BioStrand's Work Exemplifies the IPA Family's Investment in Advanced AI, Aiding Partners in Developing New Biologics for Previously Undruggable Targets

IPA's Subsidiary, BioStrand, Provides an Update on LENSai(TM)

ImmunoPrecise Antibodies Ltd. (NASDAQ: IPA) ("ImmunoPrecise" or "IPA" or the "Company"), an AI-driven biotherapeutic research and technology company, announces that its subsidiary, BioStrand®, has commercially launched its state-of-the-art Retrieval Augmented Generation (RAG)-based Large Language Model (LLM) platform.

IPA’s Subsidiary, BioStrand, Provides an Update on LENSai™

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LensAI

Hey there. Phew, it’s been a while but so many things have happened in the past four months that one article won’t be enough to cover it all. I’ll try though! In this article, I’ll talk about: LensAI - No privacy hooks! LensAI Case Studies, Statistics, and Metrics Let’s go! Lens AI: No privacy hooks detected Literally, everyone has had a chance to give their two cents on the new cookie-less era. In 2019, when a LensAI idea was in conception, we had the following thoughts: In most cases if a user is browsing the content, then somehow it is already relevant to them, they are interested in it, and our technology must “figure out” what exactly should be advertised to them while they are browsing that content. So we and we it. Watch any of the 3 videos that go over how it works. If you already are familiar with our tech, then skip to the statistics and case studies instead: brainstormed did Get more videos from our that includes video samples for different ad goals and our own LensAI ad formats. The sky's the limit! LensAI is capable of embedding any ad formats into videos and images, including the classic ones (native ads, pop-ups, etc). LensAI can be integrated either through Google Ad Manager or Prebid inventory. gallery LensAI Case Studies, Statistics, and Conclusions Before jumping into numbers, it’s worth mentioning that we used solutions provided by that as a former startup is assisting those who are just beginning their journey. Amplitude Case Study #1 - E-commerce. CPA Model. Global Online Marketplace. LensAI technology was used in this case study. LensAI Targeting was used to pinpoint users who are browsing graphic content and context related to Style and Fashion, Music, Shopping, News and Politics, Home & Garden, etc. LensAI Ad Format was installed as an ad banner in the graphic content in which the user is browsing a product relevant to the content and context. LensAI Brand Safety safeguards the ads from prohibited content. Traffic Organic mobile and desktop traffic were collected for the period of 1 week off the websites on different topics: engineering, women’s fashion, world news and politics, adult entertainment, celebrity news, news aggregators. Goals and Ad Objectives The ad goal is to sell products from an online catalogue. To deliver information about items available for purchase to potential customers without breaching their privacy. To increase marketplace awareness and to drive more users to it through different channels. LensAI Solution LensAI was targeting potential key users via LensAI Ad Format, LensAI Targeting, LensAI Brand Safety in order to draw the attention of those who take a particular interest in online shopping in different product categories offered by the marketplace. was used to target those users who are browsing graphic content and context related to Style & Fashion, Music, Shopping, News and Politics, Home & Garden, and so on. LensAI Targeting was installed as an ad banner into the graphic content in which the user is browsing a product relevant to the content and context. LensAI Ad Format safeguards the ads from prohibited content. LensAI Brand Safety A targeted ad is being delivered to the user as soon as its image or frame gets into the browsing area. This is the safest way to reach the rightest audience and draw their attention while they are focused on studying the content. Results This ad campaign produced high CTR metrics and Conversion Rates(Payments Orders). CTR numbers fluctuated between 0,4 to 6,3% from website to website. LensAI has achieved desired CTR and sales numbers and has finalized this ad campaign as efficiently. Case Study #2- Brand Awareness. CPM Model. Local Clothing Retailer. E-commerce. LensAI technology was used in this case. LensAI Targeting was used to pinpoint users who are browsing graphic content and context related to Style and Fashion, Music, Shopping, News and Politics, Home & Garden, etc. LensAI Ad Format was installed as an ad banner in the graphic content in which the user is browsing a product relevant to the content and context. LensAI Brand Safety safeguards the ads from prohibited content. Traffic Organic mobile and desktop traffic were collected for the period of 1 week off the websites on different topics: women’s fashion, world news, and politics, celebrity news. Goals and Ad Objectives The ad goal is to increase brand awareness and draw new audiences. To deliver information about a new brand to potential customers without breaching their privacy. To increase brand awareness and to drive more users to it through different channels. LensAI Solution LensAI was targeting potential key users via , , in order to draw the attention of those who take a particular interest in online shopping of fashion clothing. LensAI Ad Format LensAI Targeting LensAI Brand Safety was used to target those users who are browsing graphic content and context related to Style & Fashion, Music, Shopping, Women’s Blogs, Dating, Home and Garden, Luxury Life. LensAI Targeting was installed as an ad banner into the graphic content in which the user is browsing a product relevant to the content and context. LensAI Ad Format safeguards the ads from prohibited content. LensAI Brand Safety A targeted ad is being delivered to the user as soon as its image or frame gets into the browsing area. This is the safest way to reach the rightest audience and draw their attention while they are focused on studying the content. Results This ad campaign produced high CTR metrics and Conversion Rates(New Users Sign Up). CTR numbers fluctuated between 0,7 to 5,7% from publisher to publisher. LensAI has achieved desired CTR and new users sign-up numbers and has finalized this ad campaign as efficient. Miscellaneous Data We tried to analyze inside and outside during these case studies. While receiving the first statistics, we thought about optimizing our calculation expenses and how this may affect our flexibility and scaling. How will the user react to the novel ad format?  Which algorithms should be applied to filter the nonessential graphic content and context for analysis? How does the tuning of our algorithms affect the impressions and their numbers? How many ad slots can we sell within one frame? What is the threshold for unit economics? The data we collected will be enough to discuss over the next couple of articles, for sure. Obviously, I won’t make all the data public. But some are worth sharing, as they reflect the results of the engineering scope. Case Number #1 и #2 Data Heavy Load Testing LensAI technology was tested on the web traffic provided by QT. Media. Goals of this testing: Workload and resilience capacity of LensAI technology when mass launched on different websites. Errors detection in data analysis with heavy loads Metrics collection: impressions, conversions, CTR, etc. Ad campaign efficacy analysis on the available web traffic Received Data As a result of the heavy load testing, we have come up with the following tech conclusions: Workload and resilience under heavy load has successfully satisfied our expectations Errors related to unessential content (porn, banners, logos, backgrounds, etc.) were detected. This enabled us to introduce new filters and to avoid unnecessary processing of the graphic content that doesn’t need to be analyzed. This has been successfully implemented. Unit economics data were measured in terms of processing huge volumes of data. Cost reduction optimization has been performed. Basic metrics have been collected; they are within the norm. Ad campaign efficacy with LensAI technology and “money dot” format  - an animated dot - can be improved through introducing new algorithms which take into account the position of the graphic content on the page. This solution will enable webmasters to select the position of the ad slot by default. This is to be implemented in the near future. The click to impression ratio has been increasing from day to day due to the increasing number of processed images as well as new traffic that is served the ad right away without having to overcome “a minimum threshold of page impressions to display ads. Oh yeah, while I was busy writing this article, we received this email from : Plug & Play Hi Pavel and Tanya,  After sitting down with our corporate partners, we are really excited to inform you that LensAI has been selected to participate in Plug and Play's Media & Ad Cohort 4! Our corporate representatives were impressed with your presentation and product. We are looking forward to having you in the program and helping you foster connections with our corporates. We are humbly happy that what we do causes a stir and caters to the needs of the current and future market needs. To sum it up, I’m including pitch speech of prepared for investors: founders

