As a company, you've got data. Every company has data - and a lot of it. But, trying to troll through everything that's ever been created in order to turn it into something useful can be a challenge. One employee in the finance department has created a spreadsheet that contains the exported revenue broken down per employee. Another employee in marketing has broken the same numbers down by department. Perfect Memory is designed to help you bring this data together in order to make more informed decisions.
Perfect Memory is a company that has built a new generation of Digital Asset Management Systems (DAM). Their As-a-Brain technology is a highly versatile knowledge management platform that works with customers to collect, interpret, and transform any data and content into natively exploitable assets for operational teams and their ecosystems. Users have quick, intuitive, and exhaustive access to data and contents, whatever their nature and origin. This allows all operational and business needs to be met efficiently. The company offers several evolutive and customizable solutions.
Perfect Memory takes all of the data sources it is given and processes what it encounters. It then takes what has been injected and turns it into a standardized dataset. This data can then be accessed, queried, and reported upon. This bridges the data gap between departments and even employees within an organization.
Unified data access can improve the lives of employees across an organization. Support people can benefit from searchable access to technical documents from a development team. A marketing department can benefit from having searchable access to revenue data, as they can respond to increased or decreased sales after particular campaigns.
Other DAM solutions may not offer this same level of functionality. Additionally, Perfect Memory offers several evolutive and customizable solutions that meet all of your operational needs. The product is the ideal solution for managing digital assets because it offers quick and easy access to data and content, as well as several evolutive and customizable solutions.
Perfect Memory is for any business or organization that wants to manage digital assets more efficiently. The company offers quick and easy access to data and content, as well as several evolutive and customizable solutions. Perfect Memory helps businesses by allowing them to manage their digital assets more efficiently. The product offers quick and easy access to data and content, as well as several evolutive and customizable solutions.
Perfect Memory serves a number of industries, including the financial sector, the marketing sector, the health sector, and the education sector. Each industry has its own unique set of needs, and the service is able to provide a solution that meets those needs. Additionally, Perfect Memory offers several evolutive and customizable solutions that are suitable for any business or organization.
Perfect Memory's DAM technology is available now. To learn more or to book a demo, head over to the company's website.
Scott is a developer who has worked on projects of varying sizes, including all of the PLUGHITZ Corporation properties. He is also known in the gaming world for his time supporting the rhythm game community, through DDRLover and hosting tournaments throughout the Tampa Bay Area. Currently, when he is not working on software projects or hosting F5 Live: Refreshing Technology, Scott can often be found returning to his high school days working with the Foundation for Inspiration and Recognition of Science and Technology (FIRST), mentoring teams and helping with ROBOTICON Tampa Bay. He has also helped found a student software learning group, the ASCII Warriors, currently housed at AMRoC Fab Lab.
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So anyway, we're gonna welcome Sebastien from Perfect Memory. Welcome to the show.
Nice to meet you.
Yeah. Nice to meet you. So what's happening with your company and what are you doing at CES? Give us the pitch.
Yeah. So we are Perfect Memory. We are a tech provider. We are developing a semantic technology that allows users to find the right information in the right context because the measure of pain in companies is that you have a lot of information. But when you are looking for something, you can find anything. So what we do basically, is that we gather in your common referential system, all the data formats, all the data sources, and we extract the information from the content itself, running, like speech to text, image or facial recognition, image analysis. And then we feed a knowledge graph that represents the knowledge inside the organization. And we give it back to users to find the information through a very easy-to-use UI.
So you're basically taking if I'm searching for a topic.
You actually, you know, when you go do a Google search, you can get 5,000 pages. And of course, it's been up to Google to decide what is relevant and what's not. And theirs, And maybe it isn't, maybe it is. So you guys take a lot of that data and other sources, and then actually pull out what is actually relevant. What's I guess, for a better word, more valid, or more trustworthy, or-.
