks that publishers are now facing. In light of that. And so, it was really interesting to talk to people who, who are dealing with it from a totally different perspective. You know, how do you copyright a, a book if you are not confident that it is written by a human?

Melinda Wittstock:

Yeah, exactly right. So, tell me a little bit about the origin story of Originality AI, like how did it. And, and, and you know, what are some of the challenges that you’ve had along the way? I mean, you mentioned that you started right when ChatGPT was launched. So, what did it take to build what you’ve got?

Madeleine Lambert:

What it took was an existential threat to a previous business because of AI. So, we owned and operated a content marketing business, and we created really high-quality content for people who wanted to rank their content on Google. And so, we had a team of really excellent writers who were super well versed in different fields, and it took years to build. And then we started seeing tools like Jasper AI start to emerge. Jasper AI was kind of like one of the first like the OG ChatGPT, but it was a paid, paid platform, paid tool. And when that started emerging we were like, oh, the, the cats out of the bag. This is going to be incredibly difficult to manage a business that relies on contractors writing content.

Melinda Wittstock:

Right.

Madeleine Lambert:

And so, what we did at that point was we ended up selling the business and we are super, super glad that we did because we saw that within a year that entire industry was just bleeding because the content validation question became bigger and more severe than ever. You know, it was already so difficult to prove to a client that the, the content that we were providing them was good quality. Like what metrics did those pieces of content have to hit in order for them to be good enough to hit a client’s site? And then you just add in that extra layer of additional complexity when you start to question like authorship. And we were like, man, there’s, there’s no way that we’re going to be able to, like our industry is going to change so drastically. So, then we started thinking about, okay, well agencies like ours are going to need a way to prove to their clients that the content they are providing them is original and human written. And so that’s kind of where the idea of originality AI came about. And so yeah, then we just started building it and we were one of the first of the gates. There’s like two or three other serious competitors that had the same idea.

[PROMO CREDIT]

Wings of Inspired Business is brought to you by the podcast, Zero Limits Business Growth Secrets where Steve Little – serial entrepreneur, investor and mergers & acquisitions maestro – shares the little-known 24 value drivers that spell the difference between a $5m business, and a $50mm even $500 mm business. It always pays to understand what’s driving the underlying enterprise value of your business. So, check out Zero Limits Business Growth Secrets at zerolimitsradio.com – that’s zerolimitsradio.com and available wherever you get your podcasts.

Melinda Wittstock:

And we’re back with Madeleine Lambert, one of the co-founders of the Canadian company, Originality AI.

[INTERVIEW CONTINUES]

Madeleine Lambert:

But, yeah, we launched. And a week later, OpenAI launched ChatGPT.

Madeleine Lambert:

And we were super, super accurate on it because we had been trained, our solution on similar outputs. We kind of got really lucky with timing.

Melinda Wittstock:

Yeah, that’s. I mean, timing is everything in entrepreneurship.

Madeleine Lambert:

It is. And in technology. Right? Like, the product market fit, the timing, the. The everything, the what’s happening on a, like, global level. Like, everything. And so, yeah, we. We were really lucky and we had a really good product, a good solution, one that people needed, one that people saw value in. 

Madeleine Lambert:

And in terms of challenges, we’ve had a lot of them. I don’t know.

Melinda Wittstock:

What startup has not?

Madeleine Lambert:

Yeah, like, no.

Melinda Wittstock:

Really?

Madeleine Lambert:

Yeah, of course we’ve had a lot of them. And where should I start?

Melinda Wittstock:

Well, just start. One of the things we like to do on this podcast is destigmatize, failures large and small. There’s lots of micro failures along the way as you’re testing and trying to get to product market fit, and there’s all kinds of things that come out of left field that you can’t control. I mean, there’s a million, million things. So, yeah, start wherever.

Madeleine Lambert:

Yeah. I guess for us, like, the biggest challenge would have probably been the messaging around a tool like Originality.

Melinda Wittstock:

Right.