LensAI: Cookie-Busting Ads, Statistics, & Case Studies

HackerNoon Reporter: Please tell us briefly about your  background. Pavel is an experienced IT entrepreneur with a background in aviation and economics; he has been developing cybersecurity software since 1996 and has led various venture teams. Prior to LensAI, Pavel launched four start-ups with one successful exit. What's your startup called? And in a sentence or two, what does it do? LensAI. AI-powered contextual digital ad platform It all started with a sofa. Precisely, with me sitting on that sofa and clicking buttons on the remote control. After going through two hundred channels or so, surprisingly, I paused on the Shopping Channel. First of all, I was shocked that it even existed. Secondly, I was even more shocked after I realized it existed because that means someone still buys cheap crap like deluxe knives for $50, rare diamonds, and magic steamers. I kept switching channels until I ran into the "Casino Royale" movie with Daniel Craig. As I was watching, I could not stop wondering why they never sell items that they show in the movies? I liked the suit James Bond was wearing in one scene, and if given a chance right now, I would probably order it instantly. Although I did not need the suit, I liked how it looked on the actor and simply the idea of having such a suit gave me a good feeling. After I personally proved to myself that purchases are often an impulsive event, I began to grow this idea in my mind. The first question arose: what if the concept of the shopping channel was implemented right into the movie giving me, a potential consumer, an opportunity to buy things that were shown at that moment? Then, the second question followed: what if I could instantly buy all the things that I see in the digital space without spending so much time searching for them online or remembering the exact source I first saw them on? We wrote before. more about that What do you love about your team, and why are you the ones to solve this problem? After six months of intense work on the previous project, I finally got a free week or two. I was finally able to unload my brain, but my irresistible craving “to create” was too intense to ignore.
It's been a while since thoughts about the world's overproduction of goods and how it created the problem of selling rather than producing occupied my mind. I kept thinking of how quickly Instagram turned into a vast marketplace, where every homemaker sells a luxury anti-wrinkle cream. It doesn't take a brain surgeon to figure out that these led to such an oversupply of information that an average consumer can't really focus on one specific item for a long time, let alone focus on the text. Even though I’ve always enjoyed the process of solving problems, it felt like I’d had enough of it. Anyway, a solution to specific issues is a niche market, and I was ready to solve universal truths. It was time to shift my focus to creating new opportunities. I have always strived to think big. My team focuses on the solution. My team solves product problems. This complements us. I love it. It makes us strong. If you weren’t building your startup, what would you be doing? I would build another startup or enter politics :) At the moment, how do you measure success? What are your core metrics? At the moment, we are measuring conversion rates and testing the technology on 10,000+ websites. Checking the users’ behavior. We are also measuring the capacity of advertising inventory and generating metrics that will show us how to improve the technology for its smooth release into the market. What’s most exciting about your traction to date? Creation. Recognition. :)  Winning over competitors. What technologies are you currently most excited about, and most worried about? And why? Augmented reality. Neural Interfaces. These opportunities will open up the possibility for building new models of human interaction with the world as well as cognition processes. What drew you to get published on HackerNoon? What do you like most about our platform? There is an old-school spirit. Highly intelligent people get together on this platform. There are different points of view. It allows me to keep track of the direction modern IT entrepreneurship is heading in. What advice would you give to the 21-year-old version of yourself? Don't waste time on making money. Instead, create IT products. Technology is the new gold. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Happiness is when you find and accept yourself. This is the most important thing in everyone’s life. It may be difficult to find your real self, but it's worth it. LensAI was nominated as in Startups of the Year hosted by HackerNoon. one of the best startups in Ridgefield

