Quickplay – From Disrupted to Disruptor: How Broadcasters Win the Creator Era

Andre Christensen, Co-Founder and CEO, Quickplay

Don’t be fooled. We are in the midst of the biggest opportunity ever seen for broadcasting.

You’re thinking, “how’s that possible when 2025 marks the year when…”

…more than half of Americans prefer news via social media.
…streaming has surpassed linear in total viewing hours.
…the creator economy is pulling in more ad revenue than traditional media.

Doesn’t that define a decline, not an opportunity?

Only if you dig your heels into the old ways and resist adopting a multiplatform, creator-native business model.

This article will outline tips for creating value with new approaches to distribution, measuring success and monetization.

The Creator Economy Has Renamed the Game

Broadcasters are scrambling to adapt to the powerful creator economy and are frazzled by how quickly these newcomers have rewritten the entire monetization playbook. The likes of MrBeast and Nebula were born as integrated media businesses that seize monetization opportunities beyond the subscription and ad revenues broadcasters traditionally rely on.

This new business model doesn’t put broadcasters at a disadvantage. In fact, their massive content libraries, brand equity, and production expertise are massive advantages that creators can’t easily replicate. These attributes are unmatched by modern day creators, and are institutional assurances for viewers, whether they realize it or not.

Resist Legacy Thinking: Platforms Multiply Your Audience, Not Threaten It

Millennials, Gen Z and Gen Alpha represent ~70% of the global population and are more engaged, more monetizable, and more reachable than any prior generation.

Many broadcasters focus on viewer erosion and concerns over churn. While it’s true viewers are watching content outside of your owned platforms, that doesn’t mean your audience is smaller. It’s quite the opposite: it’s multiplied. It’s just more spread out than you’re used to.

Here are three important myths to debunk immediately:

GenZ and Millennials are not willing to pay for content. Whilst they might not sign up for the exact same offerings as older generations, they actually outspend them by 1.5-3x on digital subscriptions, content, and eCommerce. This represents a huge upside potential for broadcasters willing to broaden their approach to monetization in ways aligned with the habits and preferences of these audiences.
Gen Z only watches user-generated content. They consume both professional and social content. They spend more time watching video overall than older generations, it’s just distributed differently across platforms. They are as likely to watch produced TV content as they are non-TV content (e.g., UGC, gaming live streams, shorts on social). Nearly two-thirds of the global online population consumes short-form video content on TikTok, YouTube Shorts, Instagram Reels, and other platforms each day, compared to only 47% who watch broadcast TV channels and 46% who watch streaming services, according to Ampere Analysis.
Viewer cannibalization is real. There are only upsides to embracing third-parties within your strategy. For your most loyal viewers, reaching them where they are is an effective flywheel to lead them back to your own branded platforms. And for those who might not visit your properties at all, you’ll reach them in their preferred environment with content that could entice them to explore your libraries further, driving deeper engagement and higher–value monetization.

Tap Into AI to Accelerate your Creator-led Mindset

AI enables traditional broadcasters to operate as agile content creators at scale. Broadcasters’ traditional one-size-fits-all approach has become outdated – viewers are used to hyper personalization. There’s too much content out there for a viewer to settle for blanket recommendations when they can find exactly what they want elsewhere with less effort.

AI allows for deeper awareness of viewer preferences, their habits, and demographics combined with the ability to tailor and distribute personalized content at scale . Consider these capabilities:

Automating content-to-audience flow across linear, OTT and social media. AI makes it so broadcasters can compete with the publishing velocity of the creator economy while maintaining the production quality and editorial standards that distinguish its content.
Dynamically converting one story and content piece into multiple formats, customized for viewers and their preferred viewing platform
Transforming vast content libraries that may be lying dormant into growth and engagement engines and leveraging short-form creator tools as the front porch to these longer-form pieces.
Making relevant, personalized discovery a reality and a new route to monetization

What’s Ahead

Broadcasters aren’t condemned to become commoditized content suppliers feeding the global streaming machine. The assets they have—trusted brands, professional content, local relevance, live sports and news—remain highly valuable. But those advantages can perish if new strategies aren’t considered. The broadcasters that will thrive are the ones that transform from program schedulers to platform orchestrators.

To win as the perceived underdog, view the disruption we all anticipated as your accelerant to becoming the disruptor:

Meet your viewers where they are. Gone are the days where your own branded properties are the only places your content resides. Embrace new distribution models and third-party platforms, not for syndication, but as a part of your ecosystem. Allow AI to provide the agility and speed expected today while maintaining the trust and standards your legacy embodies.
Relevance outperforms reach now. Success is measured by deeper impacts not broader touches. Viewers today embrace community, and your success must pivot to the same, shifting away from reliance on passive viewers.
Engagement must go beyond viewing to include commerce with a single data loop – from unified ads, subscriptions, bundling, and transactions – outputting insights to inform growth and value-add strategies across every screen.

Think fast – or you’ll miss the biggest opportunity broadcasters have ever seen.

Mediagenix – The Self-Optimizing Content Flywheel: Reimagining the Content Lifecycle Around Personalization

Ivan Verbesselt, Chief Strategy & Marketing Officer, Mediagenix

Personalization has long been the silver bullet for audience engagement, and the results continue to impress. Deployments of advanced recommendation engines across major video platforms consistently show how targeted relevance can transform viewer behaviour. Recent implementations with partners such as Globo, DirecTV Latin America and Sky Brasil have delivered measurable impact: in some markets, viewer engagement has increased between 20% and 60%, while actual viewing conversion has improved by more than 35%. Clear evidence that connecting the right content to the right audience drives tangible value.

Yet these successes reflect only part of what is possible. So far, most of the industry’s progress has come from optimizing the audience-facing experience at the end of the content journey, helping people find what they want to watch. The greater opportunity lies earlier in the process. What if we could inject this kind of intelligence upstream, enriching the strategic, editorial, and operational decisions that shape what content is created, promoted, or retired?

When audience data starts to inform those upstream processes, personalization evolves beyond the user interface. It becomes a deeper operational intelligence, one that connects creativity, data, and decision-making throughout the entire content lifecycle.

The Flywheel Model

Mediagenix recently published the whitepaper Engagement & Conversion Hacking: Reimagining the Content Lifecycle Around Personalization, which explores this very concept. At its core is the Self-Optimizing Content Monetization Flywheel: a dynamic feedback loop where intelligence from engagement continuously improves upstream decisions on what to promote, reuse, or retire.