Madeleine Lambert:

Because we didn’t want to be seen as, like, the AI police. Right. We also didn’t want, like, we ran a business where we employed, at one point, like, hundreds of freelance writers, and we didn’t want our tool to wrongly or misclassify someone’s authentic writing as AI and have them either not be able to get paid for their work or have them fired. And we knew that, like, false positives are unfortunately, like, an inherent part of a tool like ours. And so, it was really hard to, you know, position ourselves in a way that we were, like, on the writer’s side, you know, because it seemed like we were the AI police, and we were probably being used across the industry in a punitive way, and that’s it. That was really hard for us to control the narrative on how our solution should be used.

Melinda Wittstock:

So how did you get the right messaging? What is your messaging? How did you get. How did you solve that one?

Madeleine Lambert:

Yeah, I think. Well, we haven’t. There’s, you know, people misuse us all the time, but I think that the biggest thing is consistent, consistent messaging across all platforms. And so, we market ourselves as, like, a highly accurate solution. We’re extremely transparent about our false pot of positive rates, and we explicitly advise users not to use a single AI score as the only thing to evaluate a piece of content. And when it comes to academia, we, we highly, highly suggest that it’s not used as, as a measure of, as the last say, you know, as to whether a student is penalized or not, because it’s just like, not a responsible way to use our tool. Because if there’s a small chance that we’re wrong, you’re falsely accusing a student and jeopardizing their education, and that’s not cool. So, we have like, super, super clear messaging around how it should and should not be used. And we just are relentless with communicating that message.

Melinda Wittstock:

Right, Right. Yeah, it’s, it can be, it can be really tricky to, to, to, to figure that out because what you’re doing is so important. And I, I could see from a writer’s perspective, a lot of writers, a lot of people use AI. Like, like, if you do deep research on Gemini 3, say, for instance. Right. It’s really impressive, like, what it comes up with. And then if you’re writing something, if you’re a busy journalist, sometimes you can end up using it just even subconsciously. 

Melinda Wittstock:

Right. It’s a tricky path to walk and like, and then for people doing marketing, it’s the AI helps with the efficiency. Like, you can get so much more done in a really short period of time. It becomes very tempting to use. And so how has it impacted? Has your tool impacted, made people think twice about using AI or has it changed their behavior in terms of how they use AI? Or like, is it like, developing more responsible work? Like, it’s a really tricky thing to balance from that, from that standpoint. Like, if you’re a marketer, copywriter, if you’re like, you know, you’re writing a book, you’re a journalist, you’re this or this or that. 

Melinda Wittstock:

How does it, your tool basically change behavior? How would you like it to change their behavior?

Madeleine Lambert:

So, I think that our, like, mandate has always been clear where it’s like, create your content however you want to create your content. But if you are a website owner, if you are a business owner, if you’re an editor and you’re responsible for a piece of content before it hits, hits, you know, the publication that you work for, you want to know if that Is AI generated like copy and paste AI generated or not? For a number of reasons. You want to know? Because we know that AI content performs poorly in Google. We know that Google has consistently penalized AI generated content over the last few years through its algorithm updates. And if you’re a business owner and you’ve paid somebody to create a piece of content for you, you want to know that you’ve paid fair market price for something.

Melinda Wittstock:

Oh, I see. Because you pay a copywriter, meanwhile, they just have like, you know, 20, and they’re just using AI and, and then you get penalized and you have no, and you have no way of tracking that either consultant or employees work.

Madeleine Lambert:

Exactly. And so, it gives you that whole transaction piece a little bit more transparency in a world that is like so tempting to not have that transparency. And so, it really is like you.

Melinda Wittstock:

It can throw prompts into, into say, Gemini 3 to do like, you know, write the onboarding copy for this app.

Madeleine Lambert:

Right, right.

Melinda Wittstock:

And then meantime, if you use it just like as it. And like, and, and what’s crazy scary about it is a lot of it is it’s really good.

Madeleine Lambert:

It’s super good. It’s super good. But yeah, so I think the, the point of it is to give editorial teams a little bit more control over the editorial process and to make sure they understand the risks of what they’re publishing if they choose to publish AI content. And I guess it’s, it’s just making sure that that’s a clear choice. Right, versus a guess, right?

Melinda Wittstock:

A hundred percent? 100.