Startup Interview with Pavel Shkliaev, CEO Of LensAI

Our hearts demand changes, Our eyes demand changes, The voice in our laughter, in our tears, and the pulse in our veins cry: "Changes! We are waiting for changes." Band: KINO Song: (aka "Changes") "Peremen!/Перемен!" Besides telling you about my Russian roots, these lines are a pure expression of upcoming inevitable changes and revolution. For ages, the song has been lifting my spirit in the toughest times, and the LensAI part of my life is no exception! So yeah, come hell or high water, LensAI is going to make it work! 💪 We are coming close to our "It's a wrap!" moment and waiting with bated breath when LensAI goes live to disturb an old-school cookie-friendly advertising era. With LensAI, there will be no tracking users and eroding their trust but only relevant contextual advertising! It's a long-awaited change that every stakeholder of the digital advertising ecosystem deserves! While tirelessly working on the product development, we made a decision to slow the pace on all external communications to invest that energy into creating a . It took us months, but it was a must, as being judged by the cover is still a thing! new website We enjoy sharing every side of LensAI and what our tireless team had accomplished during these last three months but (there is always a "BUT") not this time. Even though there is a final draft of a very piquant story ready to be posted, the Hacker Noon world will never see it: I got a soft no-no from our , who are the top industry players by the day. advisors "It is a quite questionable way to build a unicorn and make it history," they said. (Yep, these are our ambitions!) Anyway, I could not leave my people with no peek behind a curtain, so I am giving you a hint or two about the article that was scheduled to be posted today instead of this one. Firstly, try to recall where digital advertising took its roots. (We all kinda came to this world that way.) If it does not help, go back to the header image and look closely. That's the most you can get from me. #sorrynotsorry It's time to drum up some excitement about LensAI, so we are unveiling two short but informative videos for you to watch! Meet our LensAI spokesperson! She tells an easy-to-digest story on what kind of a product we are building here. Here's another one that expands on how LensAI technology turns any pics (and videos, of course) into shoppable ones within a second. To sum it up (for those who don't have time to watch it), LensAI is an AI-powered contextual computer vision ad solution that monetizes any visual content and fine-tunes targeting through identifying objects, logos, actions, and context and matching them with relevant ads. LensAI automatically embeds relevant ads straight into visual content as an overlay snippet with direct reference to the object or entire scene. The ad embedding process is instant and happening while the user is browsing the content. Those who want to dive into a cookie-less world of contextually relevant advertising, visit our new website! Come closer to a . novel advertising format What else is there to add? Maybe, some juicy details? We are happy there is zero indifference when talking about LensAI: you either love us or hate us! 😈 So, here are the top-five quotes regarding LensAI from top industry players: "A go-to website to educate yourself on contextual advertising!" "Expect copycatting of your technology from big and small fishes!" "The LensAI Era is #MoneyDots all over the Internet!" "I would pay a premium to stop seeing it! Bad for my wallet, but so much $ for publishers!" "I do not know about others, but this particular group of users will hate you a lot!" (That's coming from the person who knows more about our internal stuff than anyone else, and we kind of agree with this statement. Another hint for all those who stayed till the end of my monologue: the title of that never-to-be-released article would be "Oopsie... The Ad at That Very Moment." It tells it all, doesn't it?! 😉) PS If you are a newbie in the LensAI community, I welcome you! Here are the links to my for your convenience. previous stories

Introducing LensAI: Our Hearts Demand Changes

"Association in psychology refers to a mental connection between concepts, events, or mental states that usually stems from specific experiences." [1] Once the associative link between events A and B has been built, the appearance of event A naturally entails the appearance of event B. [2] Cool, huh? Now, imagine a scenario in which a machine (AI) understands what is happening on the user's screen (while he/she is surfing through online content) and then uses that information to show brand awareness ads designed to implant an apparent associative connection into the user's brain. Hey everyone, I'm Pavel, the CEO & Founder of LensAI. We build AI-powered advertising technology that performs in-depth aggregate analysis of visual, textual, and audio contents to enable our unique algorithms to select and then embed relevant ads into visual content automatically. Just a quick but crucial note, there is no-preprocessing needed, and everything is happening in under a sec once the user comes across the visual content. Let's start with the questions that bother me the most: How vital is non-stop complex content analysis for Advertisers? How valuable is an opportunity for ads to promote brand awareness while simultaneously being converted into real $ by facilitating impulse purchasing? How necessary to provide Advertisers with a tool/service that allows careful curation and filtration of circumstances under which to show ads for each brand/product? I am a tech guy, so maybe it will sound not very clear to you, but I will try my best to explain. Right now, our technology extracts separate frames from the video to analyze. For example, while the user is watching a reality show, the technology counts specific objects, environments, events, plus other important elements, and then it tracks the duration of their presence in the video along with the appearance of particular classes or IAB categorization. The data we get helps us design formulas that bring value to any brand as they allow the machine to get the highest level of understanding of video content and how it satisfies the needs of, let's say, the Veet brand (for those who are not familiar with a brand, it is hair removal products). We have created a technology that does a superior valuation of content based on made-to-order AI formulas: an exclusive way of setting circumstances (e.g., type of entities, environments, words pronounced, and other various parameters) under which to show ads for a specific brand. We ask our Advertisers to express their "wants" for each brand or even individual product. Then, we put them into action by enabling our algorithm to monitor particular objects, actions, events, and every critical piece of information. LensAI will never stop to enhance the machine's understanding of how different video content fits each brand/product. See the results of implementing an exclusive