By feeding audience insights back into earlier stages of the lifecycle, this model enables a process of continuous optimization. Engagement data not only enhances recommendation systems but also informs strategy, curation, and scheduling. Each turn of the flywheel builds on the last — a self-reinforcing system where every decision creates new intelligence for the next.

The result is a content operation that doesn’t just deliver personalized experiences but learns from them, becoming more efficient, responsive, and profitable over time.

Intelligence That Powers Strategy and Curation

Connecting the right content to the right audience starts with knowing what resonates and why. By analyzing anonymized engagement patterns, media organizations can uncover how demographics connect with particular content types, themes, or formats. Feeding this intelligence into content strategy helps determine what to acquire, produce, or commission next. It also reveals new editorial lines and niche audience opportunities that may not have been visible before.

The same data-driven thinking can be applied to content curation, where vast catalogues often hide valuable assets that go unseen. Smart curation tools can surface semantically similar titles, align editorial intent with audience preferences, and improve catalog utilization. The effect is significant: effective personalization can increase the Effective Catalog Size by a factor of four, meaning more titles remain active, visible, and monetizable.

This intelligence also reduces manual effort, in some cases by as much as 50%, while expanding daily catalog exposure. It allows curators to work with precision, turning what was once an intuitive process into a measurable, strategic advantage.

However, realizing this potential often requires overcoming operational fragmentation, disparate title management systems, inconsistent metadata, and siloed rights information can still limit how effectively catalogs are surfaced and monetized. Consolidating these sources of truth is therefore an essential foundation for intelligent curation.

Scheduling That Learns

Once curated, content needs to reach audiences in the right place and at the right time. In a landscape of linear, FAST, and on-demand channels, automation has become an operational necessity, not only for efficiency but also for agility.

By using audience intelligence to inform scheduling, programming can adapt in real time. Underperforming titles can be replaced with stronger alternatives, or schedules can be dynamically reshaped to serve specific demographics at specific times. This turns scheduling into a living system that continuously evolves alongside audience behavior.

When data flows seamlessly between scheduling, curation, and strategy, every function informs the next. Engagement data improves curation, curation insights guide strategy, and strategy defines new creative directions. Over time, this creates the flywheel effect: a cycle of learning that compounds operational intelligence with every turn.

The Human Element

The Self-Optimizing Content Flywheel is not about replacing human creativity. Great content still depends on intuition, risk-taking, and originality. What intelligence can do is amplify those instincts with evidence, helping creative and strategic teams make better decisions faster.

By uniting data-driven insight with creative vision, media organizations can achieve a balance between imagination and precision. They can test ideas, measure outcomes, and build stronger connections between editorial purpose and audience response. In this way, intelligence becomes an enabler rather than a constraint on creativity.

As audiences continue to fragment and content costs rise, success will depend on how effectively organizations can harness data to learn faster and act smarter. The companies that thrive will be those that transform personalization from a consumer-facing function into an organization-wide intelligence framework, one that continuously improves engagement, retention, and monetization. The future of media is not just personalized. It is self-optimizing.

Media Distillery – Content Discovery is Broken. How are Streaming Services Using Video to Fix It?

Martin Prins, Head of Product, Media Distillery

As 2025 draws to a close, the streaming industry faces a content discovery crisis. Competition for attention is fierce, content libraries run deep, and price-sensitive viewers jump between services faster than ever. At the center of this dynamic lies an enduring challenge: how to help people find something to watch quickly, intuitively, and enjoyably. In this article, I’ll discuss what’s causing these challenges, how video previews can help viewers discover content more quickly, and how they are used across the industry.

Key Trends and Research Insights

Recent reports highlight just how fragmented and competitive the video ecosystem has become:

Viewers now juggle an average of 11 video services in North America (7 paid, 3.8 free).¹ Globally, the average number of paid subscriptions has risen to 3.1 in 2025, up from 2.4 in 2023.³
Viewers are becoming serial churners, with only 40% keeping a new subscription for more than a year.¹
Nearly three-quarters of viewers use multiple video apps in a single viewing session¹, and the average time spent browsing for something to watch has climbed to 14 minutes —up from 12.5 in 2023².
Almost one in five consumers gives up – abandoning their quest for content altogether after browsing. For 18-24-year-olds, this number goes up to 29%.²
Short-form consumption is on the rise. 85% of people in the Netherlands watch short-form.⁴ In the US, more than 30% of short-form content is consumed on the big screen.¹
SVOD pricing is up across the board, sometimes by 20%, increasingly driving cancellations³. In the Netherlands, 44% of those who cancelled a service last year did so because the service was simply too expensive⁴.

Sources: 1) Tivo Video Trend Report 2025, 2) Gracenote State of Play 2025, 3) Simon Kucher The Global Streaming Study 2025. 4) GfK’s Trends in Digitale Media 2025

Too Much Choice, Too Little Time – The Dark Side of Discovery

As consumers, we’re faced with a paradox: there is so much great content, but it takes more time than ever to find something to watch, so we frequently just give up.
To paraphrase a particular Star Wars character: overwhelming choice leads to decision fatigue; decision fatigue leads to frustration, and frustration leads to churn.

So what can be done about it?

Help Viewers Discover Video With Video
Not that long ago, discovery for TV programs was primarily driven by textual Electronic Program Guides. Now, interfaces are rich with imagery: posters, stills, backdrops, and thumbnails. But even striking visuals have their limits. The new frontier is video previews, which give viewers a deeper understanding of the content. Previews come in many forms: trailers, promos, highlights (single scenes extracted from the video), and shorts.

Shorts, or short-form video, is a trend no platform can afford to ignore, one with dual roles in consumption and discovery. YouTube Shorts viewership grew 20% in Q1 2025 to over 200 billion daily views.⁵ In many countries, Shorts revenue per watched hour matches core YouTube usage. But interestingly, shorts also serve as a content discovery tool themselves. Already in 2022, YouTube’s Culture & Trends report showed that 59% of Gen Z use short-form video apps to discover longer content they end up watching. I expect to see content providers increasingly focus on repurposing content for short-form consumption and content discovery.

Source: https://blog.youtube/news-and-events/neal-mohan-cannes-2025/

Surveying Streaming Services: How Video Previews Are Used
So how exactly do streaming platforms employ video previews? As a product person and streaming enthusiast, I set out to survey how global and local video services use video previews on their home pages or home screens, as this is the starting point for content discovery. Since most services use a row-based layout, I also checked whether previews were present on all rows. This overview is a snapshot, not an exhaustive study; experiences may vary depending on time, region, or device, but I think it’s illuminating.

