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Multimodal commerce

Immersive, interactive experiences that actually convert

Multimodal AI is redefining how customers discover, evaluate, make choices and purchase products, making the shopping experience easier than ever. But what’s effortless for customers is increasingly complex for retailers. Delivering these interactions requires specialized vision models, high-quality product data, multimodal fusion layers and real-time inference pipelines.  

Solvd makes that complexity feel simple. By helping retailers harness voice, vision, AR and generative intelligence, we enable them to increase conversion, deliver lower return rates, accelerate SKU activation and unlock lower cost-to-serve through more automated and personalized experiences across every channel.

Start with a commerce innovation assessment

Why now

Why Multimodal Commerce accelerates revenue and reduces friction

Shoppers move faster than commerce stacks

Customer behavior shifts quickly across voice, video and visual cues, creating friction for retailers running on legacy systems.

Multimodal signals boost accuracy and confidence

Combining voice, vision, text and AR produces more relevant recommendations, higher conversion and fewer returns.

Integration unlocks
real performance

Multimodal capabilities only deliver value when connected to your existing commerce stack, enabling seamless discovery and personalization.

The next era of commerce

How to win with multimodal experiences

Today’s e-commerce success requires more than simply adding trendy and isolated AI features. Gartner predicts that 40% of generative AI solutions will be multimodal by 2027, up from 1% in 2023. This shift from standalone tools to unified models is signaling the new era where unified multimodal systems define who leads and who falls behind. Winning with multimodal starts with getting the foundation right.   

Modular, specialized models

Multimodal personalization

Rapid experimentation

Integrated commerce stack

AI-native experiences

Data quality & governance

Modular, specialized models outperform single-model strategies

General-purpose models can’t match the precision required for retail tasks.

Winning retailers combine language, vision and AR models that are each tuned for specific tasks. Category-leading virtual try-on succeeded because it relied on a precision vision model rather than a general-purpose system.

Personalization improves dramatically when it incorporates multimodal signals

Click data alone doesn’t reveal true intent.

Images, video, voice and text provide more context than clicks alone. Multimodal discovery experience demonstrates higher relevance and stronger conversion by blending visual and language cues.

Competitive retailers operate on rapid experimentation cycles

Slow updates cause stagnant recommendations and weak engagement.

AI-native retailers update discovery patterns and recommendations weekly, which conditions customers to expect continuous improvement. The brands that win are those with architecture built for ongoing upgrades and fast iteration.

Deep integration into the commerce stack creates defensible advantage

Siloed systems limit what AI can deliver and slow down innovation.

AI performs best when connected directly to PIM, DAM, CMS, identity, inventory and pricing systems. Amazon and Alibaba succeed because their models operate on fully integrated product and customer data.

AI-native product experiences lift conversion and reduce returns

Shoppers lack confidence when they can’t evaluate product fit or context.

Visual try-ons, shoppable video, conversational browsing and contextual recommendations help shoppers evaluate products with confidence. Retailers using AR sizing report reductions in return rates and higher purchase satisfaction.

Strong governance and clean data determine long-term success

Poor data quality and weak governance increase friction at scale.

Multimodal systems amplify both strengths and weaknesses in product and customer data. The retailers who win invest early in permissions, data quality and content governance to ensure consistent performance and safe deployment.

Technological foundation 

Our approach that makes it happen

Each of these components connects directly to business outcomes 
and each should operate flawlessly. To make it happen, we bring all the moving parts together by handling the models, data, pipelines and integrations. It delivers real business impact from day one and a scalable foundation for the future. Being more context-aware, each model in a multimodal system creates a chain of narrow “professionals” that can complete the task better than a generalized model. For commerce, this means better suggestions, fewer returns, better automation and ultimately – business wins.

Models that convert images, audio, and text into a unified vector space. This enables cross-modal similarity search, fusion and retrieval (such as image-to-text or voice-to-product metadata). Without shared embeddings, modalities remain siloed and cannot power a unified experience. 

Fusion layers combine encoded signals using attention or multimodal transformers. This allows the system to reason across modalities, such as aligning visual context with textual descriptions and voice intent.

Large pre-trained models that process multiple modalities. These can be adapted into commerce flows for interactive assistants, content generation and multimodal question answering. 

