BACKGROUND

Warner Bros. Discovery EMEA runs Cartoon Network and Boomerang across the UK and EMEA out of London. The core team we work with handles video marketing and branding, running the YouTube strategy, the channel calendars and the editorial campaigns that surround every show. We also work with WBD’s creatives in post-production, who prepare and deliver the material and sit closest to the library. The teams in France, Italy, Spain, Turkey, the Middle East and Africa use the platform as well.

For children’s media, YouTube is not a secondary marketing surface. It is the primary one, and the teams manage channels with tens of millions of subscribers between them. The main UK channels alone count more than 11 million subscribers on Cartoon Network UK and more than 8 million on Boomerang UK. That means publishing constantly, across long form, shorts and vertical, in multiple aspect ratios and territories, every week. It is a factory, not a campaign.

THE LIBRARY

We do not process the whole Warner Bros. Discovery library. We process the top kids franchises, which is where the publishing demand actually sits. That slate includes The Amazing World of Gumball, Adventure Time, Steven Universe, Apple & Onion, Ben 10, Clarence, Craig of the Creek, Looney Tunes, Regular Show, Teen Titans Go!, Scooby-Doo, Tom and Jerry and We Bare Bears, among others.

Each of those is seasons or decades of episodes, and most of it has only ever been cataloged at the title or episode level. That is fine for rights and distribution and close to useless when someone needs the eleven seconds where a specific character does a specific thing.

THE CHALLENGE

Ask anyone in either team and they will tell you the moment they need exists. They can usually describe it precisely. What they cannot do is get to it quickly. The traditional route is to guess which episode it lives in, open it and scrub. Failing that, search filenames, ask a colleague who has been there long enough to remember, or dig through legacy metadata written for a different purpose a decade ago. For a single clip that is annoying. Across a franchise slate this deep, several territories and a weekly schedule, it becomes the largest cost in the workflow.

Part of the problem is the tooling. There is a traditional MAM system in the building, but creative teams do not necessarily work with it. It serves the content operations and engineering functions and it is built for them, and like most enterprise MAM and DAM systems it was not made to understand video, find moments inside it without tags and metadata, or be intuitively simple for creative teams. Imaginario leapfrogs the MAM entirely. For this workflow it is not needed, and the content ultimately lives in cloud storage services like AWS S3, which is exactly where we plug in.

It also quietly caps ambition. When finding a moment is expensive, teams reuse the same well-known moments, because those are the ones they can locate. The long tail of the catalogue, which is where the fresher material sits, goes untouched.

THE SOLUTION

We indexed the franchise libraries using our proprietary AI systems across visuals, speech, sound and people, without relying on manual tags or labels. Teams then search in plain language and get results at shot level, with frame-accurate boundaries, rather than a full episode to scrub through. Over four years the work has settled into three patterns.

Single cuts from long-form episodes. The most common job. Take a full episode and pull the one self-contained moment that works as a standalone post. A gag, a stunt, a reveal, a line that travels on its own. The episode runs twenty-two minutes and the post runs forty seconds, and almost all of the effort used to go into locating the forty seconds rather than cutting them. The same moment rarely ships once. It gets reframed and reformatted for YouTube, Shorts and the vertical platforms, so one good find feeds every platform on the calendar.

Seasonal compilations across episodes and shows. Halloween, Christmas, back to school and the rest of the calendar all need packages that gather the right moments from across different episodes, different series and different show brands. This was close to impossible to do well before, because nobody could survey the whole slate for a theme. The realistic approach was to use the episodes the team already remembered. Searching by theme, mood, setting and action means a Halloween compilation can now draw from titles nobody had opened in years.

Character identification. This is the capability that mattered most, and it is what separates children’s animation from most other video work. These brands are built on characters. Almost every brief, editorial or commercial, is phrased in terms of a character rather than an episode. Standard face recognition is trained on human faces and does not help here. Identifying specific animated characters, and then finding every moment they appear in across the slate, is what makes the rest of the workflow usable. Without it, a search for a character returns episode titles. With it, it returns moments.

Finding the moment is half the job. The teams finish it in Clip Studio, where they finesse the in and out points, add branding and add subtitles. For bigger packages they group moments into collections and export them to Adobe Premiere, which Imaginario supports natively, so an editor picks up a sequence rather than a folder of loose files.

None of this works without the plumbing. Connecting directly to AWS S3, where much of the material lives, meant the teams never had to move or duplicate assets to make them searchable. At the other end, results go straight into Premiere. Imaginario sits on top of the storage and editing infrastructure that was already there, so nothing had to be replaced.

Language matters just as much across EMEA. The platform supports more than 100 languages and dialects, which helps the teams in other territories enormously with captioning, and the search engine natively supports more than 17 languages, so a producer in Madrid or Istanbul can search the same library in their own words.

The saving is not confined to one desk. Assistant editors, producers and marketers all spend a large part of their week locating material before any creative decision gets made, and all three groups got that time back. Output went up, because the same headcount ships more per week and publishing frequency is one of the strongest levers on YouTube views. Selection improved, because when searching is cheap teams explore, and older titles and secondary show brands come back into rotation.

The audience numbers tell the same story. When Cartoon Network UK celebrated reaching 10 million YouTube subscribers in early 2024, the team had already been working with Imaginario for a year and a half. Since then the channel has added close to 2 million more, growing to over 11.8 million subscribers, an increase of almost 18% in about two and a half years. That growth has many drivers, and we would not claim sole credit, but publishing more, fresher moments every week is one of them.

A CLEAR RETURN ON INVESTMENT

Four years of daily use have produced tangible benefits for Warner Bros. Discovery EMEA, including:

Ad sales growth always has several drivers and we would not claim sole credit, but faster, character-accurate packaging is a direct input into both the volume of sellable inventory and the conversation about what it is worth.

KEY FACTS

• Company: Warner Bros. Discovery EMEA, kids and family brands, running Cartoon Network and Boomerang out of London
• Teams using Imaginario AI: video marketing and branding, and post-production in London, plus the teams in France, Italy, Spain, Turkey, the Middle East and Africa
• Users: assistant editors, producers and marketers
• Use case: plain-language search and shot-level clipping across franchise libraries, seasonal compilations, animated character identification, with content staying in AWS S3 and results exporting to Premiere
• Library: top kids franchises including The Amazing World of Gumball, Adventure Time, Steven Universe, Ben 10, Looney Tunes, Regular Show, Teen Titans Go!, Scooby-Doo, Tom and Jerry and We Bare Bears
• Working with Imaginario: 4 years
• Results: 70% less time searching and clipping for social, over 10% year on year growth in ad sales

If your team is sitting on a library it cannot fully see, we should talk.

Read next: How Cineverse searches its 60,000-title library and cut video search and clipping time by 75%.

FREQUENTLY ASKED QUESTIONS

This guide explains the difference in plain terms, clears up how semantic search relates to vector search and lexical search, and shows what all of this means for video, where the thing you are searching for is often something nobody ever wrote down.

What keyword search does

Keyword search, also called lexical search, matches the literal words in your query against the words in a document, a transcript or a tag. Search for couch and you get results that contain the word couch. It is fast, predictable and easy to build, which is why it has powered search boxes for decades.