Among the global streaming leaders, Netflix combines trailers, promos, and highlight clips in a video-first home experience for most content. They play either in the top banner or thumbnail window, with durations ranging from 50 seconds to several minutes. Among global SVODs, it represents the clearest shift toward video-led discovery. Amazon Prime Video blends promotional clips for originals with highlights for selected content, with clip lengths ranging from 15s to several minutes. HBO Max follows a similar model for originals (promos and trailers of 35-45s), but only shows static thumbnails for most assets. SkyShowtime shows promos/trailers and video previews on the front page, with lengths ranging from 65 to 145s.

As the biggest online Video Platform, YouTube plays videos inside the thumbnail window on hover/select. It plays the video from the start until about 30-45 seconds (excluding the Shorts section).

The Regional Perspective on Video Previews

When looking at some local European players, I found the following: Norwegian broadcaster VGTV uses a compilation of 1-second excerpts stitched into a 10-second clip, which they show in the top row. Other rows only show thumbnails. VTM Go in Belgium shows a single promo preview video for one of its programs —a 30-second compilation of short clips. Similarly, German / French service Arte sometimes shows a 30-second trailer for a documentary in the top row.

On the Pay TV Aggregator side, there’s one example I’ve worked on myself: Switzerland-based Sunrise, a Media Distillery customer, offers a combination of promos, trailers, and highlights in its “TV Highlights” feature, shown in the top row and a dedicated row. For their catch-up/replay content, our solution generates spoiler-free video previews from live broadcasts for 50 channels across four languages. These preview clips are 15 to 20 seconds long.

Towards Video-Led Discovery

To summarize, services vary widely in preview type, content coverage, and clip length. While the latter may be a preference, the lack of coverage stems from the limited availability of preview assets. Advances in AI now enable scalable, automated video preview creation, reducing reliance on manually produced promos and trailers. It means more services can now provide previews for large parts of their catalogs.

As we move into 2026, video-led discovery will likely become standard practice, reshaping how audiences find and connect with content, making browsing faster and engagement more meaningful. It’s sure to help viewers worldwide discover and enjoy great content in less time.

Screenshot from the Sunrise TV Highlights feature, playing autogenerated video previews

CaptionHub – The shift from hardware to cloud native audience growth

Kirsty McGowan – CaptionHub Director of Marketing

Live captioning has long been a core component of any broadcast workflow.

In recent years, many control rooms have virtualized and some remain hardware based for a myriad reasons from legacy contracts, change management delays or production necessity such as outside broadcast trucks. Remote production is however fast becoming the standard. IP and cloud are everywhere. Yet captioning has often remained stuck with hardware encoders, SDI patching, on-prem boxes, and on-site specialists. We all know the cloud argument – it’s not new – you can genuinely do things faster, at lower cost and better scale. More with less, and today, not tomorrow.

For a long time, that was just accepted as the cost of doing things properly. But audience behavior and business models have moved on. Captioning hasn’t kept up, until now.

Captions as Revenue Infrastructure, Not Just Compliance

Captions used to live in the “regulatory necessity” bucket. Now they sit much closer to “audience growth” and “revenue generation”.

A few simple realities:

A huge proportion of video is watched with the sound off – especially on mobile and social. If your content isn’t captioned, you’re simply invisible in those environments.
Captions increase completion rates and watch time on streaming platforms. Viewers stay longer when they can follow along easily.
Multilingual captions open doors to markets that were previously uneconomical to serve with separate language feeds or fully localized productions.

Add accessibility legislation, platform-level expectations, and the fact that younger audiences treat captions as standard, and it becomes pretty clear: captions now sit in the critical path of content performance and monetization.

If captions drive reach and reach drives revenue, then captioning workflows can’t be a fragile bolt-on anymore. They need to be first-class citizens in the stack.

The “Missing Encoder” Wake-Up Call

This shift came into focus for us during one of the world’s most-watched sports events this year.

Picture a major tournament, broadcast globally, strict timings, and very little margin for error. The production truck arrives onsite. The team begins running through checks and realizes the captioning encoder hasn’t been loaded onto the truck.

Traditionally, that’s a nightmare scenario. The options would have looked something like this:

Ship in hardware at speed (and at cost)
Fly in or divert specialist engineers
Rewire parts of the chain to accommodate a temporary setup
Accept a high-risk, last-minute, half-tested solution

Instead, the team picked up the phone. They had recently been introduced to Timbra, the real-time localization suite from CaptionHub.

Because Timbra is fully cloud-based and AI-powered, they didn’t need to source a physical kit or re-engineer the signal path. Within hours, non-specialist staff were logged into a browser, running tests, configuring caption styles to fit their brand, uploading brand terms via custom dictionaries to increase transcription accuracy, and validating latency and sync. Translations are a welcome option, simply enabled with a checkbox for 250 more languages.

By the time coverage began, live captions were running smoothly in their stream. Accuracy was strong, timing was tight, and the team had the confidence to let it run across the full event. The feedback afterwards was straightforward: if this is what’s possible in a rush, what could be done with Timbra when it’s part of the plan from the outset?

They’re now assessing how to replace their old approach for future events.

This wasn’t just a “save the day” story. It was a clear sign that the underlying assumption – that serious captioning requires heavy on-site infrastructure – no longer holds.

What Cloud-Native Captioning Actually Changes

AI on its own doesn’t fix captioning. You need the right architecture around it. The real shift comes from moving captioning to a cloud-native, software-driven model that can plug into existing broadcast and streaming workflows.

A few tangible differences:

No more encoder dependency.Captioning no longer hinges on whether a box made it into the truck. You don’t need a rack full of hardware to deliver compliant, high-quality captions. You just need the right integrations and an internet connection.
One platform, all use cases.Historically, different environments often meant different solutions: broadcast encoders for linear, something else for streaming, something entirely separate for in-room displays or corporate events.

A cloud-native platform like Timbra can handle:

Live broadcast captioning
In-room captions at events and summits
Captions via companion devices
Multilingual variants for international feeds
Enterprise AV use cases like product launches, internal town halls, public-sector briefings and more
The ability to post captioned VOD content post-live broadcast

The captioning logic, AI models, dictionaries and rules live in one place, even if outputs go everywhere.

3 Multilingual as standard, not a special project.

Real-time translation has reached a point where multilingual captions are viable at scale. With the right language models and controls in place, broadcasters can spin up additional languages without commissioning a completely separate production. This supports:

Regionalized streams without separate master control
Parallel markets where English-only used to be the limit
Enterprise and government comms where multiple languages are mandatory

In revenue terms, each additional language becomes a lever to tap new audiences and advertisers, rather than a major new cost line.

