Stas Tushinskiy is the CEO and co-founder of Instreamatic, a platform that provides AI-powered voice and audio marketing solutions to enable brands to raised engage with consumers.
You previously Co-founded Unisound, an audio ad agency. How did this experience lead you to conceptualize launching an AI voice marketing company?
My experience at Unisound was foundational in understanding the evolving landscape of digital audio promoting. We were on the forefront of recognizing the growing demand and potential for audio ads in a digital space.
A key takeaway from my time there was the belief that personalization and contextualization significantly enhance the effectiveness of promoting, including audio ads. This understanding became a cornerstone for the vision behind Instreamatic.
At Unisound, we observed a spot available in the market for intelligent, responsive promoting solutions. We envisioned using AI not only for targeting but in addition for making a more interactive and interesting experience. This led to the thought of an AI-driven marketing platform, which might revolutionize how we interact with ads.
Could you share the genesis story of launching Instreamatic?
Originally, Instreamatic was born from a vision to rework how audio publishers monetize their content. Initially, our focus was on serving audio ads for monetization, which stays a major a part of our operations.
As we delved deeper into the industry, we identified a considerable opportunity in AI for creative optimization. This realization was pivotal in shaping our direction toward integrating AI technology more deeply into our services.
The convergence of our expertise in audio promoting and the advancements in AI technology was the catalyst for Instreamatic. We saw the potential to not only serve publishers but in addition to reinforce the general ad experience for users and advertisers alike, paving the way in which for a more dynamic and efficient promoting ecosystem.
What were a number of the initial AI/ML technologies that were used?
We began with a straightforward classifier. It’s a supervised machine learning method where the model tries to predict the proper label of given input data. Then, we enhanced our classifier through the use of embeddings. Eventually, we didn’t limit ourselves to only NLP technologies. Latest ideas and challenges presented us with fresh obstacles and, now, our arsenal includes text-to-speech synthesis and zero-shot voice cloning.
How has generative AI modified your technology stack and the way do you deploy it?
Generative AI has brought significant changes to each our technology stack and deployment strategies. Our current technology stack includes advanced machine learning libraries and frameworks that support generative AI models, particularly for text-to-speech synthesis and zero-shot voice cloning. We utilize high-performance computing resources to coach these models, as they require substantial computational power. This involves leveraging GPU-accelerated hardware to handle the intensive processing demands.
For deployment, we rely heavily on cloud-based solutions. This offers us the scalability needed to administer the heavy workloads of generative AI applications. We use containerization technologies like Docker and orchestration tools like Kubernetes to administer and scale our applications efficiently. This setup ensures that our generative AI models could be deployed rapidly and scaled in line with demand.
Our CI/CD pipelines are optimized for machine learning workflows. We use tools that enable us to automate the training and deployment of models, ensuring that they’re at all times updated with the most recent data and algorithms. This automation is crucial for maintaining the efficacy of our generative AI applications.
By way of data handling, we’ve implemented robust data processing pipelines. These pipelines are designed to handle large volumes of knowledge efficiently, which is crucial for training and operating generative AI models. We make sure that data is processed and stored securely, adhering to best practices in data security and privacy.
Overall, the mixing of generative AI into our technology stack has led us to adopt high-performance computing resources, cloud-based infrastructures, containerization for scalability, automated CI/CD pipelines for machine learning, and secure data processing mechanisms. These technical elements are fundamental to supporting the advanced capabilities of our generative AI applications.
Instreamatic makes a speciality of what you call contextual video and audio promoting—how do you define that?
Contextual Promoting taps into current advances inside generative AI to significantly alter what’s possible with video and audio ads. The result for businesses is increased brand engagement and ROI. Contextual Ads offer an essentially unlimited capability to repeatedly generate and A/B test latest creative content relevant to the listener’s specific context and environment.
The actual fact is the promoting industry has been up against declining engagement rates across ad types for years. That’s probably no surprise to anyone, as consumers show increased screen fatigue and resistance to generalized promoting that relies on bombarding audiences with ad quantity to earn conversions. While ads that show more specific original content and better relevance to the patron earn higher engagement, the time and price investments required to manually produce and manage separate ad copy for every individual consumer’s context are extremely prohibitive.
