At Media Distillery we have been working on a solution we’ve dubbed Snackable Content. Using technologies like speech recognition, face recognition, and topic recognition, we can automatically create short clips based on a consumer’s favourite person, topic, or interest from TV content. With this solution, TV operators can present bite-sized content extracted from long-form video content. In this article, we discuss in-depth one of these technologies and how it contributes to our innovative Snackable Content solution.

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For the main use-case of Snackable Content we look for interesting topic clips in TV content. In this process, we decide which topics to…


The year was 1941 when the age of commercial television began. It was in that same year that one New York area TV station — WNBT — had the foresight to print the first TV guide, known as a “Program Card,” to distribute to local TV owners. …


“Phoebe Waller-Bridge Inks Overall Deal at Amazon”

“Quibi will launch with 50 shows on April 6”

“NBCUniversal announces details of new streaming service Peacock”

It seems like every week we read headlines like these. Time and time again we are reminded that VOD platforms are gaining strength as key players in the entertainment industry.

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To stay competitive and secure their position in the trenches of the streaming wars, online services (and all those competing with them) are intensifying their efforts with content production. Over the last few years, subscribers/viewers have been flooded with programming from all over the world on…


Video topic detection is a technology with the potential to revolutionise how we understand customer behaviour. But there are serious challenges. How can we identify relevant topics from moving images? How can you deal with multiple topics in one video? How do we choose the best from the many available techniques?

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In this blog, I reflect on our first trials using speech recognition as an intermediate step to tackle some of the challenges listed above, and share what we have learned so far.

Topic detection is most notably a subfield of Natural Language Processing (NLP), but there have also been…


Accurately analysing video content requires a vast and diverse deluge of data. Here we’ll walk through how we’ve systematically structured and categorised our services, to get greater visibility and control over what we ship.

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We analyse approximately 20,000 hours of video content per day at Media Distillery. We structure our analysis service by splitting it up into [microservices], meaning there are many components involved in analysing one piece of video.

Speech, face, logo, topic recogniser services and more trivial services such as dashboards, authentication interact simultaneously to achieve a single goal: extract valuable information out of video in real-time. At…


We’re living deep in the experience economy. In 1999, Joe Pine and Joe Gilmore brought out a seminal book on experience innovation, and two decades later, they’re about to release a new edition.

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Nowhere is this ‘experience economy’ more relevant than in TV and entertainment: Netflix invented binge-watching, setting the standard for time spent digesting new content, episode after episode and for hours on end. But the shift in the experience economy today says that people won’t passively accept the experiences they’re given anymore. …


More people are watching OTT than ever, but that doesn’t mean advertisers can reach them in the right way. How can we build a richer picture of who’s watching what, to take the TV value exchange to the next level?

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The TV industry really needs to reimagine how it reaches audiences and offers service.

With 72% of SVOD subscribers seeing TV ads as ‘unacceptable’ (via Differentology), parties in the advertising chain need to start fiercely defending and differentiating their revenue streams: leaving users lost in a jungle of mindless spam and multi-screening just won’t work anymore… and neither will follow…


The ‘Netflix Effect’ is in full swing, with billions of people binge-watching programmes on-demand. But TV isn’t dead — it’s just evolving: what if broadcasters and TV service providers are missing a trick to offer new kinds of value to viewers, simply by reimagining and repackaging the content they already have?

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TV content today is a cultural zeitgeist that unfolds in real-time, to be consumed anytime. People are fed up with 5-to-12-minute unskippable ad breaks, search attempts yielding zero results, and putting up with hours-long programmes for the sake of finding the few minutes they want to watch.

But although…


How can traditional service providers apply deeper understanding to TV experiences, to generate new sources of advertising revenue?

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It’s time for TV to reinvent itself. Not only are viewers switching from Linear TV to Over The Top (OTT) video in ever-greater numbers, but consumer behaviours are changing rapidly too- a double-edged sword which will throw broadcaster and operator advertising models into jeopardy.

You’ve heard it before: streaming services have raised the bar in terms of how they’ve swept up their share of the attention economy. …


Are we on the verge of a robotic super era? Is just about every business “doing” Machine Learning in one way or another? When it comes to Machine Learning, Artificial Intelligence and Deep Learning, there are a lot of sensational tales being traded around the tech — and practically every — industry. All this speculation made me draw a parallel with Dan Ariely’s quote about big data: “Machine Learning is like teenage sex. Everybody talks about it. Only some really know how to do it. Everyone thinks everyone else is doing it. So, everyone claims they’re doing it”. …

Media Distillery

Artificial Intelligence technology to understand what’s inside video. https://www.mediadistillery.com/

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