4 Operational control shifts to production teams.

Cloud-native captioning platforms are designed to be operated by production, digital, or events teams – not just engineering specialists. Interfaces are more intuitive, configuration is simpler, and changes can be made on the fly. That doesn’t remove the need for engineers, but it does mean captioning isn’t blocked by a handful of people.

5 Better fit with sustainability targets.

Removing hardware from the chain isn’t just a convenience. Less shipping, less power-hungry kit, and fewer site visits all support sustainability goals that many broadcasters and media organizations have already committed to.

Captioning may be a small part of the overall footprint, but cloud workflows help take friction out of decarbonization.

From Broadcast to Enterprise AV – Same Problem, Same Solution

What’s happening in broadcast is mirrored across all other industries who have for many a political or economic reason, been able to move more quickly: global town halls streamed to thousands of employees in different regions, and enterprise broadcast suites, once laden with hundreds of light-flashing boxes, are now sitting unused or being shut down for remote control production – we’re seeing this in major public institutions to the world’s largest tech companies down to small production teams who have been serving major clients for decades.

All of these share the same constraints: limited specialist resource, dispersed audiences, and a need to move fast without compromising on quality or compliance.

The workflows that Timbra is running in major broadcast environments map directly onto these Enterprise AV scenarios. If a solution can comfortably support a top-tier sports event, it can certainly support a global internal broadcast or product launch.

This is where the unit economics become interesting. One cloud-native localization suite can serve multiple parts of a business and multiple markets, instead of each team buying and maintaining its own rigid solution. Logistics costs down, support costs down, per new region costs down, per stream costs down. And ultimately the per new audience member reach cost significantly down.

What Broadcasters Should Be Asking Now

The technology is here. The question is no longer “is AI good enough?” – live deployments are already proving that it is, especially when combined with model selection, custom dictionaries, and human oversight where needed.

The more relevant questions are:

Why are we still relying on hardware-heavy captioning chains when software-based cloud workflows are more flexible?
How many revenue opportunities are we leaving on the table by not offering captions – or multilingual variants – everywhere we distribute content?
Are we treating captioning purely as a cost, or as part of our audience and revenue strategy?

For many organizations, the opportunity is less about adding something new and more about consolidating what already exists: bringing accessibility, localization, and event workflows into a single environment; reducing duplicated spend; and making it easier for teams across the business to turn captions on when they need them.

That’s ultimately where Timbra by CaptionHub sits: a way to take live captioning and localization out of the “clunky but necessary” category and turn it into a flexible, cloud-based service that supports broadcast, streaming and Enterprise AV alike.

Captioning is no longer the awkward, hardware-bound afterthought at the edge of the signal chain. Done right, it becomes a quiet but powerful revenue growth engine – one browser tab alongside everything else.

Perfect Synchronization: How SWIT Zero-Latency Monitors Enhance Live Opera Performances at Baltic Opera in Gdańsk

Client: Opera Bałtycka (Baltic Opera)
Location: Al. Zwycięstwa 15, 80-219 Gdańsk, Poland
Product(s) supplied: SWIT Zero-Latency Production Monitor(BM-U175, BM-245 & FM-215HDR)

Challenge

In opera performances, the conductor is the heartbeat of the show—guiding not only the orchestra but also providing rhythm cues for singers and performers on stage. However, in many opera houses, including the Baltic Opera in Gdańsk, the orchestra pit is located below stage level, making it nearly impossible for singers and backstage staff to see the conductor directly.

Marek Lebida – Light Programmer, Baltic Opera in Gdańsk faced a serious challenge: how to ensure that every performer and technician could stay perfectly in sync with the conductor’s movements. His initial setup involved a camera aimed at the conductor and several monitors placed around the venue. Unfortunately, conventional monitors introduced signal latency, resulting in a noticeable delay—by the time the conductor’s baton had fallen, the image on screen was still mid-air.

The orchestra pit deep below the stage level, making it difficult for singers and backstage staff to see the conductor directly.

Solution

To eliminate visual delay, Marek turned to SWIT’s zero-latency professional monitors. After learning that SWIT monitors achieve a near-instantaneous 5ms delay through hardware-level signal processing, he decided to test the 21.5-inch SWIT FM-215HDR model together with a Marshall PTZ camera.

The results exceeded expectations. The SWIT monitor displayed the live video feed from the orchestra pit in real time, allowing performers to follow the conductor’s gestures with perfect synchronization.

On-site testing of the SWIT monitor for the conductor’s live feed. The latency of the entire system is controlled to a level imperceptible to humans.

SWIT monitor showing conductor feed backstage.

The monitors placed on both sides of the audience seating allow the singer to see the beat without having to look down.

Expanding the System

Encouraged by the success of the test, Marek expanded the system throughout the entire opera house. BM-U175, BM-245 & FM-215HDR monitors were installed on both sides of the audience hall, behind stage curtains, and in backstage corridors. Using SDI signal distribution systems, different feeds—including the live stage view, infrared camera views for dark scenes, and backstage camera views—were seamlessly integrated into a unified monitoring network.

At the stage director’s desk, a SWIT monitor displays the conductor feed for precise cue coordination during live performance.

In the control room, the video system manages multiple camera inputs and SWIT monitor outputs across the opera house.

SDI distribution hub delivering synchronized video feeds to multiple SWIT monitors installed around the venue.

Reliability and Performance

Opera performances demand continuous operation and absolute reliability. SWIT’s broadcast-grade monitors deliver both, offering accurate color reproduction, rugged metal housing, and stable SDI connectivity ideal for live theatre environments.

The Venue

The Baltic Opera is one of Poland’s leading performing arts institutions, located in the historic city of Gdańsk. Its modern interior combines rich acoustic design with advanced stage technologies—making it the perfect venue to demonstrate the reliability and precision of SWIT’s zero-latency monitoring solutions.

Exterior of the Baltic Opera in Gdańsk, Poland—home to the innovative zero-latency monitoring system powered by SWIT.

Interior view of the Baltic Opera main hall, where SWIT monitors are integrated across the stage and backstage areas.

Technical Highlights

Zero-latency (≤0.01 frames) SDI signal processing（All SWIT monitors in the BM series and FM series feature zero-latency capability.)
Wide viewing angle for stage visibility
Rugged housing and flexible mounting options
Integrated into SDI distribution for multi-feed monitoring
Detailed monitor placement within the theatre, illustrating real-time feed distribution to performers and technical teams.
The system has transformed workflow efficiency at Baltic Opera, enabling every department—from performers to technical crews—to stay synchronized with the live orchestra. The collaboration between Marek Lebida’s creative vision and SWIT’s zero-latency technology has set a new standard for real-time stage monitoring in the performing arts sector.
Credits & Acknowledgements

Marek Lebida – Light Programmer, Baltic Opera in Gdańsk

This project was realized in collaboration with Marek Lebida – Light Programmer, Baltic Opera in Gdańsk.Special thanks to the Opera Bałtycka (Baltic Opera) for their support and permission to share this case study.