This is the engine behind most search boxes, including the ones inside MAM and DAM systems, typically built on an inverted index like Elasticsearch or Lucene. Fuzzy matching softens the edges, but fuzzy search fixes spelling, not meaning. A search for Volvvo still finds Volvo. A search for couch still misses sofa.

The weakness is that language rarely cooperates. If the transcript says sofa, a keyword search for couch returns nothing. If a video shows a couch but nobody says the word, keyword search cannot find it at all. Teams compensate by tagging content manually, which is slow, inconsistent and never finished.

That tagging dependency is an operations problem in its own right. Keyword search is only as good as the index behind it, and for video that index is built by manual or semi-manual logging. Someone generates the metadata, someone else runs quality control, and a governance layer keeps the controlled vocabulary consistent, often across several passes over the same footage. The cost compounds as the library grows, because every new naming rule has to be applied backward to everything already tagged, and the backlog is never really finished. Most of that labeling also happens at asset level, one set of tags for an entire file, so even a perfectly tagged asset tells you nothing about where inside it the moment actually lives.

Consistency is the harder part. Different teams and business divisions name the same moment differently. A promo team’s key moment is an editor’s selects and a rights team’s usage event. Inter-annotator agreement is low even within a single team, and it drops further when teams work in different languages and cultural contexts, where the same scene is described with entirely different words. Synonym rings and thesauri patch some of this, but they are one more asset to govern. All of that infrastructure exists to feed keyword search a vocabulary it can match against, which is why replacing the matching model changes the economics of the whole pipeline.

What semantic search does

Semantic search converts both your query and the content into mathematical representations of meaning, often called embeddings. Instead of asking whether the words match, it asks whether the meanings are close. A search for couch also surfaces sofa. A search for celebration finds the confetti drop, the champagne toast and the team jumping on the field, because all of them sit near celebration in meaning.

Done right, semantic understanding also starts at shot level and works its way up, shot to scene to asset. That means a match points at the exact moment inside the file, not at a whole two-hour asset that contains it somewhere.

That gives you three things keyword search cannot deliver. You get exact matches plus results that are semantically close to your query. You get topic-level results, where searching for a theme finds moments that discuss it in different words. And you get resilience to phrasing, so two people asking the same question differently find the same answer.

Semantic search vs vector search vs lexical search

These terms overlap and are often used loosely. Lexical search is the formal name for keyword matching. Vector search is a retrieval technique, the mechanics of finding items whose embeddings sit close together in mathematical space. Semantic search is the user-facing capability, search by meaning, and vector search is the most common way to implement it. In other words, vector search is how, semantic search is what. Many production systems are hybrid, running lexical and semantic retrieval together and blending the results, so exact matches always rank and meaning fills in the rest.

Keyword search vs semantic search at a glance

Five examples: keyword search vs semantic search

Take a simple query: red Volvo. Keyword search treats it as two independent words. It happily returns a red dress, because red matched, and a blue Volvo, because Volvo matched. Semantic search understands that red describes the Volvo, so it returns the red car and nothing else. Here is how that plays out across the footage real teams work with.

Why this matters more for video than for text

Text search has one modality, the words on the page. Video has at least three. What is said, what is shown and what is heard all carry meaning, and most of it never appears in any transcript or tag. A drone shot of a coastline at sunset contains no words at all. A CEO giving a speech at a conference is defined as much by the stage and the audience as by the sentences spoken.

Semantic video search indexes all of it. At Imaginario, speech analysis, meaning transcriptions, captions, chapters and much more, covers more than 100 languages and dialects with automatic language detection, and search is available natively in 17 languages, so you can search for phrases, words or topics in Spanish, for example. Queries can also combine modalities, like a person plus an action plus a phrase, to pinpoint one exact moment in thousands of hours of footage.

Natural language search is the interface

Natural language search means typing the way you speak. Instead of guessing the right tag, you write two people arguing in a kitchen or our founder talking about pricing, and the engine does the interpretation. Semantic search is what makes natural language search work, because a conversational query almost never matches the literal words in the content. Together they remove the last piece of training every search box used to require, learning how the system wants to be asked.

How Imaginario runs semantic search on video

Imaginario runs two AI engines. The first is contextual understanding, which powers temporal semantic search across dialogue, visuals, people, and audio with no tags or metadata needed. The second generates enriched, shot-level metadata and descriptions for teams whose workflows run on it, mapped to their own taxonomy and schema. You can read more about both on the two engines and our AI systems pages.

Frequently asked questions

What is semantic search?

Semantic search is search based on the meaning of a query rather than its exact words. It represents queries and content as embeddings, compares them by conceptual closeness, and returns exact matches plus results that mean the same thing in different words.

Is semantic search the same as vector search?

No. Vector search is the retrieval technique, finding items whose embeddings are close together. Semantic search is the capability it enables, search by meaning. Most semantic search systems use vector search under the hood, often blended with lexical matching in a hybrid setup.

What is the difference between lexical and semantic search?

Lexical search matches literal words, so results must contain the query terms. Semantic search matches meaning, so synonyms, paraphrases and related topics are found even when the words differ.

Does semantic search replace metadata?

Not necessarily. Semantic search removes the need for manual tagging to make content findable. Many teams still want structured metadata for rights, compliance and MAM or DAM workflows, which is why Imaginario pairs semantic search with an enrichment engine that generates shot-level metadata mapped to your taxonomy.

Does semantic search work in languages other than English?

Yes. Imaginario supports speech analysis in more than 100 languages and dialects with automatic language detection, and search is available natively in 17 languages.

Can ChatGPT or Claude analyze video at all?

Not directly. ChatGPT’s file uploads cover text files, spreadsheets, presentations, PDFs and images. Drop in an MP4 and you will not get an analysis of the footage. What ChatGPT genuinely can do sits around the edges of video: describe a screenshot you paste in, work with a transcript you provide, and generate new video through OpenAI’s separate Sora models. Generation and understanding are different problems, and being able to produce a clip does not mean the assistant can watch one.

Claude sits in the same place. Anthropic’s supported formats are documents (PDF, DOCX, CSV, TXT and similar) and images (JPEG, PNG, GIF, WebP), with chat uploads allowed up to 500 MB per file. Video and audio formats are not on the list at any size. Like ChatGPT, Claude is excellent with the text around your video, transcripts, briefs, shot lists, metadata, and can describe any still frame you give it. The moving picture itself is out of reach for both.

The YouTube case trips people up most. Ask either assistant to summarize a YouTube video and you will often get a confident summary. Look closely and that summary comes from the video’s title, description and public transcript, pulled through web browsing. If the transcript is wrong, thin or missing, the summary is guesswork. Nothing looked at a single frame.

What can AI assistants actually do with video today?

The honest state of play in 2026: general assistants are getting real video input, with hard limits. Google’s Gemini app accepts video files up to 2 GB each, with total length capped at 5 minutes free or 1 hour on Google AI Pro and Ultra plans. Under the hood, models like these sample frames at intervals and pair them with the audio track, which works well for one short video: summarize this lecture, what happens in this clip, read the text on that slide.

The limits follow from the design. Sampled frames miss fast action between samples. One video at a time means no memory of your footage from one chat to the next: upload, ask, and next session start again. And a chat window is the wrong shape for the real question most video teams have. You are rarely asking “what is in this file”. You are asking “which of my ten thousand files contains the moment I need”, and after that, “now turn it into something I can publish”.

