Multimodal Search Without Redesigning Enterprise Systems

Enterprise search systems have traditionally struggled with multimodal content, requiring separate retrieval logic for text, images, and video that often demands significant architectural redesign. Existing approaches to multimodal retrieval in production environments typically involve complex, modality-specific pipelines that are difficult to integrate with established enterprise infrastructure. This limitation has constrained organizations from implementing sophisticated cross-modal search capabilities without undertaking costly system overhauls that disrupt existing workflows and investments.

AMES introduces a unified multimodal late interaction retrieval architecture that operates as backend agnostic, demonstrating that fine-grained multimodal retrieval can be deployed within production-grade enterprise search engines without architectural redesign. The system embeds text tokens, image patches, and video frames into a shared representation space using multi-vector encoders, enabling cross-modal retrieval without requiring modality-specific retrieval logic. This approach represents a significant departure from previous s that necessitated separate processing pipelines for different content types.

Ology employs a two-stage pipeline that begins with parallel token-level approximate nearest neighbor search using per document Top-M MaxSim approximation. This initial retrieval phase is followed by accelerator-optimized Exact MaxSim re-ranking, which refines for improved accuracy. The system's backend-agnostic design allows it to integrate with existing enterprise search infrastructure, particularly demonstrated with Solr-based systems. This architectural flexibility addresses a key limitation in previous multimodal search implementations that required specialized infrastructure.

Experimental on the ViDoRe V3 benchmark show that AMES achieves competitive ranking performance while maintaining scalability within production-ready systems. The research demonstrates that the approach maintains accuracy comparable to more complex, specialized systems while operating within the constraints of existing enterprise infrastructure. These previous assumptions about the trade-offs between multimodal search sophistication and system integration requirements, showing that advanced capabilities can be implemented without sacrificing production readiness.

Extend to enterprise applications where organizations maintain large repositories of mixed media content, including documents, images, and videos that need to be searched simultaneously. This research addresses practical constraints faced by businesses that cannot afford to rebuild their search infrastructure from scratch but require more sophisticated retrieval capabilities. The approach aligns with real-world enterprise needs where incremental improvements to existing systems often prove more viable than complete architectural overhauls.

Limitations acknowledged in the research include the specific benchmarking against ViDoRe V3, which may not capture all real-world enterprise search scenarios. The approach's performance characteristics in extremely large-scale deployments with billions of documents remain to be fully explored. Additionally, while the system demonstrates backend agnosticism, optimal performance may still require some tuning for specific enterprise search platforms beyond the Solr implementation demonstrated in the research.

The research contributes to ongoing efforts to make advanced AI capabilities more accessible within existing enterprise technology stacks, reducing barriers to adoption for organizations with established infrastructure investments. By demonstrating that sophisticated multimodal retrieval can be implemented without architectural redesign, the work addresses practical deployment concerns that often hinder adoption of advanced AI technologies in enterprise settings. This approach represents a pragmatic middle ground between cutting-edge research and real-world implementation constraints.

Future work could explore extensions to additional modalities beyond text, images, and video, as well as integration with emerging enterprise search platforms. The research opens possibilities for more gradual adoption paths for advanced AI capabilities in organizations with complex legacy systems. As enterprises continue to accumulate diverse digital assets, approaches like AMES that bridge the gap between research innovation and practical deployment will become increasingly valuable for maintaining competitive search capabilities.