Follow Baltic Opera and Marek Lebida:

Signiant – Lost in Space: Bringing Premium Workflows to Every Project

Jodi Carter

Post-production teams today are expected to deliver more content, across more platforms, and always faster. Lost in Space is a post house built for that reality; one that combines premium workflows with the flexibility and efficiency modern productions demand.

CEO Carolyn LaVeglia and CTO Alex Santoro built the New York–based company on a simple but staunch belief that every creator deserves access to premium post-production workflows, not just those with studio-level budgets.

Democratizing High-End Post

Lost in Space works across a wide range of projects, from branded content and independent films to multi-part streaming documentaries. Their approach is deliberately flexible, built around the needs of each production rather than a rigid pipeline.

“Whatever that process looks like from A to Z, we do everything within it,” explained LaVeglia.

That philosophy has made Lost in Space a trusted partner for creative teams that want to think big without breaking the bank.

When the company landed its first large-scale documentary in 2023, their existing tools and ad-hoc file transfer methods began to show cracks. The volume and sensitivity of media demanded something more robust; a secure, reliable foundation that could scale with their growing ambitions.

A Moment of Necessity

That’s when the team turned to Signiant, the platform used by media companies worldwide to move large files quickly, securely, and intelligently.

“I remember when the decision happened,” recalled Santoro. “That was the day we said, ‘We got to call [Signiant] up today and get it, because it’s going to be essential to deliver and coordinate every single asset starting from the first shoot.’”

Santoro already knew Signiant well, having managed it at a major production company. “Ever since then I thought, this is the pinnacle of media transfer services and our clients should be operating at or above industry standard.”

After exploring alternatives, from WeTransfer and Dropbox to Aspera and MASV, the conclusion was clear.

“A lot of companies opt for the cheapest solution,” says Santoro, “but they’ll end up with five years’ worth of projects on Google Drive, or an insane amount of WeTransfer links. Signiant has always proven to be among the fastest and most reliable systems we’ve ever used.”

The Backbone of Collaboration

What began as a single urgent deployment quickly became central to Lost in Space’s operation. Signiant now underpins every file transfer across the company’s projects, from first-day dailies to final delivery.

By replacing hard-drive shipping and consumer-grade sharing tools with secure, branded portals, Lost in Space gained something invaluable: peace of mind. Every client interaction now happens within a controlled, professional environment.

That reliability has a tangible business impact.

“Sometimes in post, you’ll consider anything. If you pay this one yearly cost, the amount of headache that you could possibly save over the grand course of the year is a lot in dollars. This results in creating efficiencies for our clients who are operating on increasingly tighter deadlines and budgets,” LaVeglia stated.

Security Meets Flexibility

For Lost in Space, flexibility and security aren’t competing priorities; they’re two sides of the same coin. Every project’s setup varies from on-prem, cloud, hybrid, or shared NAS systems. Signiant’s storage-independent architecture lets the team connect to any of them directly.

“Being able to map Signiant to whatever storage the client wants is just amazing,” said Santoro. “We’re not locked into a single environment, and that gives us creative freedom.”

That freedom is fortified by enterprise-grade protection. End-to-end encryption, granular access controls, and compliance with major industry standards ensure sensitive materials remain secure. As the company pursues Trusted Partner Network (TPN) certification, a must for major studio work, and Signiant’s own TPN Gold Shield status gives them a critical head start.

Reliability That Pays for Itself

While some file transfer tools offer lower upfront costs, LaVeglia and Santoro see Signiant as the more sustainable choice. Time lost to failed uploads, corrupted transfers, or client confusion can easily exceed any perceived savings.

“For me, it’s so straightforward to sell through,” explained Santoro. “If Media Shuttle is part of the deal, we’ll spin up a portal, add the client’s branding and colors, and within minutes they’re impressed. It sets a tone of professionalism that reflects well on us.”

By baking Signiant into every workflow rather than treating it as an optional add-on, Lost in Space eliminates guesswork for clients.

“We don’t like to deviate from what we feel is the gold standard,” LaVeglia emphasized. “For us, Signiant is the highest standard.”

Saving the Day, Again and Again

The real test of any workflow tool comes when things go wrong, and in production, they inevitably do. Lost in Space has faced plenty of those moments: waiting for shipped hard drives, last-minute upload requests, or misplaced camera cards.

“It’s crazy because there are so many moments that it almost feels normal,” remarked LaVeglia. “You’ll hear, ‘The producer shipped both sets of drives together and they got lost. What do we do?’” The solution is simple; the team sends the producer a Submit portal link and has them upload the media directly from the field.

Those quick saves often have creative consequences. In documentary work, the ability to securely receive footage immediately can mean the difference between capturing a once-in-a-lifetime interview or missing it forever.

“If you tell someone, ‘We’ll get you a drive in three days,’ they might change their mind,” said LaVeglia. Being able to interact instantly keeps the momentum of the creative team going.

The professionalism of branded portals also reassures contributors when handling personal or archival material.

“It’s not just a public WeTransfer or Google Drive link that can be shared anywhere,” she noted. “It adds assurance and probably allows our teams to get more than they otherwise would have.”

A Partnership Built on Trust

Ultimately, Lost in Space sees Signiant not just as a solution, but as a trusted partner. When issues arise, support is fast and knowledgeable, a rarity in the post-production tech world.

That trust, LaVeglia stated, mirrors the standards Lost in Space sets for its own clients: being responsive, dependable, and always there when needed.

“It adds to our reputation, because this is a service that we’re not only using but reselling. It gives me the confidence to know that we have a backbone of a vendor that we trust and can rely upon. That honestly means everything to us,” LaVeglia explained.

Datos Media Technologies – Canarias TV: The Integration of a Unified, Collaborative And Scalable News Platform In The Future

David Martínez del Cerro

COO Datos Media Technologies

Miguel Angel Rodrigo Alonso (PhD)

Marketing & Broadcast System Engineer Datos Media Technologies

The Canarias TV Project

CanariaTV, the public television of the Canary Islands, has completed an ambitious global technological renovation that marks the end of its definitive transition to HD. Datos Media has been key in the design and implementation of the news infrastructure, covering both production sets and news systems. Recognized with the award for Best Infrastructure Renovation Project in the 2025 edition of the TM BROADCAST Awards, the initiative responded to the obsolescence of old equipment and the need for modernization to improve broadcast quality.