Why is a video library a different problem?

Scale is the obvious reason. A media team’s archive runs to hundreds or thousands of hours, in masters that can be many gigabytes each. No chat assistant ingests that, and re-uploading files into a conversation every time you have a question is not a workflow. The industry numbers put the stakes plainly: producing a single clip from archive content costs $100 to $500 in search and editorial time, and around 80% of video archives sit untouched as dark data, unwatched because nobody can find what is in them.

The deeper reason is that library work needs persistent, structured understanding rather than a one-off answer. You need every file indexed once, at shot level, across everything that carries meaning in video: what is on screen, what is said, what is heard, and who appears. You need results that come back as timecoded moments you can act on, not a paragraph of prose. And you need it on footage you hold the rights to, inside your own storage, without your masters passing through a consumer chat product.

Why do codecs, frame rates and transcoding make this harder?

There is a layer under all of this that rarely makes it into AI marketing: production media is nothing like web video. Footage arrives as Apple ProRes or Avid DNxHD and DNxHR from edit bays, Sony XAVC from broadcast cameras, and camera RAW formats like RED’s R3D or Blackmagic RAW from set, at frame rates from 23.976 to 59.94 and resolutions from HD up to 6K and 8K. These files are enormous by design. A single hour of UHD ProRes 422 HQ runs past 300 GB, which puts a 2 GB chat upload cap at roughly 20 seconds of footage. Nobody is dropping a production master into a chat window.

Before any AI can read that material, it has to be decoded and normalized, which in practice means transcoding to proxies. That step costs real time and real money, and both scale with resolution, frame rate and the sheer variety of formats in a library. Mixed frame rates, interlaced legacy tape transfers and variable frame rate phone footage each add their own failure modes. For teams that live on turnaround, the transcode queue is often the difference between a highlight that ships tonight and one that ships tomorrow. Any serious plan for AI on a video library has to answer for that processing time and cost up front, not discover it later.

And that assumes the media is even reachable. A large share of real archives still lives on LTO tape or sits locked on local hard drives on shelves and under desks, which no cloud service or chat assistant can see at all. Getting value from those libraries starts with restoring and centralizing the media. The good news is that the compute side no longer has to be yours: indexing in the cloud means you are not buying racks of high-end GPUs to make an archive searchable, and for most teams that hardware bill was never going to be approved anyway. The practical pattern is to keep camera originals where they are, index once in the cloud from proxies, and let every search after that hit the index instead of the heavy files.

What about live video and broadcast feeds?

Everything so far assumes the footage is finished and sitting in a file, and for news and sports that is only half the story. The other half is happening right now: a live feed from a match, a press conference, a breaking story coming in from the field. Live video is the hardest version of this problem. The file is still growing while you work, the indexing has to keep pace with the event, and the deadline is not tonight, it is the next commercial break. A social team waiting for the final whistle to start searching has already lost, because the highlight had its audience 40 minutes ago.

This is why chat assistants are furthest of all from broadcast workflows. A model that samples an uploaded file has no concept of a feed that has no end, and a 5-minute or 1-hour cap is a rounding error against a match day. Purpose-built video AI is heading there, Imaginario included, with live streaming workflows on our roadmap, because the same shot-level understanding that makes an archive searchable is what makes a live event clippable while it is still on air. If your team lives on turnaround, this is the capability to watch.

What does an AI that can watch videos look like?

Start with understanding. Imaginario AI indexes video across visuals, speech, sound and people at shot level, so a library becomes searchable in plain language: “the drone shot of the coastline at sunset”, “the part where she explains pricing”, or every scene a particular speaker appears in. Indexing is only the record. The platform also understands the context around every scene and every person who appears, and that understanding layer is already paired with an intelligence layer that is not tied to any one hosting platform, MAM system or LLM. That is the layer that extracts insights from your library and repurposes and restructures content at scale, not just finds it.

It also meets your media where it lives. Imaginario connects to any MAM, DAM or storage system, whether your content sits in the cloud, on-prem or in a hybrid of the two: Amazon S3, Microsoft Azure, Google Cloud Storage, Google Drive, Dropbox, Box, Frame.io, LucidLink, Sony Ci, Backblaze and Wasabi in the cloud, Avid NEXIS, EditShare, Facilis and Quantum StorNext on-prem, MAM platforms like Iconik, CatDV, Dalet Flex, eMAM and Mimir, and transfer routes like Signiant Media Shuttle and SFTP. Transcription covers 100+ languages with automatic detection, and exports go to Premiere Pro, DaVinci Resolve and Avid Media Composer. Teams at Warner Bros. Discovery and Universal Pictures cut search and clipping time by 50 to 80 percent this way.

Search is where it starts, not where it ends. The shift we are building toward, and the reason we talk about a system of action rather than a system of record, is that understanding footage is only useful if it turns into output. That is what StoryLab does: give it a creative brief and its agents search your library, script a narrative and time the cuts, returning a draft story built from real clips in your library within minutes, in 16:9, 9:16 or 1:1, ready to refine and export. The same understanding also flows out through the API and back into your MAM as metadata, clips and collections, so the insights show up on every surface your team works in rather than one more app to check.

The two kinds of tools are complements, not rivals. ChatGPT and Claude are the right tools for working with the words around your footage, summaries, scripts, social copy. A video intelligence platform is the right tool when the footage is yours, there is a lot of it, and finding the moment, or turning it into a story, is the job. Ask which problem you actually have.

Frequently asked questions

If your team’s question is “where is that moment in our footage, and how fast can it become a story”, that is exactly what Imaginario AI was built to answer. Explore AI video search, see StoryLab, or get a demo.

BACKGROUND

Cineverse is a streaming and content distribution company that offers a vast library of over 60,000 film and TV titles across various genres like anime, Asian entertainment, faith and family, horror, and indie films.

In August 2023, Cineverse teamed up with Imaginario AI to accelerate its search and social video creation process. The partnership has empowered their marketing team to efficiently craft engaging clips from its extensive movie catalog, without the need for metadata and manual tagging.

Imaginario AI has significantly reduced the workload for technical and data teams in defining, enriching and maintaining metadata, while enhancing the value of their existing assets.

CHALLENGE

Before implementing Imaginario AI, Cineverse faced significant challenges in efficiently managing their social media marketing efforts for new movie releases, holiday stunts and more. The marketing team had to manually watch entire movies, select specific scenes, take note of time codes, and then edit them on Adobe Premiere for social channels, including Facebook, Instagram, TikTok, Pinterest, Twitter, and YouTube. This process not only consumed valuable time but also hindered their ability to explore diverse content, create compilations and maintain consistency.

SOLUTION

After integrating Imaginario AI into their workflow, Cineverse underwent a significant transformation in their video creation process. The platform provided a powerful tool for efficiently extracting movie clips based on keywords, topics, visual searches, and emotions.

This streamlined approach enabled the marketing team to quickly discover perfect moments within films while saving 75% in rewatching time and with more content variety.

Additionally, Imaginario AI’s capability to categorize clips into collections significantly streamlined virtual collaboration among team members, further boosting workflow efficiency.