formula for the Enfamil brand below. From now on, LensAI can build a valuable connection between any brand/product and the relevant content. Again, no manual work is involved as a machine does everything within milliseconds once it sees that the user has landed on the page of our Publishers. The speed of the whole process is mind-blowing and one of the gems of LensAI. The formula incorporates multiple pieces of data that have to be present to initiate advertising. Let's take a look at a few examples that demonstrate how LensAI works from an end-user perspective. We used visual content currently available online for demo purposes: Due to the short length of the videos, we set the Ad Display Setting to "Highest Frequency" to demonstrate technology possibilities. Now let's watch the same video to observe how the machine inserted completely different ads into it. Why is that? What made it change its mind? That's what happens when we make our machine respect each viewer's individual characteristics based on the IAB Audience. These two examples prove our machine's ability to select various objects and use them for building associative connections with different brands and their products. Demography, interests, previous purchases, and other additional data about users influence the machine's decisions about selecting the most relevant ad, its placement, and advertising purposes it should serve. I have mentioned above that LensAI can build a valuable connection between any brand/product and the relevant content while the user is surfing on the page or when he/she hits the play button. We didn't stop there and added another precious piece of information - specific data about each user - to allow AI to enhance targeted advertising. Your Questions Here are the top three fair comments/questions/concerns that our followers expressed: "I saw a total of five ads for Lysol in your videos. The ads were shown in places where no Lysol disinfectant spray was present. That's exactly what other ad networks do: show ads of anything that has to be sold everywhere and anywhere. So, what differentiates you from them? If a beautiful cover does not lead directly to sales, it has close to zero value. So, why should I choose your product? Why should I use your software?" "I was hoping you could explain the benefits for me as a potential client rather than a consumer who is the last link in the advertising chain. Make it simple. Suppose I have pictures and videos on the site and participate in the affiliate programs (ranging from marketplaces like Amazon to affiliate marketing service providers like Rakuten Marketing). Why do I switch to you? What are the distinctive advantages you have over your competitors?" "I have a question from the end-user perspective. What if I see some item of interest in the video, but the Publisher who partnered with you does not market that product (let's say, it's a dining table). Suppose I keep seeing Lysol ads embedded into that item and, even worse, other ads embedded into other objects according to the same scenario. In that case, I will stop paying attention to your ads due to seeing "wrong" ads. If users like me were able to build an immune system for banner ads, we could build one for LensAI ads too." My Answers I love questions like these and take any criticism with appreciation. So, here are my answers to those who reached out to me personally and all of you who might have similar concerns. Everything you currently see is a basic framework. We currently limit inventory that we use for analysis to cut computational resources expenses and choose short videos for demos for the same reasons. The high density of ads that you witness now is not pleasant even for me. The final product will enable Publishers to control it and adjust what is needed based on Analytical Data we make available to them (e.g., % of users who watched the video to the end). Let me speak the truth. Yes, we work hellishly hard to ensure the most accurate matching of identified objects to products available to advertise. We do want users to enjoy the interactive advertising experience with the eCommerce touch we offer. Even though we consider all stakeholders' interests, the software is being developed to precisely satisfy the needs of Advertisers and Publishers. Regarding the question about our competitors, please study the slide below. Participation in various affiliate programs does not allow Publishers to market products through image and video content available on their sites. No one but LensAI can enable them to do so. None of our competitors is technologically capable of selecting the fittest product to advertise from numerous different product lines and categories among several marketplaces. That's why our competitors by technology cannot sufficiently monetize traffic; they cannot cover a large volume of goods and, therefore, cannot find the ideal goods from advertising inventory to display (feel free to read about it ). here Impressions equal money; that's clearly why they are important to Publishers. Each of our technological competitors takes away an opportunity for Publishers to dramatically increase their revenue by their inability to match advertising inventory to the graphic content that Publishers posted, post, and will post. Suppose our machine cannot find a perfect product to advertise among products available on the marketplaces we affiliate with. In that case, we will promote the relevant item that satisfies either cross-sell or up-sell requirements (we wrote about it ). Meaning, Publishers will never lose an opportunity to make $ while users will never question why this particular ad pops up. Relevancy and similarity are the keys to success. Don't forget about our brand associations formulas we do for brand awareness ads. here Here's quite an upsetting message for the users. The dot format is simply one of the numerous designs and UX options. We take ad blindness seriously, so we made plans A, B, and Z to fight any derivative form of ad blindness syndrome. Maybe I should write another article about the advertising standard that we developed as it allows superimposing any UI/UX as easy as one-two-three. Imagination is the only thing that can stop us, and we all know it is limitless. Long story short, no technologies and products that are known today can operate according to this scheme: the installation of the script on the website/app within minutes-> the extraction of the content in real-time (once the user lands on the page) -> the detection of objects and matching them to any possible product available for sale ->  embedding the fittest ad to re-sell the product for a commission -> if no similar product is found, either a relevant brand ad or a relevant product ad pops up. Easy-peasy! Nothing is more upsetting than seeing someone do something you thought was impossible! Create brand formulas with us! Invest in us! We are open to any collaborations, so let's do something novel together! For those who want to dive in, read the I wrote. The truth is somewhere near. previous articles

