Unlocking Video Insights: How an Optimized Video Ingestion Pipeline Transforms Content Discovery
Multimodal AI combines visual, audio, and textual data to generate insights that can transform and accelerate content discovery, as well as maximize content value, such as for targeted advertising. At scale, this requires processing large volumes of image and video content to ensure that content is indexed, enriched, and made actionable at scale.
While image processing is relatively straightforward, video processing for AI presents a unique set of challenges. A single video file can contain thousands of frames, significantly increasing processing demands compared to a static image. But a video is much more than a collection of still images– video ingestion requires an understanding of the boundaries between frames, shots, and scenes. A frame, shot, or scene needs to be kept in alignment with its audio to enable multimodal video intelligence. Video ingestion also needs to handle scene changes, object tracking, and speech-to-text conversion—all while efficiently processing large volumes of video data in real-time without delays or bottlenecks.
In this post, we’re going to focus specifically on the importance of keyframes and how keyframe selection can reduce the cost of video processing. This topic is particularly important for businesses processing thousands of hours of footage daily. Inefficient ingestion leads to high processing costs, slow retrieval times, and incomplete metadata, making video assets difficult to leverage effectively for video recommendations, highlights, newsroom production, and content moderation, among other use cases.
The importance of selecting the right keyframes in video processing
A keyframe is a representative still image that captures essential moments within a video. They serve as the foundation of structured video analysis. Selecting the right keyframes and avoiding unnecessary keyframes ensures that businesses are able to 1.) extract valuable insights and 2.) ensure low-latency ingestion.
Traditional keyframe selection can inflate costs while missing key details
Most systems use time-based averages to extract keyframes at fixed intervals, for example, selecting one frame per second or two frames per second in a video. While simple, this results in redundant frames, inflated storage costs, and missed critical details.
AI-driven keyframe selection optimizes for relevance, reduces costs, and increases quality
AI models analyze scene changes, object movement, facial expressions, and action sequences to dynamically select and process the most valuable frames. This has an immediate impact on cost and scale. But for end users, this approach also reduces noise and improves searchability and metadata quality.
By adopting AI-driven keyframe selection, businesses ensure that multimodal models process only the most valuable frames, reducing storage costs while improving content discovery. This enables faster access to key moments, whether for news organizations tracking breaking stories, retailers analyzing shopper behavior in video content, or social platforms identifying trending moments in user-generated media.
How resolution and frames per second (FPS) impact processing efficiency
The resolution and FPS of a video impact both processing time and the ability to extract meaningful insights. Higher resolutions require more storage and processing power, while lower resolutions speed up AI-based analysis. For more context:
- Common frame rates include:
- 30fps for user-generated content, retail ads, TV dramas, and documentaries.
- 60fps for live-streamed sports and high-performance video games.
- Most media companies use 30fps as a balance between smooth playback and efficient processing.
- AI processing models often work best with 360p resolution to optimize speed and efficiency.
To ensure assets are available for AI processing as quickly as possible, we recommend ingesting video at 360p and 30fps to balance processing speed, storage efficiency, and AI performance. Optimizing video format at ingestion reduces computational overhead, allowing assets to become AI-ready faster, so users can immediately search, generate metadata, and begin working with their content without delay. For best results, standardizing video formats before ingestion ensures seamless processing and faster accessibility.
Choosing the right video format for processing
Selecting the right video format is crucial for processing speed, storage efficiency, and AI compatibility. The format a video is received in depends on its source, and ensuring the correct format can streamline AI-powered video analysis.
- News and Media Organizations – Publishers and broadcasters receive news footage from multiple sources, including media agencies that provide professionally formatted video files such as Material Exchange Format (MXF) and QuickTime (MOV), which may require format conversion for AI processing.
- User-Generated Content (UGC) – Social and e-commerce platforms accept multiple file formats (MP4, MOV, WEBM, and AVI) but typically convert them into standardized formats, often favoring MP4 (H.264) for compatibility.
- Enterprise and Internal Video Archives – Some organizations maintain historical or proprietary video archives in older formats (AVI, MOV, MKV). These may need conversion to MP4 (H.264) to enable modern AI-driven search and analysis tools.