With Imaginario AI, Cineverse experienced remarkable social engagement with several posts surpassing a million views. The ability to uncover dynamic moments that resonate with niche audiences and significantly boost engagement is a key strength that Imaginario AI has brought to Cineverse.

Chapterization. Imaginario AI breaks down all your long-form videos into snackable chapters including a title and short summary.

Social Media Clipping. Resize, add captions and brand your videos for TikTok, Instagram Reels and YouTube Shorts.

A CLEAR RETURN ON INVESTMENT

The adoption of Imaginario AI yielded tangible benefits for Cineverse, Including:

• Significant Time Savings: The use of Imaginario AI drastically reduced the time to find and convert long-form videos by 75%, allowing the marketing team to focus on the higher-quality compilations and more creative tasks.

• Ability to better monetize their archive: With Imaginario AI, Cineverse is strategically monetizing its vast catalog by unearthing previously untapped, highly engaging content.

• Enhanced Content Variety: By efficiently extracting moments from a diverse range of movies using various speech and visual queries, Cineverse expanded its content offerings, catering to various niche audiences and genres across social media platforms.

• Improved Collaboration: Imaginario AI’s asset management features facilitated smooth virtual collaboration between team members, ensuring efficient communication and streamlined workflows.

• Increased Engagement: The dynamic clips curated using Imaginario AI garnered millions of views and high engagement on social media platforms, driving brand awareness and audience interaction.

• Optimized Marketing Strategy: With access to a vast library of curated clips, Cineverse was able to make its library searchable without the need for metadata. This allowed for the implementation of targeted strategies aligned with specific themes, holidays, and promotional stunts, resulting in higher campaign effectiveness.

KEY FACTS

• Company: Cineverse, streaming and content distribution, library of 60,000+ film and TV titles
• Team using Imaginario AI: in-house marketing and social team
• Use case: AI video search, clip curation and social media clipping from finished content, with no metadata or manual tagging
• Workflow replaced: rewatching full films, noting time codes and editing each clip in Adobe Premiere, 12 to 24 hours of rewatching per week
• Results: 75% reduction in search and clipping time, higher content variety across niche genres, posts passing one million views, better monetization of the archive
• Customer since: August 2023

Read next: How Warner Bros. Discovery EMEA cut search and clipping time by almost 70% across Cartoon Network and Boomerang.

FREQUENTLY ASKED QUESTIONS

Every video team knows the feeling. You have thousands of hours of video sitting in libraries and thousands of raw shots, and somewhere in that mountain of content is the perfect story waiting to be told. But finding the right moments, assembling a narrative, and producing a polished cut takes days of manual work.

Industry research puts the cost at $100 to $500 per clip, with 80% of video archives sitting untouched as “dark data.”

Today, we’re changing that with StoryLab, our new agentic story creation feature now available on the Imaginario AI platform.

What Is StoryLab?

StoryLab lets you go from a creative brief to a fully assembled multi-video story in minutes, not days. Describe what you’re looking for, set your preferences for mood, genre, platform, and length, and let our AI agents do the heavy lifting.

Under the hood, StoryLab runs a multi-agent pipeline:

• A Search Agent scours your indexed video library using Imaginario’s 14 understanding layers and shot-level intelligence.
• A Scripting Agent builds a narrative structure from the best matching moments.
• A Timing Agent assembles the final cut with precise edit points, ready for review and refinement.

The result is a story you can preview, reorder, and fine-tune, all within a single workflow.

Built for Real-World Content Teams

StoryLab isn’t a toy demo. It’s built on the same multimodal intelligence layer trusted by teams at Warner Bros. Discovery, Universal Pictures, Sony Music, World Central Kitchen and many world-class video teams.

Warner Bros. Discovery cut video search time by 80% using Imaginario. Universal Pictures described the experience as “compressing ideation from days to minutes.” StoryLab takes that speed and applies it to the entire story creation process, from discovery through assembly.

How It Works

Getting started is simple:

1. Open a new StoryLab project > Multi-video Story
2. Fill out a Creative Brief with your source videos, target genre, mood, and any specific moments you want to capture, then hit Create Story.
3. Within minutes, you’ll have a draft narrative built from real clips in your library (note: subtitles and speaker tracking coming in 4 weeks)
4. You can then export it to Premiere, Da Vinci, or download locally.

From there, you can drag to reorder clips, adjust timing, and iterate until the story is exactly right.

See It Live at NAB and MPTS 2026

We’re premiering StoryLab at NAB Show 2026 in Las Vegas, April 19-22, and at MPTS 2026 in London, May 13th-14th. Visit us at Booth N1262 in the North Hall at NAB Booth K41 for a live demo and see how your team can turn archive footage into compelling stories faster than ever. You can book some time with us here.

Can’t make it to NAB or MPTS? Get in touch to schedule a private demo.

StoryLab is available now on the Imaginario AI platform (beta access). Contact us to learn more about adding it to your workflow.

‘Constrained agency’ is becoming the rule in Enterprise: AI handles the heavy lifting, while humans make the decisions. Context is the next challenge.

This is the second of a three-part post about a few key insights and findings that I came across when attending the Digital Production Partnership (DPP) Leaders Briefing 2025 in London. The DPP is one of the most important trade associations in the media industry that brings together senior executives from major media organizations. More than 1,000 attendees came to this conference held on November 18 and 19.

The transformation from AI demos to production systems is underway across media and entertainment. But the more interesting story isn’t that AI has arrived, it’s where it’s actually working, where it’s struggling, and what infrastructure changes are required to unlock the next wave of value.

Where AI Is Delivering Real Value in the Media Enterprise space

Use Case 1: Ingest and Metadata. The Highest-Leverage Point for Automation

High-quality and multimodal metadata at point of ingestion lies at the heart of building intelligent and robust AI systems further downstream in the media supply chain.

The standout finding from recent industry surveys such as the DPP’s CEO and CTO surveys is that metadata generation and enrichment, particularly speech-to-text and automated speech recognition, has crossed the adoption chasm. More than 80% of organizations are either encouraging or actively implementing these capabilities.

The logic is sound. The oldest rule in data engineering applies: garbage in, garbage out. If your metadata is incomplete, inconsistent, or wrong, everything downstream suffers. Search breaks. Rights management fails. Compliance becomes manual. Smart organizations have realized that the front door of the supply chain is the highest-leverage point for automation.

The DPP’s Media AI Radar 2026, based on industry interviews, forecasts higher adoption of AI in ideation, planning, analytics, commissioning, compliance, video marketing, advertising, and ingest/logging.

The more sophisticated implementations involve what’s being called “agentic orchestration”: AI systems that don’t just transcribe content but actively monitor ingestion workflows for anomalies.

For example, a file arrives with metadata that doesn’t match expected formats or the level of depth and accuracy required. Maybe the localization markers are wrong, or the language code contradicts the filename. Traditional workflows would let that error flow downstream until a human spotted it.

With agentic orchestration applied in Q/C, the system flags the mismatch immediately, provides context, and prompts investigation. A person remains in charge, operating with better information and recommendations earlier in the process.

However, as we will see later in this post, agentic AI doesn’t just need highly accurate metadata but also ways to access content and data silos in a more fluent, robust, and secure way. This remains a challenge for large organizations.