LensAI: Associative Advertising as An Inevitable Evil

Have you heard or perhaps even tried new ways to purchase stuff you see on TV? You know, the ones that invite you to buy things you see while your favorite show is being aired? They offer you to shop through various user interaction mechanics that range from scanning a QR code shown at the corner of the TV screen to pressing a set of navigation buttons on the remote control to receive a text message with a link to the product. What a maze, I have to say. Come on, people! Do you really think the user can enjoy such an effortful shopping experience? I would change my mind to purchase something a number of times before completing the "hell of navigations" like this... Let me spill the tea here. We are often compared to such products, and when it happens, it devastates us. We Are Different If you missed the previous articles about our technology, I recommend you familiarize yourself with what we've been working on and how it all started before reading this one (it will make more sense to you then). How We Taught Artificial Intelligence to Sell Where Can Dreams Take You? The Art of Teaching Associative Thinking to Machines Online Advertising in The Time of The Consumer Attention Deficit Hyperactivity Syndrome (ADHD) Different by Concept Inspired by computer games, cyberpunk, and HUD interfaces, we imagined a wholly digitalized and full of AR-based interactions world where every object would allow you to get information about itself and there were no boundaries to purchase it. That's how we started to develop our internally called "seamless sale" concept that would create an opportunity for the user to buy goods directly from the open-world when interacting with the object or viewing the content where the object was. No visit to the store's website or another place is necessary. Effortless! For us, LensAI, the fundamental quest was to solve inefficient web traffic monetization by installing our ads in any graphic and video content regardless of its release date. In "techy" words, we had to train the machine not only to detect all types of objects in the content (that vary from cats and dogs to a spaceship) but detect these objects instantly. To execute it instantly was essential criteria as we wanted the machine to show only relevant to the content ads at the very same moment when the user is viewing that content. To solve the before-mentioned problems, we first had to become conceptually different by technology, and everything else was second. Our conceptual difference is not recognizing objects and then searching for similar ones, but the machine's ability to begin to understand the cumulative meaning of all three types of content: visual, textual, and audio. Feel free to explore more details on the challenges we faced in the article . Where Can Dreams Take You? The Art of Teaching Associative Thinking to Machines Different by Technology The foundation of the world's digitalization lies in the classification of all possible entities, objects, events, actions, and relationships between all of them. It reminds me of creating a game engine where particle interaction (physics) is replaced with a comprehensive advertising graph with a detailed description of how every entity relates to and influences each other. When creating our relevance model of advertising, we combined various taxonomies (taxonomy "is the practice and science of classification of things or concepts, including the principles that underlie such classification"). [1] In the model, our AI detects every entity and how entities influence each other. This approach gave us a clear understanding of how everything found in the image correlates to its context. The understanding we’ve gained opens up almost endless possibilities for both displaying ads and accomplishing more specific tasks. Different by Capabilities Our technological differences get us the access to previously inaccessible opportunities. Advertising Selection for Any Object Case #1: The advertising goal is to sell products from the eCommerce catalog by selecting the fittest ones for ad display from 5,500 advertising categories from Google Products and advertising feeds/APIs of top US marketplaces. Tasks: - Detect objects images or video frames.
- Determine which the objects are suitable display advertising.
- First, each object that was determined suitable display advertising, select relevant advertising e-commerce categories, marketplaces, and then specific products (that range anything like pet goods to aircraft parts).
- Lastly, select the fittest products ad impressions based on:
       -- the product availabilities, the rotation impressions, and other parameters that affect the decision to display a particular product,
       -- the context analysis,
       -- the similarity analysis. in of for for as for from for of Product Display Ads: Cross-Sell & Upsell Case #2 The advertising goal is to sell products from the eCommerce catalog. Tasks: - Detect objects images or video frames.
- Determine which the objects are suitable display advertising.
- Select the fittest eCommerce advertising creatives to render on a user in of for 's screen depending on the product availability, rotation of ad impressions, and other parameters that influence the decision of ads being fetched. Case #3 The advertising goal is to sell products from the eCommerce catalog. Relevant Ad Selection Regardless of The Initial Content Release Date Tasks: - Detect objects images or video frames.
- Determine which the objects are suitable display advertising.
- Select the fittest eCommerce advertising creatives based on associative thinking algorithms. in of for Associative Display Advertising Selection Case #4 The advertising goal is to boost brand awareness, traffic, and conversions. Tasks: - Detect objects images on the site.
- Determine which the objects are associatively suitable to use displaying advertisements various plans the internet, cable TV, or telephone.
- Select the fittest advertising creatives among others provided by an advertiser (there are many such creatives).
- Embed advertising creatives. in of for of for of New Opportunities for Advertisers New Opportunities for Publishers Different by Ad Format: Visualization & User Interaction At the early "birth-of-an-idea" stage, we dreamt of AR smart glasses. We were inspired by interaction with the outside world and the instant "getting-to-know-it" process: the possibility to obtain all information about the object as soon as we focus our attention on it. The cost of consumer attention rises every day. Rapid embedding of our ad formats straight into the graphic/video content that had caught the user's eyes seemed to be the most promising. Alongside, we enlivened the picture with animation, added bindings, and tried to cause no significant interruptions to the main content. Initially, we craved to implement a spatial positioning algorithm that would consider objects' perspective in the frame to make our ads shift relative to other objects while the video was being watched. The old geek wisdom saved me from troubles (at least for now): "Do not develop new functions until you have not sold the old ones." Having developed a completely new #MoneyDot format, we endowed it with great flexibility and settings for various uses based on advertising goals and advertising creatives currently available on the market. #MoneyDot Ad Formats Our ad formats support the following advertising goals: Brand Awareness Traffic Mobile App Downloads Video Views Conversions & Message Displays eCommerce Catalog Sales We work with Publishers according to the following scenario: Publishers install LensAI scripts to their websites. The installation process is similar to Google AdSense. Publishers may configure or use default settings. Scripts extract visual content once user traffic is recorded. LensAI servers analyze the content of the web pages, identify images and videos, and detect objects in them (e.g., a dress, chairs, a microwave, etc.) LensAI algorithms find matching product categories for the detected objects and then products themselves. A user comes to the website. (*Optional Step) The LensAI scripts that were pre-installed (see #1) look up the user's interests through cookies. Based on that particular user's interests, content and context analysis, ads of the most relevant products are embedded into the objects of visual (video and image) content. LensAI receives a commission from marketplaces/advertisers for ad impressions and purchases made by users on Publishing Partners' websites. LensAI shares its earned commission with its Publishing Partners. The Cases Demonstration of Technology on The Verizon FIOS TV Platform (In-Video Ads) For this demonstration, the following tasks were set: - Show advertisements various Verizon for 's services, including the Internet, cable TV, and phone.
- Show other advertisements for products found in video content. The demo shows the performance of our advertising format for both Verizon plans and products available from various U.S. marketplaces. Desktop version Mobile version Demonstration of Technology on The TechCrunch Platform (In-Image Ads on Website) For this demonstration, the following tasks were set: - Show advertisements various Verizon for 's services, including the Internet, cable TV, and phone. 
- Show advertisements for other products found in the content. The demo shows the performance of our advertising format for Verizon plans only. The demo shows the performance of our advertising format for Verizon and other advertisers. Demonstration of Technology on The AOL Platform (In-Video Ads on Website) For this demonstration, the following tasks were set: - Show advertisements various Verizon for 's services, including the Internet, cable TV, and phone. 
- Media advertising for other advertisers.
- Show advertisements for other products found in the content. Desktop version Mobile version All advertising goals and various display advertising options can be combined and customized based on the . specification Brief Information About Settings Our ad format has a flexible system of settings that enable publishers to fine-tune their advertisements. 1. Platforms Mobile Desktop 2. Ad Placement Area.  The setting enables publishers to choose if they want to embed ads all over the entire site, on an individual page only, or even in a particular area of that page. There are two ways of how ad slots can be created: Ad slots that are generated by our system automatically. Ad slots that are hand-picked by publishers. 3. Content for Ad Placement. For ad placements, we use visual (graphic) content that our system detects on sites. After content analysis, AI selects the most relevant ads to show in it. The following formats are supported to use for embedding our ads: Images: jpeg, jpg, png, webp, gif Videos 4. Context for Ad Placement (IAB Standard). Along with graphic content, our system analyzes any accompanying textual content. This additional analysis is performed to help AI to make the rightest selection for ad display. 5. Placement Position. The setting enables publishers to choose the position of content (in which ads will be embedded) on the site.  It relates to visibility/significance. Here is a list of ad placement positions to choose from: above the fold, below the fold, header, footer. 6. Ad Placement Density. The setting enables publishers to choose the frequency of ads to be embedded: For images, publishers can set the frequency of ad displays per page. For videos,  publishers can set the frequency of ad displays per video stream. 7. The Number of Ad Placements. The setting enables publishers to set the number for simultaneous ad placements in the content. For images,  publishers can set the number of ads to embed per one image. For videos,  publishers can set the number of ads to embed per one video frame. 8. The Order for Ad Placements. The setting sets the rules and describes specific placement parameters for advertisements on the platform as a whole, its part, or its content. 9. Ad Placement Categorization. The setting specifies what categories of Google Product Taxonomy as well as brands can be advertised and what advertising purposes can be executed on the platform as a whole, its part, or its content. Advertising Purposes Categories Brands 10. Ad Placement Classes. The setting enables publishers to choose specific classes of objects among detected ones to use for ad display on the platform as a whole, its part, or its content. 11. User Data Parameters. The setting allows publishers to choose the characteristics of users to target for ad displays. Browser Data Device User Data (IAB):Interests &Purchases 12. Types of Ad Format. We developed various ad formats to be placed straight into the graphic content. The setting allows publishers to choose which ad formats to use for display. Here is a list of currently available ones: #MoneyDot #MoneyBox #MoneyLens Abar classic Vbar 13. Display Methods. The setting allows publishers to choose various ways to display ads: Animation Elements Interaction Dimensions Display Logic 14. Other Settings Is there a need to implement more "meanings" into technology? Is there a need to implement more "meanings" into technology? If you read to the end of this article, you are a rock, and we share the same blood! :) Join us, invest in us, do business with us.

