Affle (Visenze)

ViSenze: Agentic Visual Discovery & Multi-Search Review

As of January 2026, ViSenze has integrated into the Rezolve AI ecosystem, transitioning from a standalone visual search tool to a comprehensive Conversational Commerce Agent. The platform's architecture is centered on a high-throughput multimodal engine that reconciles visual intent (photos, screenshots) with natural language queries to deliver industry-leading 99% search accuracy [Documented]. The core system acts as a specialized orchestration layer that bridges the gap between unstructured social content and merchant inventory databases [Inference].

Model Orchestration & Agentic Logic

The 2026 framework utilizes the Multi-Search Architecture, which allows for the simultaneous processing of text, keywords, and image embeddings in a single unified query [Documented].

• GenAI Tagging: Employs generative models to automate catalog enrichment, extracting hundreds of style attributes (material, silhouette, occasion) to reduce manual metadata overhead [Documented].
• Conversational AI Agents: Integrates with Rezolve’s Brain to handle live shopping inquiries, suggesting outfits based on body type and weather trends [Documented].
• Visual Similarity Engine: Uses specialized deep-learning transformers optimized for street-to-shop scenarios with sub-500ms latency [Documented].

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Integration Patterns & Shoppable Media

Interoperability is achieved through a REST-based API and a new Model Context Protocol (MCP) bridge, allowing AI agents to autonomously generate shoppable galleries [Documented]. Native SDKs for Unity and WebXR facilitate immersive 'See-It-Want-It' experiences in spatial computing environments [Documented].

Performance & Resource Management

The system handles over 3 billion image searches globally, utilizing distributed GPU clusters for real-time vector indexing. While high-volume tagging is offloaded to managed compute, the exact latency of generative description synthesis under peak loads remains proprietary [Unknown].

Evaluation Guidance

Technical evaluators should verify the following architectural characteristics:

• Multi-Search Latency: Benchmark end-to-end response times for queries combining high-res images and complex natural language strings (target < 800ms) [Unknown].
• Tagging Accuracy: Audit the GenAI Tagging precision across diverse SKU categories, specifically for non-fashion items where visual training data may be sparse [Inference].
• CPCU Attribution: Request documentation on the deterministic attribution logic used to map visual search intent to final conversions within the Affle advertising stack [Documented].