Use Case 2: Ingest and Metadata. The Highest-Leverage Point for Automation

AI is heavily disrupting the localization industry. Yet, the media industry continues to maintain a high bar for quality dubbing and audio descriptions. Humans and AI together produce the best output.

Subtitling, captions, and synthetic dubbing are seeing strong adoption (especially the first two), but with an important caveat: only 14% of teams find auto-generated captions “fully usable” without human review, and 75% cite timing and sync issues as their primary complaint.

Bad subtitles aren’t just annoying, they’re trust-destroying. The pattern emerging is AI-assisted drafting plus mandatory human review. Speech-to-text models generate initial captions. LLMs detect sync problems and propose corrections. Linguistic experts review the final files. The AI handles grunt work; humans ensure quality.

Use Case 3: Marketing and Promotion – Dramatic Time Savings

Beyond the hype. Automated and semi-automated video repurposing is proving to be a growing revenue pipeline for companies with long-form content, such as those in Sports, and a key way to monetize deep high-quality catalogues.

This is where AI delivers some of the most compelling efficiency gains and this goes beyond flashy Nano Banana text to video generative AI. A single piece of premium long-form content (sports matches, TV episodes, etc) might need dozens of promotional assets tailored for different platforms, audiences, and campaigns.

These are AI tools that can analyze content, identify emotional beats, extract compelling sequences, and generate draft cuts in minutes. What used to take days happens in hours or minutes. This is one of the use cases driving the highest demand for Imaginario AI, and this cuts across media, entertainment, sports, news and even SMB content.

In the case of Imaginario AI, we have seen our Enterprise clients save between 50% and 75% in searching deep catalogues, navigating dailies, and generating social media compilations or single cuts. This is quantifiable ROI.

Use Case 4: Contextual Ad Discovery and Insertions. The Next Frontier in Programmatic Advertising

AI contextual ad discovery understands the nuance of content to place ads in the perfect environment. This enables highly personalized creative that align with a user’s immediate mindset and mood, boosting engagement without relying on personal data.

AI tools can analyze narrative structure, detect scene transitions, and suggest non-intrusive ad slots based on emotional pacing. They can also explain their reasoning, which is critical for creative buy-in. The tools that work don’t make autonomous placement decisions. They suggest options, show reasoning, and make overrides easy.

The Maturity Gap: Where We Actually Are

For all this progress, humility and baby steps are still in order. When surveyed by the DPP about media supply chain maturity, 61% of respondents said “developing” and 36% said “maturing.” Only 3% called it “advanced.” Nobody, not a single respondent, said “leading”.

The vast majority of media companies are still developing their cloud, interoperability and AI capabilities. They expect to reach a maturity stage in three years. Sources: DPP CTO Survey 2025.

Projected three years ahead, roughly half expect to be maturing, a third hope for advanced, and just 3% think the industry will be truly leading.

We’re in the early innings. The teams deploying AI today are pioneers, not late adopters. Most organizations are still figuring out use cases, governance, training, and change management. The technology is ahead of organizational readiness.

Co-Pilot, Not Autopilot: The Design Pattern That Works

The highest gains are seen when humans and AI do what they do best: AI handles data processing, automation of repetitive tasks, and generating drafts. Humans use critical judgement, context, creativity, and empathy.

Across trust, integration, governance, security, and perceived technology maturity, concern levels spike whenever AI autonomy increases. People want smart assistants. They do not want self-driving compliance, QC, or editorial decisions.

The pattern that’s working: constrained agency. AI with enough freedom to do meaningful work, but within boundaries that remain legible and controllable by humans. The level of acceptable autonomy varies by use case. Legal compliance has a higher accuracy bar than generating marketing highlights, but the principle holds.

This isn’t because the technology can’t handle more autonomy. It’s because organizations aren’t ready to trust it, and more importantly, because accountability matters. When something goes wrong, someone has to answer for it. That someone needs to be a human.

As one technology vendor put it perfectly during the DPP Leadership Summit last November in London: “AI handles the heavy lifting; humans handle the decisions.”

The Enterprise Challenge: A Fragmented Context

AI implementations often stall because essential enterprise context is locked in fragmented systems and unstructured media formats like video and audio. Making diverse data accessible is critical insfrastructure work required for scalable AI success.

For AI agents to be effective in the enterprise, they need enterprise context. That context sits in contracts, financial documents, research, marketing assets, meeting notes, conversations, and every other piece of information across the organization. By volume, most of this data is unstructured and remains both on-premises and in the cloud. There is no one-size-fits-all solution, as each organization is at a different stage of digitalization and cloud development.

Your customer information might live in HubSpot. Projects are in Monday. Engineering issues sit in Jira. Financials are in Xero or SAP. Your product catalog exists in a different database and MAM systems. Branding and marketing is scattered across DAMs and creative toolkits like Adobe or Avid.

Enterprise context is highly fragmented and requires bespoke integrations, taxonomies, and adapted schemas that ensure data consistency and make it easier for different systems to understand and exchange data. Without this, even the smartest AI still won’t know your organization and your workflows, therefore providing inaccurate information and potential hallucinations.

This fragmentation is why so many AI implementations stall after promising pilots. The AI works brilliantly on a single data source but struggles when it needs to compose context from multiple systems to answer real business questions.

And there’s another layer of complexity: a massive portion of enterprise context is locked in complex media formats like video, audio, and images that agents can’t natively process. Making these formats truly legible to AI is essential infrastructure work that most organizations haven’t yet addressed. Until this unstructured content becomes structured and searchable, AI agents will operate with significant blind spots.

The only way AI agents will be successful at scale is if they have access to the right information, in the right structure, at the right time, in a secure and well-governed way. AI will expand the use and value of this information by orders of magnitude over time, but only if the infrastructure exists to make it accessible.

MCP and the Agentic Architecture Shift

A single contextual layer to rule all APIs and bespoke integrations in the Era of AI: Model Context Protocol (MCP). Source: Descope.

This fragmentation problem is exactly why the Model Context Protocol (MCP) ecosystem has become strategically important, even with APIs available.

MCP, originally developed by Anthropic and now donated to the Linux Foundation’s new Agentic AI Foundation, provides MCP clients with a universal, open standard for connecting AI applications to external databases, servers, and third-party systems. It’s not just about connecting agents to a system, it’s about composing context from all the systems that collectively hold your enterprise’s cognitive reality.

The numbers tell the story of rapid adoption: over 10,000 active public MCP servers exist today, covering everything from developer tools to Fortune 500 deployments. MCP has been adopted by ChatGPT, Cursor, Gemini, Microsoft Copilot, Visual Studio Code, and other major AI products. Enterprise-grade infrastructure now exists with deployment support from AWS, Cloudflare, Google Cloud, and Microsoft Azure. This will likely become the underlying glue of AI.

What we’re seeing emerge is an evolution from atomized MCPs (reflecting individual APIs) to aggregated, orchestrated context layers that can assemble meaningful context on demand. The winners in the AI-enabled enterprise won’t just be the platforms that secure their own data well. They’ll be the ones that embrace composability and make their context genuinely AI-visible.

The agentic future requires infrastructure that makes context available, structured, and secure across the entire SaaS ecosystem. That’s the architecture shift everyone is building toward.