Yeah. In fact, this is like an internal Google. But the main difference between Google is that Google is running probabilistic AI. It means that it will calculate a score to determine the content, to determine if the content is relevant for the users. What we do is that we start with a workshop with the company and then we will define with them, what is their business language, what are their business rules, and what use case they want to achieve. And, starting from this, we create what we call an ontology. This is really the description of the concept inside the organization and the relationships between those concepts. And, then we will feed the knowledge graphs with the information we have. And the machine will deduce things like we do as a human. If I say that the brother of your father is your uncle, it's easy for the cheat of two years, but the machine cannot under it. So, we will give this map representation of the knowledge of the organization so that the machine can deduce things. We call it inferences. That means that it did use relationships between concepts into the documents, into the different data of the company.
So is this mostly a B2B or B2B product?
Yeah. For now, we are mainly a B2B company. We are working with big companies, mainly in Western Europe. Broadcasters in the media industry. OTT is like Salto javelins in France. We are working in the telco company, we have some clients in the healthcare industry. I think every company is concerned because every day we have the same problem. We know that the information is here, but we can't find it. And so our system really empowers users to find, to retrieve their own knowledge, you know.
Oh, that's interesting.
Yeah. I mean, whenever you mentioned media companies, the first thing that I thought of was my experience of broadcast and spending hours trying to find B-roll that I knew I took that somebody else archived.
And it was like, I know I took this. I know it happened. And yeah, sometimes it would be a half a day search through archives trying to find it. So to be able to put in what I'm looking for, the object, the company, whatever. And habitus search through archives.
And that's terabytes of data.
Yeah, exactly. And the power of this system is that you are able to cross information. So if I am looking, we are working for the PSG. So because football club, a soccer club in France, and if you are looking for Nemo scoring goal, and just at the time where the visa logo was displayed on the LED panel when you can search that, and you will be able to give to your partner visa, the ability to search every goal where their logo is displayed. So that's powerful. And that's also a new business model for them.
Even for the On-Air broadcaster. He's trying to look up statistics real quick to find out.
The last time there were three points ahead against Manchester United.
You know, there was, you know, many times when companies do a sponsorship on a sporting event, they have people they hire to assist there with a stopwatch and just, every time they see the logo, they just run, they run the clock up to see how many minutes exposure. So this would be able to say, Okay, you got, here are the clips, here's how long you were able to.
Think about this. Think about this in terms of our industry and podcasting and IAB.
Yeah. Trying to find data.
And trying to verify that data.
Yeah. That is very, very cool stuff. Yeah. So where can people find out more information about Perfect Memory?
So we add presurgery, a two years ago, or 5 million euros. And this year, we are looking for real Syria between 20 and 30 million euros. This is part of our presence on this CES and we are developing in Europe currently. But our objective is to open an office here in the US next year. So it's a really great experience to be here today and this week because we see that there is a lot of needs. And we have a lot of visitors asking for our technology. And when we explain what we do. They're. Wow. Is that possible? Yeah, just like you said, Yeah. And yeah, we are really happy of the week?
Where's the website where people can find information?
So you can find more information on perfect-memory.com.
And you will see some use cases, business cases. And you can see also more information on how the technology works. And once it's all.
Alright, outstanding. So everyone checks it out. Sebastien, thanks for coming.
And best of luck to you in getting your next round of funding and good luck with your company.
Thank you very much.
Thank you so much. Yeah, absolutely.
Enjoy the rest of CES 2022.
TPN CES 2022 coverage is executive produced by Michele Mendez. Technical Directors are Kurt Corless and Adam Barker. Associate producers are Nancy Ertz and Maurice McCoy. Interviews are edited by Jo Mini. Hosts are Marlo Anderson, Todd Cochrane, Scott Ertz, Christopher Jordan, Daniele Mendez, and Allante Sparks. Las Vegas studio provided by HC Productions. Remote studio provided by PLUGHITZ Productions. This has been Tech Podcasts Network Production, copyright 2022.