A global-scale retail enterprise deployed an end-to-end Multimodal AI Personalisation and Fulfilment Platform designed and delivered by GenAI Protos to transform how customers discover, evaluate and purchase products. The platform fuses deep learning, regression models and multimodal AI to analyse browsing behaviour, purchase history and visual preferences in real time, generating hyper-personalised recommendations that adapt to each individual's intent and context.

Beyond personalisation, GenAI Protos extended the solution to orchestrate the full order fulfilment value chain using agentic AI. It automates multi-step workflows across inventory allocation, routing logic and one-click checkout optimisation. The combined impact: a 35% uplift in incremental sales revenue and a 20% improvement in customer loyalty rates, alongside measurable gains in satisfaction and platform engagement.

Use case

AI-powered retail personalisation and fulfilment orchestration for enterprise e-commerce.

Industry

Retail and E-Commerce

Core capability

Real-time customer intent intelligence, multimodal recommendations and agentic fulfilment workflow automation.

Customer signals used

Clickstream, search queries, purchase history, product hover events, cart additions, visual signals and purchase completions.

AI methods

Deep learning encoders, regression propensity models, multimodal transformer models, A/B testing and causal inference.

Primary outcomes

35% incremental revenue uplift and 20% customer loyalty improvement.

Business value

Better product discovery, higher conversion, stronger retention, faster fulfilment and reduced marketing waste.

Retail at enterprise scale confronts a paradox of abundance: millions of SKUs, billions of user signals and no human-feasible way to match them in real time. The enterprise operated one of the largest e-commerce catalogues in its market, yet recommendation conversion was plateauing, fulfilment workflows required significant manual oversight and customer churn was rising despite heavy discounting.

• Disconnected data silos: clickstream, purchase history and visual/search data were processed in separate pipelines with no unified representation of customer intent.
• Static, rule-based recommendation engines: recommendations were driven by collaborative filtering alone, missing signals from visual browsing patterns and real-time session context
• Fulfilment bottlenecks: order routing, inventory prioritisation and checkout optimisation were manual-intensive, causing drop-offs at the final stage of the funnel.
• No cross-channel continuity: a customer exploring a product visually on mobile would receive no continuation of that intent on desktop or at checkout.

GenAI Protos designed a layered, production-grade AI platform structured across three interconnected capability pillars: Unified Intent Intelligence, Multimodal Recommendation Engine and Agentic Fulfilment Orchestration.

Unified Intent Intelligence The foundation of the platform is a real-time event streaming layer that ingests and unifies signals across all touchpoints: page views, search queries, product hover events, cart additions and purchase completions. Deep learning encoders convert raw behavioural sequences into dense customer intent vectors, while regression models layer in purchase propensity scores and churn risk signals. This creates a continuously updated, 360-degree customer representation that feeds all downstream AI layers.

Multimodal Recommendation Engine Moving beyond traditional collaborative filtering, the recommendation engine fuses three modalities of customer signal: behavioural signals from click and purchase sequences, textual signals from search queries and product descriptions, and visual signals from image embeddings derived from product photos the user engaged with. Multimodal transformer models align these representations in a shared embedding space, enabling recommendations that reflect not just what customers have bought, but what they are visually drawn to.

The engine operates in real time, re-ranking the recommendation slate as each new user event is ingested. A/B testing infrastructure and causal inference models ensure that every recommendation change is evaluated against revenue and engagement lift before full deployment.

Agentic Fulfilment Orchestration Once a customer reaches purchase intent, an agentic AI layer orchestrates a sequence of downstream tasks with no manual intervention. The agent coordinates inventory checks across fulfilment centres, selects the optimal shipping route based on delivery SLA and cost, triggers one-click checkout pre-population using stored preferences and initiates post-purchase workflows including confirmation, tracking and cross-sell nudges. This end-to-end orchestration reduced funnel drop-off at the checkout stage and shortened mean time from cart to confirmed order.

The architecture combines customer signal ingestion, unified intent intelligence, multimodal recommendations, agentic fulfilment orchestration and customer experience delivery on top of a shared data and AI foundation.

Customer Signals

Collects clickstream, search, purchase history, visual signals and cart events.

Unified Intent Intelligence

Uses deep learning encoders, propensity models and real-time intent profiles.

Multimodal Recommendation Engine

Combines behavioural, textual and visual signals to re-rank recommendations in real time.

Agentic Fulfilment Orchestration

Coordinates inventory checks, order routing, one-click checkout and post-purchase workflows.

Customer Experience

Improves personalised discovery, checkout, fulfilment and relevant engagement.

Data and AI Foundation

Supports real-time event streaming, unified customer data, model monitoring and A/B testing.

The customer journey uses AI signals from discovery through post-purchase engagement. Each step improves the next action, from understanding browsing intent to re-ranking products, predicting propensity, accelerating checkout and triggering relevant follow-up workflows.

Real-time event streaming

Ingests clickstream, cart and purchase events with sub-second latency.

Unified customer representation

Consolidates behavioural, textual and visual signals into a continuously updated customer intent profile.

Deep learning and regression models

Supports personalised product recommendations, demand forecasting, churn prediction and purchase propensity scoring.

Multimodal recommendation logic

Uses visual preference analysis, image-based product matching and cross-channel intent detection.

Agentic orchestration

Automates multi-step tasks across inventory, fulfilment and personalisation workflows.

A/B testing and causal inference

Measures recommendation and fulfilment changes against revenue and engagement lift before broader rollout.

Model governance

Uses drift monitoring, fairness audits and retraining pipelines embedded from day one.

The value of the build is not only better recommendations. The larger business pattern is a connected AI operating layer that links intent intelligence, product discovery, checkout and fulfilment into one customer journey. This gives retail teams a stronger foundation for conversion, retention and operational efficiency.

GenAI Protos delivered a production-grade retail AI platform that unifies customer signals, improves recommendations, accelerates checkout and automates fulfilment workflows. The implementation improved revenue, loyalty, discovery, customer satisfaction and operational efficiency while creating a reusable real-time data foundation for future retail AI use cases.

This use case can be reused as a pattern for retail and e-commerce systems where personalisation, fulfilment and customer engagement need to operate as one connected AI workflow instead of disconnected point tools.

• Signal unification: combine clickstream, search, purchase, visual and cart events into one intent layer.
• Recommendation design: fuse behavioural, textual and visual embeddings for real-time ranking.
• Decision orchestration: connect recommendations to checkout, inventory and fulfilment actions.
• Measurement layer: use A/B testing and causal inference to prove revenue and engagement impact.
• Governance layer: monitor drift, fairness, latency and performance after deployment.