What This Means for the Next Wave

If you’re building AI Enterprise tools for media or implementing them in your organization:

• Prioritize the ‘front door’. The highest-leverage automation opportunities are at ingest and metadata generation at scale. Get that right, and value compounds downstream. Note: Imaginario AI provides labeled and vector-based contextual understanding and can help your organization in this part of the supply chain.
• Design for transparency. If your AI makes recommendations, show your reasoning. Black boxes don’t scale where trust must be earned through explainability.
• Assume human review first. The workflows that work (today) are ones where AI drafts and humans approve. Plan for that from the start, optimize for automation later.
• Solve the fragmentation problem. Invest in infrastructure that makes enterprise context composable, secure, and AI-visible across your SaaS ecosystem. MCP adoption is accelerating for good reason. APIs are rapidly adapting to support MCP and Imaginario AI is not the exception.
• Build for constrained agency. Give your AI enough freedom to do real work, but keep it within boundaries humans can understand and control.

The transformation is messy, uneven, and slower than the hype cycle suggests. But it’s happening. AI is moving from the margins to the mainstream, from idiotic hype to efficient supply chains.

We’re not at full automation for most workflows, as context and personalization at scale still need to be completely solved. However, we’re at something more mundane and more valuable: AI as co-pilot, lifting cognitive weight and letting humans focus on judgment and creativity.

The infrastructure work, making enterprise data composable, structured, and AI-visible, is less glamorous than demos of agents completing complex tasks autonomously. But it’s the foundation everything else depends on. The organizations that invest in this plumbing now will be the ones positioned to capture value as agentic capabilities mature.

That might not make for breathless keynote presentations. But it’s the future that’s actually getting built.leaders in the first place (hint: premium content).

About Imaginario.ai
Backed by Techstars, Comcast, and NVIDIA Inception, Imaginario AI helps media companies turn massive volumes of footage into searchable, discoverable, and editable content. Its Cetus AI engine combines speech, vision, and multimodal semantic understanding to deliver indexing, simplified smart search, automated highlight generation, and intelligent editing tools.

Frequently asked questions

Every year we get a new “state of social media” report from Pew Research and every year the headlines scream disruption, innovation, a new platform rising to dethrone the old guard. But if you read the latest Pew Research data carefully (and you should), a very different story emerges: Social media isn’t exploding. It’s settling.

• YouTube sits at 84%
• Facebook at 71%
• Instagram stuck at 50%
• TikTok hovering around 37%

These numbers barely move year to year. In fact, Facebook has essentially been flat since 2016. YouTube gained a whole two percentage points since 2023; barely statistical noise.We’re not witnessing a revolution.

We’re watching a plateau.

And when you zoom into age groups, the picture becomes even clearer. Among 18–29 year olds, the group that supposedly defines the future of culture, almost everyone uses everything. Around 95% use YouTube. 80% use Instagram. 68% use Facebook. 63% use TikTok. 58% use Snapchat.

This is not a battlefield with a single winner.

It’s a crowded food court. People wander between stalls, nibble from each plate, and commit to none.

Meanwhile BeReal, remember that hype cycle? Sits at 3% adoption. Proof that novelty without a real use case burns out faster than the venture money funding it.

All Platforms, Same Features

Pew’s dataset confirms something we’ve all felt intuitively: every platform now looks like a remix of every other platform.

Feeds. Stories. Short-form video. Messaging. Shopping.
Algorithms pushing content you didn’t ask for, trying to keep you inside their world just a little longer.

The result? Commodification.

When the feature sets converge, the differences between platforms stop being strategic and start being aesthetic.

YouTube keeps winning because it solves an actual problem: hosting, archiving, and discovering long-form video (and Imaginario AI can help you transform those YouTubes for other platforms). That’s hard to replicate.

TikTok’s insane rise: Mostly novelty and a powerful recommendation loop both of which competitors have now cloned.

Facebook? The data is brutally consistent: it hasn’t grown in almost a decade, despite owning half the social internet. More apps don’t mean more engagement. They mean more fragmentation.

Even among older adults, supposedly the “offline” generation, YouTube still has around 64% adoption. That’s higher than TikTok gets with any age group. A quiet reminder that usefulness > trendiness.

What This Means for Anyone Trying to Reach an Audience

We’ve been conditioned to chase the “next platform,” the “next format,” the “next wave.” And for a long time, that made sense, the land grab was real.

But now?
The land is fully owned. The borders have been drawn.

Audience behaviour has crystallized around four core modes:

YouTube → sit back, learn, watch something meaningful.
Facebook → talk to your existing community, stay in touch.
Instagram → aesthetic discovery, curated-self world-building.
TikTok → quick dopamine hits, novelty, entertainment in fast motion.

Marketers (and founders, creators, journalists, anyone who publishes anything) don’t win by being everywhere. They win by understanding why people are where they are, by creating distinctive niche content, and tailoring the message accordingly.

YouTube users want to learn or be entertained.
Facebook users want to talk to someone they know.
Instagram users want things that look good.
TikTok users want to feel something immediately.

Once you internalize this, something shifts: you stop chasing platforms and start designing stories.

Because here’s the real truth in all this data: The platforms are no longer the differentiators. You are.

What you say, how you say it, how well it resonates, that’s the new advantage.
Platform choice is basic hygiene.
Message-market fit is the edge.

About Imaginario.ai
Backed by Techstars, Comcast, and NVIDIA Inception, Imaginario AI helps media companies turn massive volumes of footage into searchable, discoverable, and editable content. Its Cetus AI engine combines speech, vision, and multimodal semantic understanding to deliver indexing, simplified smart search, automated highlight generation, and intelligent editing tools.

The DPP Leaders’ Briefing 2025 gathered more than 1,000 attendees and key decision-makers from over 30 major media organisations. Credit: DPP

This is the first of a three-part post about a few key insights and findings that I came across when attending the Digital Production Partnership (DPP) Leaders Briefing 2025 in London. The DPP is one of the most important trade associations in the media industry that brings together senior executives from major media organizations. More than 1,000 attendees came to this conference held on November 18 and 19.

Across the two days of the DPP Leaders’ Briefing, you could almost forget who worked where. Once people stopped talking about their logo and started talking about the technical and management problems they were facing, the patterns lined up across major media, news, broadcast, and entertainment organizations.

Key questions that emerged across most presentations:

• How and where do we use AI in production rather than in slideware?
• How can we trust AI tooling and agentic solutions while more decisions are assisted or automated?
• Do we have the right integrations and security mechanisms in place?
• How do we turn deep archives, dailies, and live content into something companies can leverage to reduce costs, increase revenue, and boost quality?
• How do we avoid vendor lock-in while the technology continues to advance at a breakneck pace?
• How does our tech stack need to adapt to accommodate this level of flexibility?
• How do we survive the new content and distribution economics of an AI-driven internet and aggressive hyperscalers?

If you needed a one-line summary of the conference, it is this:

“Despite the high priorities for media companies in AI implementation and automation, the reality is that so far for many players internal business engagement and operational effectiveness have been far more challenging than actual tech delivery”

Another risk discussed during the event was relying on legacy workflows to define the future of work in media.

As an executive from RTL said:

“If you narrow it to KPIs too early you’ll be optimizing what you already have”.

In other words, media enterprises will not be investing in learning what they need to learn and building new workflows from scratch.