Our Quest To Solve Inefficient Web Traffic Monetization

Wikipedia characterizes ADHD symptoms as "difficulty paying attention, excessive activity, and acting without regards to consequences." [1] As we all live in times when not only new generations, but older ones are prone to the syndrome, how to promote and make money on online advertising? How to gain consumer attention with ad blindness on the rise? How to respond to this consumer trend that negatively affects every online business? Hi everyone! For those of you who don't know me already, I'm Pavel Shkliaev, a CEO & Founder of LensAI. LensAI is a tech startup that offers a business-to-business service to monetize both image and video content. The LensAI Technology allows our Publishing Partners to earn money for ad impressions that are displayed in an innovative format using PiP (picture-in-picture) placement. If you have not read my previous articles about LensAI, here are the links for your convenience: How We Taught Artificial Intelligence to Sell Where Can Dreams Take You? The Art of Teaching Associative Thinking to Machines The Fight for Attention: The Most Recent LensAI Technology Results I hasten to share the results of LensAI Technology that we've got through our combat testing. I call it "combat" as we did it without including the last module, which focuses on the similarity and relevance of the products. (We are currently testing it and optimizing products from the most significant marketplaces such as Amazon, eBay, Walmart, etc.) The videos below show how LensAI Technology analyzes graphic content on TechCrunch and Mashable sites. Notice how the new "money dot" advertising format is placed straight into detected objects. Both videos are fresh from the oven. The first video shows the processing of images and placement advertising dots in images, and the second one shows the processing of video and placing of advertising dots in it. Here’s a quick recap of how LensAI technology works. Firstly, it identifies an object, and then it moves to identify either a product category or an advertising category if the object is not associated with e-commerce. After that, it identifies a specific product that is available among various marketplaces. It might look as simple as one-two-three to you, but a whole bunch of analyses takes place, starting from the context analysis and ending with the analysis of the scene in the frame/image. Also, the influence each piece has over each other piece is incredible — what a thick layer of AI we've built. #proudofmyteam Finally, we are approaching the moment of transition from technology to product and first . investments Money Dot: As You Name the Boat, So Shall It Float In developing LensAI ad format and testing it among our followers, we decided to stick with the "Money Dot" name for it. We came across it in the dialog with one of our followers. Here's how he convinced us: The end-user who clicks on the ad is not interested in, how you name your advertising format. Also, as Advertisers are not likely to use LensAI Technology to sell their products directly, the name "Sales Dot" is not quite suitable. I see that LensAI Technology's primary users include Affiliate Networks, Content Delivery Networks, Webmasters, and Arbitrageurs. They are the ones that will sell those products for Advertisers through their sites, and they care about two things mostly. The first one is how much money your advertising dots will help them to earn. The second one is whether, and how, the advertising dots will affect the quality of their sites. Therefore, go with "Money Dot Join Us. If You Do Not, Won't Somebody Else? If you recognize yourself within these lines, you are welcomed to join the closed beta testing group at LensAI benefits its Publishing Partners by: Producthunt placing ads in previously unused places that have the highest audience response, selecting advertising based on both content analysis and analysis of objects found in that content, based on similarity and relevance. Also, all early joiners will get the most out of it. Why? Because nothing catches the eye better than something you have never seen before. It is a well-known fact that Advertisers want to get a higher return on investment. For this reason, LensAI Technology allows Advertisers to choose not only the site where its products will be displayed, but also the specific type of content and objects in the selected content. That is to say the likelihood of purchasing those products, by users who view the content, rises dramatically. Publishers, in turn, get an innovative way to monetize the content that they have not monetized before. I want to stress that by using LensAI Technology, Publishers do not lose their existing earnings by using other types of online advertising. LensAI "Money Dots" do not occupy any existing ad slots and can co-exist with other types of ads. It's time to wrap up. Don't hesitate to and follow us. As we have moved to the stage of optimizations and technology improvements, it's time for us to attract the attention of end-users. I and thank you for your interest. subscribe to our updates LensAI Team Works Cited Wikipedia contributors. "Attention deficit hyperactivity disorder." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 14 Jun. 2020. Web. 15 Jun. 2020.

Online Advertising in The Time of The Consumer Attention Deficit Hyperactivity Syndrome (ADHD)