Vector stores like FAISS or Pinecone index embeddings for fast nearest-neighbor search. This enables instant retrieval across modalities, such as matching a camera image to the product catalog.

Multimodal commerce requires immediate responses. Optimized GPU/TPU inference, quantized models and service orchestration ensure low latency for applications like visual search and shoppable video.

Pipelines that clean and prepare inputs from voice, cameras, video and AR sensors. This includes speech-to-text, image normalization, frame extraction and SLAM for AR experiences.

Generative models create adaptive product content: lifestyle imagery, generated video, PDP copy and more. These are typically built from multimodal foundation models. 

Persistent memory layers track user interactions across sessions and modalities. This enables tailored recommendations, adaptive flows and consistent AI behavior.

Object detection and video tracking make media interactive. Integrations with commerce APIs connect visual moments directly to the product catalog and checkout flow.

For AR, mobile, and in-store experiences, some processing must run on the device to maintain speed and privacy. This requires edge-optimized models, lightweight SDKs and deployment pipelines.

Multimodal encoders/shared embedding spaces

Cross-modal fusion/attention mechanisms

Foundation multimodal models/LLMs with vision and audio

Vector databases and similarity search infrastructure

Real-time inference and low-latency serving

Multichannel input capture and pre-processing

Generative AI for content and product experience

Personalization and contextual memory systems

Shoppable media and embedded commerce APIs

Edge and on-device processing

Multimodal encoders/shared embedding spaces

Models that convert images, audio, and text into a unified vector space. This enables cross-modal similarity search, fusion and retrieval (such as image-to-text or voice-to-product metadata). Without shared embeddings, modalities remain siloed and cannot power a unified experience. 

Cross-modal fusion/attention mechanisms

Fusion layers combine encoded signals using attention or multimodal transformers. This allows the system to reason across modalities, such as aligning visual context with textual descriptions and voice intent.

Foundation multimodal models/LLMs with vision and audio

Large pre-trained models that process multiple modalities. These can be adapted into commerce flows for interactive assistants, content generation and multimodal question answering. 

Vector databases and similarity search infrastructure

Vector stores like FAISS or Pinecone index embeddings for fast nearest-neighbor search. This enables instant retrieval across modalities, such as matching a camera image to the product catalog.

Real-time inference and low-latency serving

Multimodal commerce requires immediate responses. Optimized GPU/TPU inference, quantized models and service orchestration ensure low latency for applications like visual search and shoppable video.

Multichannel input capture and pre-processing

Pipelines that clean and prepare inputs from voice, cameras, video and AR sensors. This includes speech-to-text, image normalization, frame extraction and SLAM for AR experiences.

Generative AI for content and product experience

Generative models create adaptive product content: lifestyle imagery, generated video, PDP copy and more. These are typically built from multimodal foundation models. 

Personalization and contextual memory systems

Persistent memory layers track user interactions across sessions and modalities. This enables tailored recommendations, adaptive flows and consistent AI behavior.

Shoppable media and embedded commerce APIs

Object detection and video tracking make media interactive. Integrations with commerce APIs connect visual moments directly to the product catalog and checkout flow.

Edge and on-device processing

For AR, mobile, and in-store experiences, some processing must run on the device to maintain speed and privacy. This requires edge-optimized models, lightweight SDKs and deployment pipelines.

News & insights

Research Solvd at NeurIPS 2025: Explainable AI (XAI) and Reinforcement Learning (RL) at scale
AI & data engineering
Multimodal commerce
White paper Multimodal commerce: new era of online shopping
AI & data engineering Digital experience Retail & consumer goods
Introducing Multimodal Commerce
Article Introducing multimodal commerce: The next era of customer experience
AI & data engineering Retail & consumer goods
Research GUIDE for incremental learning – Solvd at ECAI 2025
AI & data engineering
Research Classifier-free Guidance with Adaptive Scaling  – Solvd at ECAI 2025 
AI & data engineering
Research Studying the particle collisions – Solvd at ECAI 2025 
AI & data engineering
AI adoption framework
White paper Modern banking experience: A framework for AI adoption
AI & data engineering Banking, financial services & fintech
UCE
Article The business payoff of Unified Customer Experience (UCE) 
Digital experience
AI fraud
Article The $16B question: What to do about the rise of AI fraud
AI & data engineering

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