Uriah Smith’s 1899 “Horsey Horseless”, a car fitted with a fake horse head, is the ultimate cautionary tale of “optimizing what you already have” rather than trusting a completely new workflow.

To innovate faster, partnerships and collaboration were mentioned as key by C-Level executives. However, organizational challenges remain in the Enterprise space.

For CEOs media tech partnerships are key to be successful. The DPP provided a space to foster those relationships. Credit: Imaginario AI.

If there was one thing CTOs agreed on, it is that technology is now the easy bit, but people and culture are not.

A DPP chart on leadership challenges placed the biggest challenges on organizational dynamics:

• 75% of CTOs said their biggest issue is change management. Not a specific technology, not a security threat, but the basic job of getting people to work in new ways.
• Securing cross-business engagement came next at 50%.
• Technology project delivery only showed up in the middle.
• Recruiting and retaining talent, securing executive buy-in for tech investment, and keeping employees motivated followed behind.
• Improving diversity within the tech function did appear on the list but was rated by just 9% as their single biggest challenge.

The DPP’s CTO Survey 2025. Management and cross-business engagement remains the main barrier for innovation and AI adoption.

During the conference, plenty of commercial media companies, in particular, said that they need suppliers to act as collaborators rather than just traditional providers.

This was confirmed by the DPP CTO survey: the majority chose “collaborative” as their preferred role for vendors, with only a small minority wanting them to stay “supportive.” Such groups, like Public Service Broadcasters (PSBs) and non-profit organizations, leaned more toward “supportive” than “collaborative,” but very few wanted suppliers to be hands-off in an arm’s-length posture.

The DPP’s CEO Survey 2025. Technical partnerships are a top priority for leaders in this space with 85% stating they are being proactive or opportunistic. Credit: DPP.

The CEO survey backed that up with a clean headline: 85% of media tech CEOs say they prioritize partnerships. These ecosystems are evolving from simple integrations to “deep collaborations aimed at solving shared customer challenges.” In plain language, everybody realizes they cannot solve this alone at the current pace of innovation in AI.

However, the mood around AI across the event was at the same time strangely cautious for a technology that dominated the conference agenda. Several people warned about “AI FOMO” quietly driving bad decisions.

One NRK executive even said, without much sugar-coating, “Don’t fall into vendor lock-in, I’m not your bitch.” The line landed partly as a joke, but the sentiment was serious.

Organizations want partners who are honest about what they can really deliver, who will give directional pricing and modular solutions early rather than hiding behind all-encompassing solutions and five-year contracts, and who are willing to integrate with rival systems when that is what the customer needs.

AI adoption playbooks that are working in Enterprise

Groupe TF1, with Olivier Penin as Director of Innovation, remains as one of the examples of successful leadership implementing AI at scale. 75 PoCs last year; 20 use cases industrialized including AI native content studios. Credit: DPP.

Advice given during the conference: Start small, with one video workflow, one team, and one function. Use your own content, style guides, and rights data, so the solution reflects your reality. Pick problems where gains are clearly measurable: time saved, errors reduced, fewer manual touches, faster time to air. Keep humans in charge of outcomes.

None of that is glamorous, but it is where the real progress is happening. With Imaginario AI, this is exactly how we have been driving adoption of our solutions: starting small with a limited pilot in a specific function and landing wider corporate-level MSAs later.

Another golden nugget from the event for international media tech vendors: the DPP’s CTO survey mentioned that commercial media companies based in the US are more optimistic about the future (and therefore willing to invest more and experiment) than public service organizations (most of them based in Europe). However, PSBs expect a budget increase next year.

Where this leaves us

Pulling all of these threads together, you get a picture of an industry leaving its AI demo phase and entering the infrastructure phase.

AI and cloud are no longer bolt-ons. They are becoming part of the media supply chain from ingest and logging through post, archive, versioning, localization, compliance, marketing, scheduling, and consumption.

At the same time, most CTOs freely admitted the industry is only at a “developing” stage of maturity. The next three years are about turning experiments into dependable systems.

… and the organizations that seem to be making real progress shared a few traits during the event:

• They treat AI as operational technology, not as an innovation showpiece. As one speaker said: “AI is not the strategy, it’s a means to an end.”
• They keep humans clearly in charge for decision-making and supervision, with AI in a constrained co-pilot role… at least for now.
• They recognize that rights, data, and AI intelligence (in that order) will unlock true value. They invest in in-video metadata, not just asset-level metadata and cloud infrastructure.
• They take change management, specialist partnerships, and world-class UX design as seriously as they take models and buying GPUs.
• Those adapting faster are getting closer to their core strengths in premium content production (e.g., TF1, RTL) rather than trying to build all AI capabilities themselves.

Partners with all-encompassing, monolithic solutions slow companies down rather than help them get to the next wave of video intelligence and automation.

On the contrary, many companies like Freemantle mentioned that partnering with smaller tech vendors normally provided that level of personalized and high-touch support required by Enterprises. Those specializing in AI, need to be API-first, modular, and that serve core areas such as metadata generation/enrichment, model fine-tuning, and training based on the client’s data and context are the perfect co-pilots in this sprint to innovate.

For those of us building AI-enabling solutions and platforms in this space, the engineering and UX bar is high and goes beyond “we have a clever model.”

We need to help enterprises build their own capabilities on their own terms and focusing on specific use cases. Help them with specific AI razor blades with native integrations and modularity, rather than vendor lock-in with middle-of-the-road solutions.

Other challenges vendors need to understand: clients want automation with control, intelligence with explainability, cloud scale with sovereignty (many times fully on-prem), and AI with genuinely human-centered UX. All with full transparency.

The honest reflection of the industry and the openness to collaborate is what rreally defined the DPP Leaders’ Briefing this year.

Behind all the fear slides and maturity charts, you could feel something else: the sense that the industry has stopped arguing about whether this transformation is happening and has started implementing how to do it without breaking the core strengths that made media companies leaders in the first place (hint: premium content).

To their benefit, content is still king and the time to catch the AI wave is now.


About Imaginario.ai
Backed by Techstars, Comcast, and NVIDIA Inception, Imaginario AI helps media companies turn massive volumes of footage into searchable, discoverable, and editable content. Its Cetus AI engine combines speech, vision, and multimodal semantic understanding to deliver indexing, simplified smart search, automated highlight generation, and intelligent editing tools.

LONDON, 8th September 2025 — At Imaginario AI, we know that seamless video workflows depend on meeting teams where they already work. That’s why, in addition to our existing integrations with AWS S3, Wasabi, Google Drive, Dropbox, Adobe Premiere, and DaVinci Resolve, we’re excited to announce a major expansion of our partner ecosystem.

Through our new partnership with Ortana Media Group announced last month, Imaginario AI now supports a broader set of cloud storage, media asset management, editing systems, and social platforms, giving creative teams of all sizes greater flexibility to ingest, index, and export to editing suites and social media platforms.

New Cloud Storage & MAM Integrations

With Ortana’s Cubix Platform connected to our Cetus AI engine, Imaginario now works with leading storage and media management solutions, including:

• Azure – Microsoft’s enterprise cloud platform for scalable storage and compute.
• Google Cloud – Google’s infrastructure for media storage, AI, and collaboration.
• Backblaze B2 – Cost-efficient object storage for archiving large video libraries.
• StorJ – Decentralized, encrypted cloud storage with high resilience.
• LucidLink – Cloud file system that streams large media files instantly.
• Iconik – Cloud MAM for organizing assets globally.
• Ross – Broadcast workflow and media asset management solutions.
• Ci Media Cloud (Sony) – Secure cloud collaboration for sharing and reviewing media.