A video format consists of a few key components:
- Container: The file wrapper that holds video, audio, subtitles, and metadata. Examples include .mp4, .mkv, and .avi.
- Video Codec: A technology used to compress and decompress the video data. Popular video codecs include H.264 (AVC) and H.265 (HEVC).
- Audio Codec: Compresses and decompresses the audio data. Common audio codecs include AAC and MP3.
For the fastest processing and best AI integration, videos should be provided in MP4 (H.264 video, AAC audio). This format is optimized for efficient ingestion, ensuring minimal processing delays and seamless compatibility with AI models. Providing videos in this format reduces storage overhead, speeds up indexing, and enhances search performance—allowing content to be analyzed and retrieved faster.
How Coactive processes video and images at scale
Managing and processing large-scale video and image datasets is complex, but Coactive now makes it easier than ever with the launch of our new ingestion pipeline—a major expansion of our platform designed for lightning-fast performance, efficiency, and scalability. This newly built capability automatically optimizes every step of video and image processing, so that businesses don’t have to build or manage these systems themselves. (Read more here: Should you build or buy your AI? A primer for business leaders)
From ingestion to metadata generation, Coactive structures raw media into searchable, AI-ready assets—allowing users to instantly find and analyze their content. Whether processing millions of images or hundreds-to-millions of hours of video, our ingestion pipeline ensures that content is indexed, enriched, and made actionable at scale.
The Coactive Ingestion Pipeline
Here’s how our pipeline seamlessly processes video and image assets:
- Ingestion: Assets are submitted via an API call or through the Coactive UI, which supports ingesting single assets, batch processing (CSV/JSON), and cloud storage buckets, all while letting the user provide any custom metadata that they may need for their assets.
- Optimized Video Processing: Generates a unique video identifier so users can reference their assets and stores user-provided video metadata.
- Keyframe Selection via Intelligent Sampling: Coactive intelligently selects the most relevant moments—such as shifts in scenery, facial expressions, gestures, or action sequences. This ensures the most relevant content is surfaced while reducing processing overhead, storage costs, and noise in search results. (Learn more about Intelligent Sampling)
- Audio Processing: Transcribes speech, segments transcription into structured text, and generates searchable embeddings to give users the power to make speech-based content instantly discoverable.
- Metadata and Indexing: Extracted keyframes and embeddings are stored in vector embedding storage, allowing for rapid content retrieval and AI-powered search. Additionally, audio is aligned with video frames, ensuring that speech-to-text transcriptions and spoken content remain synchronized for accurate metadata tagging and searchability.
By automating these processes, Coactive ensures businesses get the highest-quality structured data—without the overhead of building complex video ingestion infrastructure.
What makes Coactive’s ingestion pipeline the best choice?
Coactive streamlines video ingestion, handling the complex processing so that businesses can focus on insights, not building infrastructure. The benefits of this approach include:
✅ High-speed processing and automation – Processes hundreds of hours of video in just one hour. Traditional pipelines can take days.
✅ AI-optimized & preprocessed data – Our platform automatically prepares assets for AI models, eliminating the need for manual preprocessing and costly compute overhead.
✅ Transfer descriptive and structural metadata – Bring in any existing metadata associated with the asset, such as video identifiers, timestamps, release dates, SKU numbers, or tags and labels. This allows your business to retain and leverage all existing metadata.
✅ AI-ready search and indexing – Every asset is converted into structured, vector-based content, enabling instant retrieval and scalable AI-powered search across vast media libraries.
At Coactive, we see ingestion as the foundation for efficient and cost-effective video intelligence at scale.
Unlock the full potential of your video data
With Coactive, you can ingest your visual content once and then build any application, experience, or data analysis on top of this single, Multimodal AI Platform. Whether you’re managing news footage, moderating user-generated content, or enhancing e-commerce product discovery, Coactive ensures your content is instantly accessible, searchable, and optimized for AI-driven insights. And all of this begins with fast and efficient content ingestion.
Get in Touch! We would love to share how Coactive is helping Media and Entertainment companies find new efficiencies in their media intelligence stack.