Madeleine Lambert:

Yeah. So, publish what you want, but at least have some idea as to what risks you might be accepting if you are publishing AI content on your site.

Melinda Wittstock:

Yeah, that makes a lot of sense. So, so how do you work with people? How much does it cost? How have you priced it? I imagine it’s like a SaaS type business model.

Madeleine Lambert:

Yeah, it’s a SaaS type business model. There are a number of ways or a number of ways. So, we have like pay as you go solution. We’ve got the monthly subscription that is a little bit cheaper. And now we have like seat seating so you can add people to your team for, you know, X amount of dollars. And then we’ve got like our enterprise users who typically need API access and who, yeah, who basically need API access and are doing like really large bulk scanning efforts, so you know, processing thousands and thousands of words. And so, some of those enterprise clients have like websites to audit, etc.

Melinda Wittstock:

Right, right, right, right.

Madeleine Lambert:

Yeah.

Melinda Wittstock:

Awesome. So, Madeleine, you’re in Canada, and Canada has a different approach to AI than the United States. The United States is, is a wild west country, and it continues to be, especially with AI.

Melinda Wittstock:

It’s all move fast, break things, who cares? Let’s, like, you know, get to IPO really fast, get as much money out of this, and build a billionaire bunker. Canada is really an engine for a lot of AI innovation. So, you’re in the epicenter of it in the Toronto area.

Madeleine Lambert:

Yeah.

Melinda Wittstock:

And. But Canada tends to be taking, like, a more ethical approach. So, tell me a little bit about the differences, like, for an AI business in Canada as opposed to one in the United States, and how the AI solutions in Canada may be safer or, like, what’s the really primary difference? I have a lot of people listening to this podcast in the US and elsewhere.

Madeleine Lambert:

Yeah. So, I know that, you know, our data security is, like, really, really, really, really enhanced here. Like, there are crazy, crazy hoops that we have to jump order to make sure that we can even provide a solution like this in Canada. And, you know, if there’s ever any, like, potential breach, like, we get audited all the time. You know, these are, these. They take it really seriously. I actually can’t really speak to what they do in the States because I have no idea whether it’s, you know, similar governance in terms of data security, but in terms of, you know, we’re dealing with a lot of people’s sensitive information. People are pasting their college dissertations, university dissertations, PhDs, into our solution.

Madeleine Lambert:

We need to make sure that our data is incredibly robust and protected.

Melinda Wittstock:

Right, right, right, right. Yeah. I mean, this is, this is, this is a really interesting thing because there’s a lot of worry given, given the political situation in the United States. Increasingly, a lot of Americans are like, is my data safe? Is my privacy safe? Am I safe?

Madeleine Lambert:

Right.

Melinda Wittstock:

So, you know, there’s a lot of, you know, there’s some companies that are just much more on the ethical side of the equation in the US Are like, well, maybe we should move all our data to Europe or to Canada.

Madeleine Lambert:

Right. Yeah.

Melinda Wittstock:

And, and so, I mean, that becomes a marketing advantage. I mean, it has that. Have you found that to be an advantage? Do you sell into the United States?

Madeleine Lambert:

Is that we definitely sell, sell into the United States, but all of our sort of like data warehouses are here, I believe.

Melinda Wittstock:

Right, right.

Madeleine Lambert:

Yeah.

Melinda Wittstock:

So where do you see it going in Canada in terms of legislation? Like, are politicians up to this task? I mean, is there a real opportunity for Canada to dominate this space as more and more people think, oh man, like, we need a real solution to this and like Europe as well?

Madeleine Lambert:

I would say, I would say that the US and China are kind of in the AI race. But that’s a great question. I think that Canada is developing amazing solutions. I think there are some really, really great AI companies coming out of here, like small ones that are gaining public attention pretty quickly. And yeah, like apparently all of the AI, like the serious AI detection firms are Canadian based. So, there’s ourselves, there’s copy leaks and there’s GPT0 and we’re all Canadian. And those are kind of like the three main players in the space.

Madeleine Lambert:

So, it’s just super, super interesting that like the, you know, the companies that are calling for like transparency and the ethical use of AI are all coming out of Canada. But. Yeah. Does that answer your question?