The Challenge

The deployment took place coinciding with a anniversary of the Public TV, with tight deadlines and minimal operational disruption. Inter-island distance and limited connectivity were solved with lightweight web clients and transfer accelerators such as FileCatalyst, allowing remote work from any island. The SD-HD transition required extensive training of staff. The onair was staggered: one location first, followed by the other in 48 hours, always with the old system in parallel.

For this end-to-end renovation of the news production systems, it was decided to integrate the Avid MediaCentral Cloud UX platform. Both headquarters now operate as a unified newsroom, despite their technical independence to ensure redundancy and availability. A private cloud was implemented with two data centers (one per island), virtualized on Dell hardware, VmWare and Cisco network. This covers ingest (via Telestream Content Agent and MediaCentral Acquire), transcoding, editing (85 web clients and 10 Avid Media Composer rooms), redaction (100 iNEWS/MediaCentral clients), graphics (Avid Maestro), and playout (Avid Command). Avid Nexis F4 shared storage offers up to 3.6 PB of scalable capacity.

A Unified, Collaborative, and Scalable Platform.

This project represents a milestone in the evolution of Televisión Canaria, providing it with a robust, scalable infrastructure focused on remote collaboration, which improves operational efficiency and quality for viewers. The abstraction of hardware and the use of cloud solutions not only solve past limitations, but also prepare the channel for future innovations, such as the integration of AI or 4K content. Datos Media has stood out for its technical solvency, demonstrating that in complex environments such as islands and their inter-island distances, it is possible to achieve modernization without interrupting production. This whole process has positively influenced editors, technicians and audiences by raising the standards of public production in the Canary Islands, since access to inter-island information and its production is significantly reduced, providing an immediate public information service.

NStarX – From Hospital Beds to Studio Sets: Applying Healthcare’s Predictive Census AI to Media Workforce Optimization

Sujay Kumar(CRO), Suman Kalyan (CAIO), NStarX Inc.

Why Healthcare’s Predictive Census AI Maps Directly to Media Production
Both healthcare and media production face the same operational challenge: managing a large, specialized workforce under volatile, high-stakes demand. In hospitals, patient inflow unpredictability strains staffing, while in media production, fluctuating project pipelines drive overtime, budget overruns, and resource conflicts. Healthcare has already solved this problem at scale using predictive census AI—forecasting demand with >90% accuracy and optimizing staffing in real time. Media production follows a structurally similar pattern: patient flow mirrors project flow, departments mirror production units, and clinical resources map to crew, studios, and equipment. By adapting healthcare’s proven architecture—time-series forecasting, ensemble models, and visual operational dashboards—media companies can shift from reactive scheduling to proactive workforce management, unlocking significant reductions in overtime, idle time, and budget variance.

The Costly Challenge of Workforce Mismanagement
Human capital represents the largest operational expense in both healthcare and media production, yet effective resource allocation remains elusive. In healthcare, the financial toll is staggering: hospital labor costs surged 258% between 2019-2022, with staffing shortages costing $24 billion in 2021, projected to reach $86 billion by year’s end. Contract labor expenses skyrocketed 257.9%, while median wages to staffing firms rose 56.8%. Each registered nurse vacancy costs approximately $175,000 annually at medium-sized hospitals, while emergency departments experienced 22% more overtime hours per visit.

Media production faces parallel challenges. Film productions routinely exceed budgets by 31%, with independent films overshooting by 40%. Workforce costs consume 30-40% of total budgets. Notable examples: ‘Cleopatra’ (1963) ballooned from $2 million to $44 million (equivalent to $340 million today), while ‘Waterworld’ (1995) escalated from $100 million to $175 million, both partially due to crew management failures. Coordinating specialized talent across production phases, managing union requirements, and controlling overtime creates persistent margin erosion.

Healthcare’s AI-Powered Breakthrough
Healthcare organizations have turned to artificial intelligence and machine learning for workforce optimization, with 65% of U.S. hospitals now deploying AI-assisted predictive models. These solutions leverage sophisticated time-series forecasting architectures, with Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) emerging as preferred models for capturing temporal dependencies in sequential healthcare data. LSTM-based models achieve remarkable accuracy levels of 92.4% with 90.1% precision in predicting patient outcomes and resource
needs. Ensemble approaches combining ARIMA (statistical time-series models), multilayer perceptrons, and LSTM networks deliver even superior performance, with relative 95% confidence intervals of ±3.4% for 12-hour census predictions.

The implementation architecture typically includes data ingestion layers pulling from Electronic Health Record (EHR) systems, preprocessing pipelines handling irregular time-series data and missing values, and real-time dashboard applications delivering actionable insights to hospital administrators. Hawaii Pacific Health exemplifies successful deployment, using predictive analytics for workforce forecasting and scheduling to transition from retrospective planning to proactive management, achieving 10-20% improvements in staff utilization and 15% reductions in overtime and agency labor costs. The system analyzes historical admission data, patient trends, seasonality, and real-time variables to ensure right-sized staffing at optimal times.

Healthcare-Media Workforce Parallels
While healthcare provides rich quantitative insights into workforce strain, media production exhibits the same pattern in financial and operational terms. Hospitals face cost inflation through overtime and contract labor; media productions experience parallel erosion through union overtime rates, freelancer premiums, schedule delays, and department overruns. The numerical symmetry is striking: healthcare sees 10–20% workforce inefficiency, while films exceed budgets by 31–40%—a function of the same underlying failure to predict demand and align labor in advance. This structural alignment strengthens the case for transferring predictive census AI from healthcare to media with minimal architectural change.

Translating Healthcare’s AI Architecture
Healthcare’s proven architecture translates directly to media with minimal modification. Core components—time-series forecasting engines, ensemble ML models, and operational dashboards—remain consistent. The data layer integrates production management systems, casting databases, equipment platforms, and historical project data. LSTM/GRU networks learn from project types, crew requirements, equipment dependencies, and seasonal cycles.
The prediction engine forecasts project demand, optimal crew allocation, resource conflicts, and budget risks. Dashboards provide production managers with staffing recommendations, equipment optimization, overtime alerts, and conflict warnings. While tools like Filmustage and Autodesk Flow have begun incorporating scheduling capabilities, comprehensive predictive census functionality remains nascent.

