Scaling Asset Discovery with Amazon Nova Multimodal Embeddings

Introduction

If you have ever managed a massive library of creative assets, you know the frustration of the "lost file." In the gaming and advertising industries, this problem is magnified by scale. Companies are now producing thousands of video advertisements for a single A/B testing campaign. It is not uncommon for a creative team to sit on a library of 100,000+ assets, growing by thousands every month.

Traditionally, finding the right clip meant relying on manual tagging—a process that is labor-intensive, inconsistent, and ultimately fails to capture the nuance of the content. If a designer needs a clip where "a character is pinched away by a hand," but the tagger only wrote "gameplay UI," that asset is effectively invisible.

Amazon Nova Multimodal Embeddings, available via Amazon Bedrock, changes this dynamic. By creating a unified vector space where text, images, video, and audio coexist, you can move away from rigid keywords toward true semantic search. This post explores how to architect a system that achieves high-precision discovery across massive media libraries without the overhead of manual metadata.

The Problem with Traditional Asset Management

Before diving into the solution, it is important to understand why the old ways are breaking. Keyword-based search systems are only as good as the person doing the tagging. If your team is global, language barriers and subjective descriptions create a fragmented database.

Even modern Large Language Model (LLM) solutions, which can automatically generate tags, face a scaling issue. Running a full LLM analysis on every second of 100,000 videos to generate text tags is computationally expensive and slow. More importantly, it forces the search to stay within the boundaries of those generated tags. If the search requirement changes—perhaps you suddenly need to find all clips with a specific "retro synth" audio vibe—pre-defined tags won't help you.

What matters here is moving the intelligence from the tagging phase to the representation phase. Instead of describing what is in a video, you represent the video itself as a mathematical vector.

Understanding the Unified Vector Space

Nova Multimodal Embeddings is a state-of-the-art model designed for agentic Retrieval-Augmented Generation (RAG) and semantic search. Its most significant advantage is the unified vector space architecture.

In older systems, you might have one model for text and another for images. To search for an image using text, you had to find a way to map those two different mathematical spaces together. Nova generates embeddings that exist in the same semantic space regardless of the input type.

When you convert the text "racing car" into a vector, its position in that high-dimensional space will be naturally close to the vectors generated from a photo of a Ferrari or a video clip of a Formula 1 race. This allows for intuitive cross-modal retrieval: you can use text to find video, or use a 5-second audio clip to find a similar visual scene.

The Role of Matryoshka Representation Learning (MRL)

One technical detail that often gets overlooked is how the model handles dimensions. Nova offers four embedding dimension options: 256, 384, 1024, and 3072.

AWS uses a technique called Matryoshka Representation Learning (MRL). Think of this like a Russian nesting doll. The most important semantic information is packed into the smaller dimensions (like 256), while the larger dimensions (3072) add more granular detail.

256/384 dimensions: Best for ultra-low latency and reduced storage costs.
1024 dimensions: The "sweet spot" for most enterprise applications, balancing speed and precision.
3072 dimensions: Used for critical use cases where you need the highest possible precision in distinguishng very similar assets.

Architecting the Multimodal Search Pipeline

To build a production-ready discovery engine, you need an architecture that handles both the heavy lifting of video processing and the low-latency requirements of search. The following design uses a serverless approach to scale automatically with your library size.

The Ingestion Workflow

Storage (Amazon S3): When a creative asset is uploaded, it lands in an S3 bucket. This acts as the "source of truth."
Trigger (AWS Lambda): The upload triggers a Lambda function that validates the file type and size.
Embedding Generation (Amazon Bedrock): The Lambda function calls the Nova Multimodal Embeddings model. For video, this is usually an asynchronous call because the model needs to segment the video.
Vector Storage (Amazon OpenSearch Service): Once the embeddings are generated, they are stored in OpenSearch, which serves as your vector database.