The idea of LensAI was born last year sometime in May. If you've missed Part I of my story, you can read it here -> . How We Taught Artificial Intelligence to Sell At the early stage of our product development, we were passionate about it and full of energy. We desired to change the world now and forever. We felt that we were the innovators the industry had never seen, and massive changes would come with us! We wanted to fly, but the experience we gained from previous startups kept us pulled to the ground. "You are dreaming again. Stop it! It is time to build a product that can make you money, preferably quickly! Enough thinking about changing the world, at least until you see the first AI-earned $1k in your pocket," my mind kept repeating to me all the time. After negotiating with my inner-critics and my partner, we decided to press the stop button. A well-known and proven business model was chosen for our startup. Since that moment, there have been no more thoughts about the integration of our technology into lenses and glasses. We shifted our focus to recognizing objects and selling ads. Competitors as a Source of Inspiration We were doing our research on competitors, but our social media followers were a great help too as they were continually sending us tons of information on various companies that were somehow engaged in the object recognition in images or videos. The Cutting Board Being the restless minds, Konstantin and I were tormented by endless doubts. We had so many unanswered questions. "Why all these competing companies are worth millions and hundreds of millions of dollars, but there are no unicorns among them? Why are their technologies so rare to come across on the Internet? Why? Why? Why?" I was asking myself so often that it drove me nuts. To proceed further with our idea, we had to find the answers. Using "visual search" and "advertising" as the keywords, we were able to find the companies that were more or less related to our idea. We gather all of them on our "cutting board," took out a "knife," and began the messy examining process. Through analyzing companies one after the other, we noticed two major similarities: limited advertising inventory and limited ad displays. All those projects were sharpened to work with a narrow circle of products. More than that, some of them worked only within a single category of products or one brand. Also, to display an advertisement, the identical match of the product had to be available. This prerequisite seemed too strict for us. Many projects had limitations due to the business model they used. Either they were B2B (business-to-business) and very complex or B2C (business-to-consumer) and, in our opinion, not viable. For example, some projects required interaction with product placement agencies to show ads in a video that was streamed on Smart TV. All projects had the same "chicken and egg" problem in common.  It occurred due to an insufficient amount of data about products that were being sold at that moment and could be displayed in relevant visual content even though the advertiser was ready to pay to advertise these products. Our "cutting board" helped us to identify three main problems that we needed to solve more efficiently than our competitors if we wanted to succeed: The ads of products/services had to be hyper-relevant to the detected objects of the visual content regardless of the date when the content was first released. We had to place our ads straight into detected objects and show them in a way that ad placement decisions would not depend strictly on similarity algorithms. Simultaneously, we didn’t want the user to question our ad placements or ad choices. We had to provide a machine with a broad and relevant advertising inventory that we would have to receive regularly. This way, the machine could apply various display advertising algorithms. Eureka! We had to make the machine to "think" exactly like a person in the moment of browsing. For example: "I want this red bag. It goes perfect with my shoes!" "What a cute shirt this guy is wearing, I know that a men's watch would match it perfectly!" "Great hotel, I should book it for my next vacation!" As we know, a human brain thinking process is hard to duplicate by the computer due to its emotional inability. But we decided to give it a try and train the machine in associative decision-making. Teaching Associative Thinking to Machine Our first try to formulate all the requirements and link relationships between detectable objects, entities, people, and other characteristics ended up looking like some unknown monster that demanded us to analyze all possible and impossible data. Through multiple tests and pain in the brain, we were able to collect all the data we possessed that could be analyzed.  The following picture cleared up: We systematized the entire "zoo" of data into an influence map that navigated how every entity influenced every other entity in that particular scene. Let me show you how it worked within a frame. For example, once "Environment" was identified as "Kitchen," the influence of objects from such advertising categories as "Kitchen Furniture," "Kitchen Appliance," "Spoons," "Kitchen Care Products," "Cups," "Tableware," and others, was increased. As a result, we built an influence map, a version of which you can see below.  (We distorted it on purpose as it includes parameters of the algorithm that are of value to us.) In this image, you can see what entities we operate and how all of them influence each other while the process of analyzing images and context is running to display the most relevant advertisement. The task was successfully defined: determine the relationships of objects and entities to advertising categories according to characterizing features. Once accomplished, the next one was to establish a connection between two entities: a category and an object. Object relationships are ways of one object to have an obvious connection with some Taxonomy category. In other words, each object relation can be described as some influence of the object on the products from Taxonomy. Meaning, when we see an object in the image, we can assume that this object is a central one, and we need to find relevant to this object products according to some characterizing feature. When working on the task, it was necessary to apply the following relationships (characterizing features): Taxonomy products are often located on the object, Taxonomy products are often used for an object, Taxonomy products cannot be used without an object, Taxonomy products are used with the object. We needed two elements to work with large volumes of data and various types of relationships between entities, they were: The Tool people with arms and heads, a tool that would help these people to transfer their knowledge to a machine, in our case, the particular knowledge to identify relationships between objects. Guess which one was much harder to find? (Spoiler Alert! It was the first one.) We searched all over the Internet but could not find a tool that would satisfy our requirements. We had no choice but to create our own. We brought together the capabilities of working with massive amounts of data and types of connections. Notice that each possible connection had to have its own weight of influence. Also, all these connections had to be established by various people who could catch machine-made errors and then correct them. As a result, we got a two-panel data manager that had a broad set of functions and the ability to work with both relational and graph databases. Handwork: Hell Exists This stage of product development was the one that almost burnt us out. Neither Konstantin nor I experienced the euphoria we had at the beginning of the project. It seemed to us that to process and link such volume of data is beyond the bounds of possibility. Our mission to determine the relationships between the objects was about to fail. To feel our pain, imagine a few of the processes we had to deal with, such as linking detectable objects to advertising categories, collecting all possible variations of detectable objects, connecting both industries and context themes with advertising categories... We didn't count all these processes, but there were hundreds of thousands of them. It felt like HELL. We knew it was time to delegate, so two data since groups of five people were created, both had the only purpose of linking all entities together. Here is the picture we got. You can see the relationships of various entities in the images above. The last one shows a "scene," aka the influence of multiple entities within a single image. We were able to achieve the scenario where the detected in the frame objects always fight for the right to be displayed, and the algorithm's job is to determine which ad to display in each frame. For example, the final decision on which one of three advertisements to show (a teapot, a coffee maker, or a furniture store brand) is totally on the algorithm's shoulders. Nowadays, you cannot surprise anyone with object recognition software, but with the high accuracy of recognition, you can. The next step was to identify how accurately our models worked and give Fs to machines if they were wrong. In parallel with this work, we also manually tested some of the relationships that we established when we were completing the task to determine relationships between entities. As a result, we got a whole bunch of classification errors that were done by machine algorithms. We found them in thousands of images and video frames of movies. To correct the errors, both our data since groups had to mark them manually and then classify them one by one. There were nearly thirty different classes of errors. I will list some of the largest ones below. Please know that each of these error classes includes up to ten subclasses. Here is a list of errors: Wrong Class Wrong Product Wrong Taxonomy For Classes Without Coordinates: Wrong Surrounding We continue to work and are getting closer to our goal. The next step is to release a public demo so that you can evaluate our work or point out our shortcomings that require a fix. Continue to follow LensAI and join us on . PH We promise to delight you with quality content, update on the progress of our project and various entertaining stories about machine learning. LensAI is an Affiliate Marketing Platform that automatically delivers the right ads in the right place at the right time! 🎯 We are building an ad format that turns visual content into a seamless shopping experience — no more gap between browsing and sale. And yes, we know how to catch a fleeting glance of a customer! 😉 LensAI is about to bring some changes to digital advertising. Be the first one to witness them! 👀 -> https://www.producthunt.com/upcoming/lensai