Our contextual audio, video, and connected TV (CTV) ads are powered by AI to buck this low-engagement trend by enabling advertisers to make every ad hyper-relevant and precisely targeted to the patron hearing it. Consider a standard 30-second audio ad spot: a hired voice actor might record a number of ad copy variations at most, not enough for the listener to be particularly surprised, or to necessarily capture their attention. Contextual Ads are able to enhancing that traditional ad content, using generative AI to synthesize the identical actor’s voice and mechanically generate hundreds of ad variations across a campaign.
Contextual Ads are especially useful for revitalizing longer ad campaigns (within the 3-6 month range). Traditionally, these campaigns are very vulnerable to creative fatigue: audiences get the identical creative time and again, inevitably resulting in decreased engagement. Our technology solves this challenge by making it easy to refresh creatives weekly. For retailers with weekly-updated product offers, for instance, our automatic ad generation is similarly ideal for keeping those campaigns current and fresh.
How realistic is it for brands to expect AI to hyper-personalize ads?
It’s now fully realistic, as demonstrated by generative-AI-powered contextual promoting. Contextual Ads can feature hyper-personalized details, including the listener’s location, the time of day, the name or kind of app or platform they’re using, and the activity they’re engaging in, whether it’s listening to a podcast, playing a game, etc. Contextual Ads may even include variables corresponding to naming local storefronts and addresses, local in-store promotions, promo codes (unique to every channel to enable performance measurement), travel destinations with specific offers, and rather more. These ads may also name the closest local storefront where a listener can interact with the brand and redeem the deal offered within the ad. This same targeting capability ensures that ad campaigns reach vetted audiences which might be most receptive to the products and solutions being offered. These ads are all generated and delivered without recording latest voice or voice-over content.
Are you able to discuss the core offerings that your customers have access to?
From a brand perspective, our Contextual Ads platform takes a single original voice sample and script, identifies the set of parameters unique to every individual listener, and uses our voice AI capabilities to seamlessly produce and serve audio, video, or CTV ads aligned with those specifics. For instance, a Contextual Ad generated for a specific user could begin, “Hope you’re having fun with your podcast on this rainy morning in Chicago, I just desired to quickly let you already know that coffee is buy-one-get-one-free at Jake’s Coffeeshop all month.” Whereas producing that very same ad creative with prerecorded audio and branching logic could be an all-but-impossible undertaking, the voice AI behind Contextual Ads prepares this creative on-demand—mechanically and in real-time.
From a publisher’s perspective, AI-driven voice, video, and CTV Contextual Ads offer a game-changing innovation with no complex integration required. Contextual Ads work with all demand-side platforms (DSPs) and ad servers supporting VAST tags, offering fast scalability. Publishers may also leverage our ad network to achieve greater than 6 billion impressions globally at no platform cost: technical costs are included in media spend when publishing inside network.
Could you share some details on the technique of launching an ad on the platform?
Launching an ad on our platform literally takes just minutes. The brand or agency user simply writes ad copy with or without help from AI, then either chooses a royalty-free voice from our Voice Library or clones their very own voice talent. Users may also upload any additional assets vital (background music, video footage, banners, etc). The user finalizes the ad, and the platform provides versions able to serve—either via the VAST tag (the industry standard for ad trafficking), or as downloadable media files able to go for any digital and broadcasting environments.
These AI-enriched ads not only increase the performance of video and audio ad campaigns by enabling hyper-personalization at scale, but in addition slash the fee to supply campaigns and reduce ad creation time from weeks to minutes. For campaigns with 50+ versions, users experience a ~10X cost decrease. Our technology offers similarly decisive advantages for single-creative campaigns as well. The platform can be an awesome instrument for sales teams to quickly produce ad mock-ups for his or her clients without engaging with production and artistic teams at an early stage, since our AI can write copy and fully produce custom ads.
What’s your vision for the longer term of AI promoting and marketing?
I actually do see a future where customers aren’t annoyed at (or tuning out) ads because each is now relevant and more interesting to them, with brands are that rather more able to reaching the suitable audiences at the suitable moments with the proper contextual message. That’s obviously a sea change from where the industry is now, but I do consider that’s where we’re headed—and AI, leveraged strategically, is making it possible. Contextual Ads are also going to repeatedly recuperate at capturing listeners’ attention because they speak precisely to their context and their needs, especially within the privacy-first world where user targeting gets harder and harder—so context targeting is the one efficient mechanism for reinforcing ad performance. Our advanced generative ad AI can create unlimited latest creatives to handle each listener as a person. The result’s an uplift in listener engagement, greater ad ROI, and more meaningful customer connections for brands.