Melinda Wittstock:

Yeah, it, it does. 

Melinda Wittstock:

It’s very difficult for legislators to have the expertise to keep up with the, with the, you know, the, the fast pace of innovation and change in this space. And sometimes legislation has unintended consequences. Like it could accidentally like, you know, create all kinds of roadblocks to innovation. Like getting that balance right is really, is really tricky. Do you think the governments, like, what would you like to see governments doing? What, what is, you know, helpful to your business? What’s a hindrance to your business?

Madeleine Lambert:

Right, That’s a great question. So, I think that that’s always been the case with these types of technologies. Like, do you remember Facebook and the people that were questioning the, the CEO there had no idea what this technology even did, you know, So I think that there’s, it’s always been the case that there’s always a lag between governance and the, the expeditious nature of these types of technologies. Where I think it’s going to become increasingly dangerous is when politics are involved and it’s really, I think the cat is out of the bag and it’s going to be really, really hard to put it back in. And I think that there are platforms like Facebook, like Twitter, like all TikTok that, you know, people are consuming so much content, a lot of political content and it’s really, really difficult what is real and what’s not. And there’s a lot of like impressionable people that are consuming this content.

Madeleine Lambert:

And so, I think that’s where the most dangerous aspect of this type of technology, like, I think that’s where the most dangers and potential for like real societal harm come from.

Melinda Wittstock:

Right, right. Yeah, exactly.

Madeleine Lambert:

But, and I unfortunately, like, I, I don’t want to sound like a doomsdayer, but I think the cat is out of the bag. Think it’s going to be incredibly difficult to get a handle on it.

Melinda Wittstock:

Yeah, it, it is. And then, and you don’t know to what extent things like just consumer pushback on AI generally. Right. Or to what extent it’s a, it’s a bubble akin to the dot com bust. Also, just the demand for like data centers, the energy for, you know, all of this kind of stuff. So, operating in that kind of level of uncertainty, like are you, where do you see this going? Because you’re, you’re dependent on AI continuing to grow so you can better. Right, yeah. Where, where do you see all that at the moment?

Madeleine Lambert:

I see us getting increasingly busy with, you know, patching up vulnerabilities in our technology because so many LLMs get updated so frequently and they get more advanced. And so, then we need to enhance our technology as well. I see it just being like more and more difficult to keep like at pace with.

Melinda Wittstock:

Right, right, exactly. It’s, it’s, it’s really, really tricky. So anyway, where do you see originally AI? Sorry? Where do you see originality? AI being in like about 5, 10 years’ time. What’s your goal? Is this going to go to like a massive exit or like what, what’s the plan for it?

Madeleine Lambert:

I don’t think we have any like massive exit plans at the moment. I think we want to continue to grow it, continue to make it better. And you know, we’re, we’re still in high growth mode right now, so I’m not entirely sure that’s a, that’s a great question. We have that, we have that debate often. But right now, we’re happy, just kind of hunkering down, letting it let grow, pushing really hard, and then seeing what comes next.

Melinda Wittstock:

Wonderful. Well, look, I want to make sure that everybody who’s interested in, like, checking out Originality AI and they could use your services and all of that. What’s the best way to find you and work with you?

Madeleine Lambert:

Yeah, so our, our website is great. So, Originality AI and then we’ve got a great YouTube channel, and we post a bunch of really interesting studies and findings and thought leadership type content on our LinkedIn. So, yeah, that’s where you can that’s where we hang out.

Melinda Wittstock:

Fantastic. Well, thank you so much, Madeleine, for putting on your wings and flying with us today.

Madeleine Lambert:

Thank you so much for having me.

[INTERVIEW ENDS]

Melinda Wittstock:

Madeleine Lambert is one of the co-founders of Originality AI, a platform that helps publishers, writers and content marketers alike navigate the AI world to publish with integrity.

Melinda Wittstock:

Please take a moment to give us a five-star rating and review the podcast on Apple and Spotify—it helps more entrepreneurs like you find the secret sauce to support and grow their businesses.

Melinda Wittstock:

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