Where Can Dreams Take You? 
The Art of Teaching Associative Thinking to Machines

Introduction I am Pavel Shkliaev, a co-founder of , and this is my story. LensAI After six months of work on the previous project, I finally got a free week or two. I was finally able to unload my brain, but my irresistible craving to create was too intense to ignore. It's been a while since thoughts about the world's overproduction of goods and how it created the problem to sell rather than produce occupied my mind. I kept thinking of how Instagram turned into a vast marketplace, where every homemaker sells another luxury wrinkle cream. It doesn't take a brain surgeon to figure out that all these led to such an oversupply of information that an average consumer can't focus on one specific thing for a long time, forget about focusing on the text. Even though I always enjoy the process of solving the problems, it felt like I had enough of it. Anyway, a solution to specific issues is a niche market, and I was ready to solve universal truths. It was time to shift my focus to creating new opportunities. The Birth of An Idea It all started with a sofa. Precisely, with me sitting on that sofa and clicking remote control. After changing over two hundred channels, surprisingly, I reached the Shopping Channel. First of all, I was shocked it existed. Secondly, I was more shocked to realize it existed because someone still buys such crap as super-duper knives for $50 , questionable diamonds, and magic steamers. Channel switching continued until the "Casino Royale" movie with Daniel Craig caught my attention. While watching, I could not stop wondering why would they not sell items that they show in the films? I liked the suit James Bond was wearing in that scene, and if given a chance right now, I would probably place an order for it. Although I did not need the suit, I liked how it suited the actor and just an idea of having one gave me a good feeling. As I proved to myself the known truth that the purchase of consumer goods is often a spontaneous event, I began to grow this idea in my mind. The first question arose: what if the concept of the shopping channel would be implemented right into the movie giving me, a potential consumer, an opportunity to buy things that were shown in that same movie? And then, the second question followed: what if I could instantly buy all the things that I see in the digital space without spending time searching for them or remembering the exact source where I saw them? The idea tempted me. I did not think about AI and computer vision at that time, but some visuals began to emerge in my head. I kept imagining some object selection tool that would enable me to pick a product from the movie and buy it straight from that movie in real-time. The idea's global nature and potential caused me sleepless nights. I started to gather information and research other similar projects. It looked promising. Once I finished the online research part, it became evident that similar ideas and projects exist in one way or another. However, all of them are niche projects that use computer vision to solve only one specific problem, such as video search for weapons, smartphone applications for searching for particular goods, etc. While researching, I crossed only one American project that was based on a similar idea and had a similar implementation, but it specifically concentrated on media advertising and stopped there. Undoubtedly, Internet giants such as Google, MS, and Amazon were ahead of all. I had to consider new inputs to make sure my idea could work, but everything else was pretty stable. During the development process, the main points of my idea had to shift more than once since I knew how essential it was to enter the market as quickly as possible. "Hi, Konstantin! I have an idea!" I called the partner from the previous projects. Before making a call, knowing Konstantin and his tiredness from the cut-and-dried work, I knew for a fact that he would get involved as it was not another application for Apple but a damn artificial intelligence and machine learning! You know, some good food for an analytical mind. "Yes, you've got some interesting idea, but I have to check the technical feasibility before giving you an answer. I will need a week," Konstantin replied. That's when the technical feasibility study began.  We quickly concluded that the project was technically feasible, and we expected no unusual restrictions. The one thing we didn't foresee is a crazy amount of manual work that we disliked the most but teaching associative thinking to the machine was too tempting. It took us three weeks to create a prototype, and that's what we got: The prototype was able to identify both key objects and positions of them and calculate the percent accuracy of the object detection algorithm. We moved to the brainstorming the ways to develop the project. Among all unbridled creativity and non-stop idea-generation, we highlighted four main components: Development of an advertising platform for Publishers and Webmasters to join. A computer vision technology that processes both images and videos found on the website and then, depending on the detected objects and other data obtained through the analysis, automatically embeds advertising straight into visual content. A pay that comes from Advertisers for the traffic that such advertisements bring. A commission that we take through our platform. The output should be: A user clicks on an advertisement and finds himself/herself on the website of the store where that particular product is available for sale. Yes, everything is that simple. The technical implementation was a fascinating process that generated lots of obstacles that we overcame during the development stage. We wrote another post regarding some of them but be aware of a lot of incomprehensible words we used to write it. "Hello, Max! We are ending the work on the "Bread Card," I called my guy to bring some news that it was time to put our previous project on the shelf. "Oh, why? Damn, so much work has been done on it," Max sounded upset. "Yeah, no one pays for it... Anyway, I've got a better idea, and it is far away from being as banal as this "Bread Card," I cheered Max up. I knew Max well, so his immediate desire to engage was not a surprise… My team of engineers has been building software for the past ten months that hopefully, will bring a change to the digital advertising industry in the nearest future. I am looking for some feedback on using AI-powered ads that would turn the visual content of any website into a shoppable one. Last summer, I got that crazy idea to create a tool that would allow users to shop items that they see in the video/image straight from that video/image. , that's how I named it, is B2B software that would analyze any visual content found on your website and then automatically deliver hyper-relevant ads of the products that were detected and available for sale on leading marketplaces like Amazon, Walmart, eBay, etc. Also, I choose the design of the ads to be as native as possible, and the ads would look more like helpful hints rather than irrelevant ads. What do you think about the idea in general? Can you see yourself using it if it doesn't cost you anything but unlocks new income for you? has already joined the first round of various Affiliate Programs, and we would share generated revenue from those programs with our Publishing Partners/Websites, so it's a win-win. We are making our baby steps to bring some innovative changes to the digital advertising industry. Here's what we got for you! We worked hard 🤓(from home, of course 😉) and finally finished Slide #2 on our website at Yay! 💥Make sure to check it out to see how LensAI Advertising Technology works on the famous "Diana" movie trailer. 🎥 Grab popcorn! 🍿 OR Today LensAI LensAI https://lens-ai.com Subscribe on PH: https://www.producthunt.com/upcoming/lensai Stay tuned for more cool AI-Powered stuff to come!

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