Aug 20, 2025
Our goal is to create a system capable of analyzing a user-provided input image. By leveraging advanced AI, the system searches a large-scale, indexed collection to identify and return entries with similar characteristics, then fetches and displays detailed information for the matching items. Building on the momentum of our earlier exploration into multimodal AI, we now take a closer look at how these concepts come to life in practice.
The system is designed to ingest input from a single modality, such as an image, and convert it into a high-dimensional vector embedding. This embedding is then used to perform a similarity search against a unified database to find and return the most relevant entries. Once a match is identified, the system retrieves the associated metadata for that entry from the data catalog.
We established the following foundational capabilities:
UI: We developed a web application to accept an image uploaded by the user.
Data Platform: The uploaded image is then saved in Azure Blob Storage and made accessible through Databricks.
Machine Learning: Vector search is used to convert the images to embeddings and perform similarity searches.
Process
Raw infrastructure transforms into the foundation of a smart, responsive search engine. From here, we embark on a deeper journey through the core components of a multimodal system, shaping data into intelligence step by step:
1. Dataset Onboarding: This initial step involves importing and organizing a curated dataset of e-commerce product images and their associated metadata, such as SKU, category, brand, and pricing. This process ensures all product information is clean, structured, and ready to be used in the subsequent stages of the pipeline.
2. Embedding Pipeline: Here, a vision-text embedding model, CLIP(Contrastive Language–Image Pre-training), is chosen to serve as the core of the multimodal process. This model is used to batch-generate vector embeddings for all images within the product catalog in Databricks. The resulting embeddings and their corresponding product IDs are then persisted in a vector index, stored in a service like Databricks Vector Search, creating a unified representation space.
3. Similarity Search: KNN similarity search (k=5) is then performed against the stored vectors. The system then retrieves the metadata and image URLs.
4. Model Serving: An API endpoint, /search/similar, is exposed to accept an image (or blob path) and return the top-5 results with details like score, product ID, name, price, and image URL. This live API endpoint is what the frontend calls to initiate a search and retrieve the necessary product information.
5. Frontend Integration: This final step displays the results returned from the API in a UI with features like loading states and error handling.
How does CX Data Labs Help?
We specialize in the protocols and practices for creating vector embeddings and optimizing each modality-specific encoder process. Specifically, we have expertise in the creation of the following:
Vector Embeddings: We specialize in Vector embeddings, which are numerical representations of data (like text, images, or audio) in a high-dimensional space, allowing machines to understand and compare their meaning or features mathematically.
Cross-Modal Alignment: We construct data pipelines to synchronize and link related data across modalities (e.g., video to audio) using timestamps and identifiers for semantic coherence.
Vector Databases: We persist the outputs into performant and specialized datastores such as Pinecone, Milvus, Chroma, and index high-dimensional embeddings for fast Approximate Nearest Neighbor (ANN) similarity searches.
Metadata Management: We create context and update the catalog using tools like Azure Purview and AWS Glue Data Catalog with the organization and discoverability for unified datasets.
About CX Data Labs
CX Data Labs is a modern data and analytics consulting firm that empowers organizations to transform their data utilization to achieve tangible business outcomes. With deep experience from Fortune 50 environments, we specialize in building and executing data strategies that seamlessly align data, technology, and business goals. Our approach emphasizes hands-on execution, guiding clients from defining a comprehensive data strategy to building scalable data platforms and robust data engineering pipelines, including the specialized infrastructure needed for multimodal AI. Whether it's structuring KPIs, implementing governance, or designing cloud-native architectures, CX Datalabs ensures every decision is driven by measurable business value.
Retail runs on data, but most of it lives in silos — your POS here, your e-commerce there, your supply chain somewhere else. Add in customer reviews, clickstreams, and images, and the picture gets even messier. That chaos slows down every attempt to innovate with AI.
At CX Data Labs, we bring order to the noise. We unify retail’s scattered data into a governed, Azure-native platform on Databricks — clean, connected, and ready for AI. Teams get fast, safe sandboxes to test new ideas, while production pipelines turn the winners into engines for personalization, demand forecasting, and customer experience at scale.
The result? A data foundation built for retail’s next chapter.