Connected to more Editing Systems and Social Media Platforms

Our existing integrations with Adobe Premiere and DaVinci Resolve are now joined by Avid Media Composer, a cornerstone of professional editing for broadcast and feature film. This expansion ensures that editors can access AI-driven enrichment, search, and automation within the platforms they know best.

• Adobe Premiere Pro – Industry-standard editor for creative and marketing workflows.
• DaVinci Resolve – Professional editing and color-grading platform for film and TV.
• Avid Media Composer – Broadcast-grade editor

Content today doesn’t just end in the edit suite, it needs to reach audiences across multiple channels quickly and consistently. Imaginario now supports direct publishing to:

• Facebook – Social video distribution for broad community engagement.
• Instagram – Short-form, Reels, and Stories video publishing.
• TikTok – Vertical, short-form platform for viral and algorithm-driven reach.
• Vimeo – Professional hosting and review platform for creatives.
• X (Twitter) – Social and news platform for fast video distribution.
• YouTube – The largest global video platform for reach and monetization.

“For many of our customers, workflows aren’t purely in the cloud; they’re hybrid, or tied to on-prem infrastructure that has been built up over years. What excites us about this partnership is the ability to bridge those environments seamlessly. With Ortana’s orchestration and AI curation app and engines, teams can take advantage of AI-driven indexing and automation without disrupting the way they already store, edit, or deliver content.”

Jose Puga, CEO of Imaginario.ai

Why This Matters

These new integrations expand the reach and flexibility of Imaginario AI across every stage of the media lifecycle.

• End-to-end flexibility – Hybrid, on-prem, and cloud-native infrastructures are all supported. Live streaming workflows coming soon!
• Editorial efficiency – Editors and marketing teams can stay within their preferred environment while accessing AI insights.
• Faster distribution – Direct social publishing accelerates the journey from raw footage to audience-ready content.
• AI-powered intelligence – Video becomes instantly searchable, discoverable, and actionable across speech, visuals, and sound, without disrupting your workflows. It meets you where you are.

Our partnership with Ortana Media Group represents an important step in building a truly connected media ecosystem. Together, we are making workflows smarter, more efficient, and more adaptable, helping teams deliver content at the speed and scale today’s audiences expect.


About Imaginario.ai
Backed by Techstars, Comcast, and NVIDIA Inception, Imaginario AI helps media companies turn massive volumes of footage into searchable, discoverable, and editable content. Its Cetus AI engine combines speech, vision, and multimodal semantic understanding to deliver indexing, simplified smart search, automated highlight generation, and intelligent editing tools.

VISIT US AT IBC (HALL 7 – 7.P46 and 7.P48) – September 11-15 in Amsterdam

About Ortana Media Group
Ortana develops scalable and modular media orchestration solutions. Its flagship platform, Cubix, enables organisations to manage content workflows across cloud and on-premise environments, providing full visibility and control from ingest to distribution.

VISIT US AT IBC IN AMSTERDAM (HALL 1 – 1.C37H) – September 11-15 in Amsterdam

For more information, please contact:

Jose M. Puga
CEO
Imaginario AI
support@imaginario.ai

LONDON, 14th August 2025 — Ortana Media Group, creators of the Cubix Media Aware Workflow Engine (MAWE), today announced a strategic technology integration with Imaginario.ai, the company behind the Cetus AI engine, a next-generation platform for multimodal video understanding, smart search, and AI-powered editing.This collaboration combines Ortana’s robust media orchestration and automation layer with Imaginario’s AI-native infrastructure, empowering media organisations to turn raw video into enriched, discoverable, and production-ready content with unprecedented speed and scale.

A Leap Forward in Video Intelligence & Orchestration

Designed for streaming services, post houses, broadcasters, and corporate marketing teams, the integration delivers a future-ready solution that:

1. High-accuracy multimodal indexing at scale – Powered by Imaginario’s proprietary Cetus engine, the system performs advanced scene understanding, shot detection, facial analysis, object recognition, speech-to-text, ambient sound and SFX detection, logo recognition, OCR, image-to-video matching, and multilingual transcription.

2. Automated end-to-end workflows – Through Ortana’s Cubix MAWE, the integration orchestrates ingest, AI tagging, metadata enrichment, transcoding, and asset movement across hybrid or cloud environments — reducing manual effort and accelerating turnaround.

3. Deep semantic search and dynamic discovery – Enables users to quickly find and repurpose scenes or moments, regardless of archive size, file format, or content type.

4. API-native and modular by design – Offers seamless integration into existing MAM/PAM ecosystems or as a standalone solution — supporting flexible, cloud-agnostic deployments.

5. Explainable AI with scene-level captioning – Generates descriptive scene captions and match scoring to clarify how content is indexed — enhancing transparency, user trust, and tagging accuracy.

Unlocking the Value of Video at Scale

This joint solution enables post-production, marketing, and archive teams to spend less time rewatching content and more time creating. Whether for programmatic content creation, social repackaging, contextual advertising, or large-scale archive revitalisation, teams can now:

– Search video by who, what, where, when, and even why, using multimodal prompts.
– Automatically surface the best moments for highlights, trailers, ad inventory, and shorts, all within existing workflows.
– Push enriched assets directly into cloud storage and post-production tools like Adobe Premiere or DaVinci Resolve.

“We’re thrilled to integrate Imaginario.ai’s technology with our Cubix platform. Their AI engine unlocks a new layer of intelligence for our users, enabling smarter automation and better content insights — all seamlessly tied into our orchestration layer. Together, we’re making AI a practical reality in daily media operations.”

Simon Harper, Channel & Partner Manager at Ortana Media Group

“Ortana has built one of the most flexible media infrastructure platforms in the industry. Pairing that with our Cetus AI engine gives creative teams a powerful way to move faster, not by adding more headcount, but by letting AI do the heavy lifting behind the scenes.”

Abdelhak Loukkal, CTO of Imaginario.ai

The integration is available immediately and supported across Cubix Yunify, Appliance, Halo, and Connect. It is already being evaluated by several broadcasters and content providers in Europe and North America.


About Imaginario.ai
Backed by Techstars, Comcast, and NVIDIA Inception, Imaginario AI helps media companies turn massive volumes of footage into searchable, discoverable, and editable content. Its Cetus AI engine combines speech, vision, and multimodal semantic understanding to deliver indexing, simplified smart search, automated highlight generation, and intelligent editing tools.

VISIT US AT IBC (HALL 7 – 7.P46 and 7.P48) – September 11-15 in Amsterdam

About Ortana Media Group
Ortana develops scalable and modular media orchestration solutions. Its flagship platform, Cubix, enables organisations to manage content workflows across cloud and on-premise environments, providing full visibility and control from ingest to distribution.

VISIT US AT IBC IN AMSTERDAM (HALL 1 – 1.C37H) – September 11-15 in Amsterdam

For more information, please contact:

Jose M. Puga
CEO
Imaginario AI
support@imaginario.ai