Simplified Three-Layer Architecture for Executives
To make the AI approach intuitive for production leaders, the full technical architecture can be summarized in three simple layers:
• Data Layer – Ingests project schedules, casting data, equipment inventories, historical productions, and union rules into a unified data lake.
• Intelligence Layer – Uses LSTM/GRU models, ARIMA ensembles, and attention mechanisms to forecast project demand, staffing levels, equipment conflicts, and overtime risk.
• Action Layer – Delivers clear recommendations through dashboards: optimal crew allocation, resource heatmaps, overtime warnings, budget variance predictions, and automated scheduling.
This streamlined view gives executives immediate clarity on how predictive census AI transforms production planning from reactive firefighting to proactive resource orchestration.

Figure 1: Media Workforce Predictive Census- Solution Architecture
Data Layer: Production systems, casting platforms, equipment inventories, union rules, historical project metadata, market indicators.
Analytics Engine: LSTM/GRU networks for temporal patterns, ensemble models combining ARIMA with neural networks, attention mechanisms for dependencies, transfer learning from healthcare.
Prediction Outputs: Project inflow forecasts, optimal crew allocation, equipment utilization, budget variance probabilities, schedule conflict scoring.
Dashboard: Real-time staffing recommendations, resource heatmaps, automated scheduling, budget scenarios, what-if analysis.

Revenue Impact
Predictive census offers multiple revenue pathways. Direct cost savings: 10-15% reductions in idle time, 20-30% decreases in overtime, 15-25% lower freelancer premiums. Enhanced throughput enables more concurrent projects without proportional cost increases, while faster completion accelerates revenue recognition. Accurate bid pricing improves win rates and margins.
Strategic capacity planning unlocks new revenue streams through proactive capacity investments aligned with forecasted demand. For mid-sized production companies processing $50M revenue annually, conservative 8-12% utilization improvements and 5-7% throughput increases could generate $4-7M additional annual profit.

Implementation Challenges
Successful deployment requires overcoming three critical challenges:
• Data Fragmentation – Media lacks an integrated equivalent to healthcare’s EHR systems. Solving this requires consolidating scheduling tools, budgeting software, and equipment platforms into a single governed data layer.
• High Creative Variability – Unlike standardized clinical pathways, productions range dramatically by genre, budget, director style, and VFX intensity. This is mitigated through ensemble modeling, project taxonomies, and complexity-weighted feature engineering.
• Cultural Resistance – Production teams may perceive AI recommendations as limiting creative autonomy. Adoption improves when AI is framed as augmentation—not replacement—and introduced through transparent pilot programs.
These streamlined challenges make the path to implementation clearer for executive decision-makers.

Conclusion
Healthcare’s success with predictive census AI demonstrates that workforce optimization in complex, high-variability environments is not only possible—it is proven. Media production mirrors healthcare’s operational structure: unpredictable demand, high labor intensity, expensive overtime, and cascading resource dependencies. By applying the same predictive architecture, media companies can achieve measurable gains: 20–30% reduction in overtime, 10–15% reduction in idle crew time, 15–25% lower freelancer premiums, and 5–7% higher throughput without increasing headcount. In an industry where margins are tightening and project complexity is rising, predictive workforce optimization is no longer a future ambition—it is a strategic imperative for competitiveness, profitability, and sustainable growth. The technology is validated, the architecture transferable, and the financial upside compelling.
References
1. Healthcare Finance News (2023). ‘Hospitals’ labor costs increased 258% over the last three years.’
2. American Hospital Association (2021). ‘Health Care Workforce Challenges.’
3. Filmustage (2024). ‘Film Budget Breakdown by Department.’
4. Health Affairs (2024). ‘AI and Predictive Models In US Hospitals.’
5. PMC (2020). ’12-hour hospital census prediction algorithm.’ NIH.
6. ACM Transactions (2022). ‘Time Series Prediction Using Deep Learning in Healthcare.’
7. Frontiers (2020). ‘AI in Healthcare: Time-Series Forecasting Architectures.’

Dalet – The Economic Shift: From Waterfall to Agile in 2026

Matteo De Martinis, Head of Product – AI & Media Production, Dalet

Looking back at 2025, one thing is clear: this was the year AI finally moved from experimentation to production in the media industry. At the beginning of the year, especially at the NAB Show in April, we still saw hesitation. Media technology buyers were curious but cautious, unsure whether AI would deliver real operational value. But very quickly after the show, that hesitancy faded. Broadcasters and content companies began requesting implementations, not just demonstrations. They tested AI capabilities and, most importantly, saw tangible benefits.

Transcription, translation, and other core AI-powered functions, once considered innovations, became table stakes. Once customers used them in real workflows, expectations shifted. As we step into 2026, the question is not whether AI belongs in media workflows, but where the industry goes next. The answer lies in two major shifts: first, a move towards autonomous, task-oriented AI components; second, a cultural embrace of agile, iterative approaches over traditional waterfall thinking.

Agentic AI as the Next Evolution of Workflow Design

Over the last year, it has become clear that generative and assistive AI, while transformative, were only the beginning. The real opportunity emerges when these systems evolve into agents: specialized, autonomous components that take action, collaborate, and help orchestrate complex processes.

At Dalet, this is exactly the direction we have been exploring with Dalia and our broader roadmap. Instead of designing a large AI platform that attempts to do everything, we are building a system of focused agents that mirror real media functions. For example:

a Search Agent that navigates and extracts information from archives,
an Editing Agent that assists in assembling sequences,
an Ingest Agent that manages large volumes of incoming material,
a Review Agent that helps with packaging and quality checks.

Each agent focuses on a specific task area, and together they shape a coordinated ecosystem that supports the entire media workflow. This reflects how teams actually operate: coordinated, yet specialized.

What makes this model powerful is that it reduces complexity, not adds to it. Integrations that once required heavy engineering can be mediated through agentic frameworks. Actions that once required deep product expertise can now be performed intuitively. Instead of making systems larger or more rigid, this approach makes them more accessible and simpler to use, even as capabilities expand.

Simplifying Deep Systems Without Losing Power

Stepping into the media technology industry, one of my first realizations was the paradox at its core: incredible capability often trapped inside legacy systems. These platforms can do almost anything, if you know how. But that very power has become a barrier. They were designed for control and customization, not simplicity, and as a result, only a select few truly master them. For newcomers, the learning curve can be daunting.

This is precisely where modern AI can help, not by replacing expertise, but by lowering the barrier to entry and making deeply engineered systems easier to navigate. When users no longer spend their time on repetitive, highly technical steps, they gain more time for creativity, editorial judgment, and meaningful decision-making. Reducing friction inside workflows is about amplifying the value teams can produce.