The Search Workflow

When a user wants to find an asset, the process is reversed but happens in real-time:

Query Processing: The user enters a text query or uploads a reference image.
Vectorization: The query is sent to Amazon Bedrock to be converted into a vector using the same Nova model.
Similarity Search: The system performs a K-Nearest Neighbor (KNN) search in OpenSearch. It compares the query vector against the stored asset vectors using cosine similarity.
Results: The system returns the top matches, including specific timestamps for video segments.

Handling Video Complexity: Segmentation

One of the most impressive features of Nova is its ability to handle long-form video. It doesn't just create one vector for a 10-minute video. Instead, it performs segmented embedding.

You can configure the model to break videos into meaningful segments (typically 1 to 30 seconds). The model analyzes visual scenes, actions, and audio context within these segments. This is vital for creative teams. If you are looking for a specific "card tap" animation in a 30-minute gameplay recording, the system can point you to the exact second that action occurs.

Code Implementation: Generating Segmented Embeddings

Here is how you would structure the request to Bedrock for a video asset:

request_body = {
    "schemaVersion": "amazon.nova-embedding-v1:0",
    "taskType": "SEGMENTED_EMBEDDING",
    "segmentedEmbeddingParams": {
        "embeddingPurpose": "GENERIC_INDEX",
        "embeddingDimension": 1024,
        "video": {
            "format": "mp4",
            "source": {
                "s3Location": {
                    "uri": "s3://my-creative-assets/gameplay_01.mp4"
                }
            },
            "embeddingMode": "AUDIO_VIDEO_COMBINED",
            "segmentationConfig": {
                "durationSeconds": 5 # Creates a vector for every 5-second block
            }
        }
    }
}

# Start the asynchronous invocation
response = bedrock_client.start_async_invoke(
    modelId="amazon.nova-multimodal-embeddings-v1",
    modelInput=request_body,
    outputDataConfig={"s3OutputDataConfig": {"s3Uri": "s3://my-embedding-results/"}}
)

Performance in the Real World

In testing against a library of 170 gaming creative assets, Nova Multimodal Embeddings showed remarkable accuracy. It achieved a 96.7% recall success rate. Even more impressive is the high-precision recall: in 73.3% of searches, the target content was returned in the top two results.

The real value here is the cross-language capability. Because the model understands the visual and semantic meaning rather than just the words, it demonstrates minimal performance degradation across different languages. If a user searches in Japanese for a concept depicted in an English-language ad, the system still finds the match.

Scaling with Amazon OpenSearch

Once you have your vectors, you need a way to search them at scale. Amazon OpenSearch Service is the standard choice here because of its KNN (K-Nearest Neighbor) plugin.

When a search comes in, OpenSearch doesn't look at every single vector in your database (which would be slow). Instead, it uses efficient indexing structures to find the closest neighbors in the vector space.

Feature	Benefit for Creative Teams
Cosine Similarity	Measures the angle between vectors, focusing on the "orientation" of the content rather than just magnitude.
Metadata Filtering	You can combine vector search with traditional filters (e.g., "Find videos like this, but only from the last 30 days").
Millisecond Latency	Even with millions of vectors, OpenSearch can return results in under 100ms.

Tecyfy Takeaway

Scaling creative asset discovery is no longer a manual tagging problem; it is a vector search problem. By leveraging Amazon Nova Multimodal Embeddings, you can build a system that truly "understands" your media library.

Actionable Insights for your implementation:

Choose the right dimension: Start with 1024 dimensions for a balance of cost and precision. Only move to 3072 if your assets are visually very similar (e.g., different versions of the same UI).
Use Asynchronous Workflows: Video embedding is computationally heavy. Use SQS and Lambda to handle the Bedrock calls asynchronously to avoid API timeouts and ensure a smooth user experience.
Optimize Segmentation: For fast-paced gaming ads, a 5-second segmentation window usually captures the key actions without creating an overwhelming amount of data.
Think Cross-Modal: Don't just build a text-to-video search. Enable image-to-video search so designers can upload a still frame and find all video clips that match that aesthetic.

Beyond Keywords: Scaling Video Discovery with Amazon Nova Multimodal Embeddings