Video Intelligence is Now Native to the Modern Encoding Pipeline

About Quencode

Qencode is on a mission to democratize world-class video technology, making it simple for anyone to turn their idea into the next great video experience. Its full-stack video platform gives engineering teams everything they need to build and scale video products through an integrated platform with unified APIs spanning transcoding, live streaming, storage, content delivery, player, and analytics. Qencode's AI-powered optimization works seamlessly with your existing codecs, reducing file sizes by up to 60% without sacrificing quality. Based in Los Angeles, it empowers media companies to focus less on infrastructure and more on the things that truly make them great.

The Stakes Just Got Higher

Every successful video platform is encoding more videos in 2026 than ever before, and the pace is only accelerating. AI-generated content alone is reshaping how much footage moves through transcoding pipelines every day.

Encoding has also gotten significantly smarter. With ML-driven compression, automatic captions and translations, smart cropping and thumbnails, and high-quality AI upscaling, the cost and quality curves of video experience and delivery have been collapsing. The output of a modern encoding pipeline is smaller, sharper, and more economical to ship than ever before.

It is also still semantically opaque. A platform can encode ten thousand hours of 4K footage overnight, and still have no idea what's in any of it until someone watches it.

That gap is currently the most expensive inefficiency in the stack. As content volumes accelerate, the cost of leaving videos "un-understood" keeps compounding. The platforms winning today make their videos searchable, analyzable, and understood immediately after they finish processing them.

Qencode has been dealing with these challenges directly, and they finally solved them by embedding TwelveLabs directly into the encoding pipeline.

The Biggest Gap in AI-Native Video Platforms

Before the TwelveLabs integration, Qencode had spent years building artificial intelligence into every part of their encoding stack. ML-powered encoding makes files 60% smaller at matched VMAF level, adapting settings to content type and treating sports, animation, UGC, and conference recordings as fundamentally different encoding challenges. Auto-Subtitles, speech-to-text across 70+ source languages with translation into 100+ target languages, AI-Detection, AI Upscaling, Smart Cropping, and Smart Thumbnails all run as outputs of the same job. Encoding has become smarter than ever, but until recently, one of the most fundamental intelligence features was still missing.

The encoder knew the difference between the best codec settings for a sports clip and a conference recording. It knew how to generate multilingual captions, crop intelligently for vertical formats, and surface representative thumbnails. It did not know which scene contained the winning goal, the quote that made the keynote viral, or where to find the specific moment a viewer needs to see right now.

The Qencode platform was AI-native at the pipeline layer, but completely clueless at the comprehension layer. When a social team needs a specific emotional reaction from a library of broadcast footage, they don't search, they scrub, clip by clip, episode by episode. When a compliance team needs to surface brand-safety violations across thousands of hours of encoded content, they can't find them without meticulously watching everything.

Every hour a video spends waiting to be understood is an hour it can't be used. Putting more people to process this manually still adds a tremendous amount of latency, cost, and failure points that prevent content from flowing smoothly from the operations team to live on the platform. This is a foundational limitation of traditional infrastructure at scale, and it is increasingly becoming more expensive.

What Qencode Built, and Why It Changes the Equation

Qencode already handled some of the most challenging encoding problems: highly efficient encoding at scale, AI-powered codec optimization, adaptive bitrate packaging, and integration of most of the state-of-the-art AI features available across large video platforms today. What they added was the layer that makes all of that content intelligible, searchable, and actionable.

By embedding TwelveLabs' video understanding models directly into the Qencode platform, customers gain video intelligence as an additional output in their current encoding process.

Encode and understand, at the same time. Video enters the Qencode pipeline through the existing API with encoding, video file optimization, and adaptive packaging happening as usual. In parallel, TwelveLabs' Marengo model processes the same content and generates multimodal embeddings that capture what's visible, what's being said, what's written on screen, and how it all fits together. This is a fundamentally different approach from services that do frame-by-frame image recognition or audio transcription bolted onto a vision model. For Qencode, this was the first model that had a unified understanding of video as video.

Search in plain language. After encoding, Qencode can index every video automatically. Teams search their entire library with natural language queries, such as "highlights of the latest live performance," "outdoor scenes with crowd energy," and "any scene with a visible logo", and get back exact clips with timestamps instead of a list of files to scrub.

Intelligence without additional overhead. Since there is no separate vendor to manage, content to re-upload, or custom plumbing to bridge, it's never been easier to enrich and expand your content operations function.

What the Integration Unlocks Across Use Cases

The TwelveLabs Advantage

Qencode evaluated the video AI landscape before committing to this integration. The decision came down to a specific technical distinction that matters for encoding platforms.

Most AI tools that claim to analyze video are image classifiers running on extracted frames, or speech-to-text models that treat audio as the primary signal. TwelveLabs' models (Marengo for multimodal embeddings and Pegasus for reasoning and generation) process the full video signal simultaneously: visual, audio, and textual, in context, across time.

For a platform serving customers who need to search for "the most heartfelt family moment" or "every scene with a product unboxing," Qencode finally had a solution that gave them exactly what they needed.

The integration was deployed rapidly, with TwelveLabs models embedded directly into Qencode's existing platform architecture. This made it super simple for anyone to add this capability to their existing pipeline without re-engineering it, or disrupting the existing customer API surface. Customers who are already encoding with Qencode can access video intelligence without changing anything else about how they work today.

"We evaluated the AI video understanding market carefully before choosing TwelveLabs. They were the only provider whose models truly understood video the way we always envisioned and the way our customers actually needed. They weren't just extracting frames and transcripts, but indexing the full signal in a way that was extremely useful, robust and flexible. The multi-modal combination of visual, spoken, and contextual layers absolutely blew our mind. Our imagination started running wild, and the use-cases for the diverse range of customers using our platform basically began writing themselves.

— Murad Mordukhay. CEO, Qencode

Results

Qencode customers no longer run two pipelines. Encoding and video intelligence ship as one. That shift changes what's operationally possible at every scale.

Technical Architecture