The Real Transformation Ahead: A Shift in Mindset

While technology is evolving rapidly, the biggest challenge the industry faces is cultural, not technical. Many media organizations, and especially technical teams, still operate using a waterfall mindset. Long RFP processes, multi-year projects, rigid requirements, and an expectation that a solution must be complete and perfect before deployment.

This mindset is rooted in the history of broadcast and media technology. In the 80s, 90s, and early 2000s, systems were rigid, expensive, and hard to change. Failure had real operational risk. But today, the environment is entirely different. AI and software capabilities now evolve in rapid, continuous cycles, far faster than the multi-year upgrade paths the industry grew up with. Customer needs shift quickly, and integrations that once took years now happen in weeks.

This is why the industry must accelerate its move toward agile thinking. That means:

adopting technology in smaller, high-value steps,
deploying quickly, rather than waiting for perfection,
learning from early results,
improving continuously instead of periodically.
Even achieving 20% of a desired outcome in a month is more valuable than waiting five years for a theoretically “complete” solution. This is where agentic AI and agile thinking come together. Agents allow for small, modular improvements, and agility allows organizations to extract value much faster.

Financial Pressure Will Push the Industry Forward

The economics of the media industry are heavily influencing technology decisions. With constrained revenues and thin margins, organizations have little room for error in their business models. They must simultaneously find new sources of efficiency and innovative paths to revenue.

Industry leaders have increasingly pointed out that the media sector moves too slowly, with long decision cycles that no longer align with technological reality. New leaders entering media organizations, especially those coming from the technology sector, expect rapid progress, iterative development, and user-friendly design. They’re used to consumer-grade tools that work intuitively and update continuously. They will expect the same from media technology and the organizations that deploy it.

This pressure means that investments must deliver long-term value while enabling short-term wins. More modular, flexible AI approaches fit this need because they do not require platform-wide overhauls or massive upfront investments.

Some regions and organizations are ready to adopt innovation aggressively, while others remain cautious. But the trend is unmistakable: the pace of change is accelerating, and the cost of inaction is rising.

2026: The Year AI Broadens Participation

Perhaps the most meaningful transformation AI will bring in 2026 is broader access. When complexity decreases, more people can contribute. Tasks that once belonged only to technical specialists become available to a wider range of team members. This democratizes technology, empowering diverse teams to collaborate and innovate in ways that were previously out of reach. It changes not only how teams work, but who gets to participate.

AI in media is not about reducing the human role, but about expanding it by removing barriers, enabling new voices, and giving professionals more time to focus on what they do best.

If 2025 was the year the industry embraced AI as a tool, 2026 will be the year it learns to work with it more collaboratively, more intuitively, adopting a mindset ready to evolve as quickly as the world around it.

One Year In: Driving Zixi Forward with the Three Ps – Pricing, Partners, and Politics

Marc Aldrich, CEO, Zixi

Nearly a year into my role as CEO of Zixi, I’ve spent much of this time listening—listening to customers, partners, and the broader industry. From that, three priorities have guided our path forward: Pricing, Partners, and Politics. Each has been central to repositioning Zixi as the leading enabler of live video delivery over IP.

Pricing: Simplifying Engagement, Driving Outcomes

When I joined, customers praised our technology but found our business model complex. Pricing and packaging weren’t always clear, making it harder for organizations to engage fully with us.

We’ve since simplified our offerings into straightforward solutions aligned with real-world use cases—from sports broadcasters regionalizing feeds, to cloud playout providers scaling globally, to enterprises delivering corporate video. This shift ensures pricing is predictable, transparent, and aligned to business outcomes—with clear metrics such as per event, per channel, or per affiliate/station.

This clarity doesn’t just make invoices simpler—it enables deeper partnerships. Customers now start conversations not with “what does it cost?” but with “what can we achieve together?”

At IBC, we’ll showcase how technology innovation underpins this work: zero latency frame thinning, expanded encoding, and a mobile contribution app. These features solve problems in practical ways—and our new pricing ensures customers know exactly how to leverage them.

Partners: Building an Ecosystem for Shared Success

Zixi has always had strong relationships, but they weren’t always structured for mutual success. Over the past year, we’ve reinvigorated our partner ecosystem with a focus on interoperability and joint value creation.

Today, partners are not add-ons but multipliers of impact. Whether through interoperability with Harmonic, co-innovation with Amagi, or service delivery with Uplynk and Encompass, our aim is the same: reduce friction for customers and drive measurable outcomes.

The industry is moving away from siloed, proprietary workflows. Customers demand seamless interoperability—and Zixi enables it. Supporting 13 different protocols, including SRT, RIST, HLS, and our own, Zixi is the connective tissue powering the future of IP video delivery.

The results are clear: faster feature velocity, stronger solutions, and deeper trust with customers who increasingly view us as a long-term business partner.

Politics: Preparing for a Changing Landscape

Technology doesn’t exist in a vacuum—the political and regulatory environment is reshaping the industry.

The upcoming sunset of C-band satellite spectrum is accelerating the shift to IP delivery. At the same time, the U.S. administration’s “Big Beautiful Bill” is investing heavily in broadband infrastructure, opening new markets but also increasing scrutiny on how video is transported, monitored, and monetized.

Zixi is positioned to be the bridge from satellite to IP—helping customers transition smoothly while ensuring compliance and efficiency. We support existing standards like SCTE 224, SCTE 35, and ESAM, while also innovating with tools like POIS (Placement Opportunity Information Service) to unlock new monetization opportunities.

Our approach is not about lobbying but about aligning technology with real-world frameworks, so customers are prepared for what’s next.

Looking Ahead: Enabling the IP Era

Across Pricing, Partners, and Politics, the common thread is clear: Zixi enables the industry’s transition to IP.

At IBC we’ll demonstrate this mission in action:

Zero latency frame thinning for smoother streaming.
Modernized Broadcaster interface and ESNI management tools.
A mobile contribution app that turns any device into a Zixi encoder.
Integration with Time Addressable Media Store for fast live clipping.

These aren’t just features—they signal a clear direction: Zixi as the platform of choice for delivering live, high-value video anywhere in the world.

Year One Reflections

A year ago, Zixi had world-class technology but untapped potential in how it was positioned. Today, through the Three Ps, we’ve simplified engagement, rebuilt partnerships, and aligned with the realities shaping our industry.

The journey is ongoing, but the path is clear. With transparent pricing, a reinvigorated ecosystem, and forward-looking strategies, Zixi isn’t just adapting to the future of media distribution—we’re helping define it in close alignment with our customers and partners.

Quickplay – From Disrupted to Disruptor: How Broadcasters Win the Creator Era