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aioTV Blog

You have been creating and distributing content for years. You’ve had some blockbuster hits, some cult favourites, and perhaps a few that have not had their chance to shine in this new OTT world. Combined, it is your content library.

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Metadata is not new. What is new is the need for deeper and more robust metadata if you want your video content to be competitive in our multi-platform, multi-vendor consumption environment. Do you know how your content is being discovered and consumed on a daily basis? When is competitive content winning out over yours?

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Why We Switched

Our team of developers were using several different services in separate repositories, so we were faced with a few challenges when it came to our development and CI processes. We had an infrastructure repository which contained the dependencies for several of our services. This caused issues when we had to do lockstep updates for dependency changes. This problem was amplified when we were required to add breaking changes to our infrastructure repository.

Once the infrastructure changes would go in, our master branches would be in a broken state while waiting for the changes in the service repository, often causing headaches for other developers. The same issue also appeared when committing changes for our backend and frontend services that relied on each other. In addition to the impact of these lockstep changesets, we had a huge timesink with managing several different repos, creating Pull Requests for each one, and trying to reduce code duplication between them.

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Weather. It’s more than small talk. It is something that affects all of us, every day. In fact, searches for ”what’s the weather today” have grown over 160% over the past 2 years.

We want to be prepared.

We talk about it, search for it online, watch it on tv, listen to it on the radio, and read about it. We rely on those that make a living out of it so we can prepare for what is in the forecast. We want sun on our wedding day, rain on our farmer’s crops, and we need to monitor when disastrous weather is coming. Seeing weather videos from the past or in other parts of the country help us see first hand the type of weather we can expect.

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If you’re reading an article, whether it’s online or in a physical magazine, chances are you will see an ad or two placed beside it that is relevant to that article. Because you are interested in the content of the article, the ad placement makes sense, and you will likely have an interest in the content of the ad as well.

Ads are far less intrusive when they are in direct context to what is on screen at the time. That is why the impact of contextual advertising has been significantly positive for revenue generating websites.

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Putting the Context in Your Content

We produce, collect, aggregate, and adapt enormous volumes of data on a daily basis. When it comes to all of this content, how do we determine context? To solve this issue, we need to automate the extraction of contextual information from Big Data.

With video content in particular, even speech-to-text automation leaves behind errors and large gaps in the information such as which words to capitalize. Content owners spend countless hours and budget resources transcribing and correcting this messy data.

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You finally get to enjoy some downtime, so you sit down to watch some television. Looking through the ‘recommended for you’ list, you still feel like there is nothing that appeals to you. With so much video content available, why does the recommender never seem to work?

Here are the top five reasons that recommendation engines do not work:

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Are you wondering why your customers get frustrated with their viewing experience?

To curate and present the right content to your customer, you need high quality, complete metadata. At this point, high quality OTT and web metadata is virtually non existent. Even traditional metadata providers have gaps in many fields with their licensed metadata.

Missing and inaccurate metadata makes it extremely difficult for customers to find the content they want through search, discovery, recommendation and personalization. This leaves the customer with an overall underwhelming user experience.

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Customers are demanding to know more and more about what they are watching, why it was recommended to them, and how they can easily access more of what they want.


To meet customer demand, video operators are struggling to find ways to manually fix broken metadata. They are spending excessive amounts of time trying to identify gaps, access accurate content, and edit fields in an overwhelming amount of metadata.

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