AI Virtual Assistant for Shopping

Success Story

AI-Powered Virtual Assistant for Personalized Shopping

Extending a premium jewelry retailer’s signature in-store customer experience into digital channels to drive measurable engagement and conversion.

+6% add-to-basket rate

increase in conversion compared to standard online journeys.

+5% product views per visit

deeper catalog exploration through guided interactions.

Technologies

Agentforce

GitHub

Confluence

Expertise

AI and ML

Conversational commerce

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In-store experience gap

The signature consultative selling approach was missing online, limiting personalization and emotional engagement.

Generic digital journeys

Standard browse and search experiences failed to guide customers through meaningful product discovery.

Scaling personalization

Delivering tailored recommendations at global scale required advanced AI capabilities beyond traditional systems.

Sensitive context handling

Jewelry purchases often involve emotional or personal contexts, requiring nuanced and inclusive conversational design.

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Conversational AI excellence

Proven ability to design AI agents that go beyond support, enabling meaningful, revenue-driving customer interactions.

Human-centered design approach

Deep focus on tone, inclusivity, and emotional intelligence ensured alignment with the client’s brand values.

Zoolatech is a senior-heavy engineering firm with Silicon Valley roots and a Miami HQ, specializing in legacy modernization, system re-architecture, and AI deployment to drive long-term, compounding value.

2017

Year Founded

600+

Employees

96%

Client Satisfaction

Phase 1

Discovery and experience mapping

Defined the digital equivalent of the retailer’s signature customer consultation process, identifying key interaction patterns and personalization opportunities.

Phase 2

Conversational design and tone development

Established a brand-aligned voice—warm, knowledgeable, and inclusive—guiding all AI interactions.

Phase 3

AI agent development

Built and trained the Selling Agent to ask contextual questions, recommend products, and guide decision-making.

Phase 4

Integration and expansion

Integrated with existing customer support systems, with a roadmap to unify multiple agents into a seamless experience.

Phase 5

Continuous optimization

Iteratively improved performance based on behavioral data and engagement metrics.

The AI-powered assistant guides personalized shopping journeys—driving higher conversion and deeper product discovery.

Guided product discovery

The AI asks targeted questions to understand the customer’s intent and recommends relevant products with clear reasoning.

Inclusive conversational design

The system avoids assumptions around gender, relationships, or identity, ensuring a universally respectful experience.

Sensitive content handling

Advanced logic enables empathetic responses to distressing or personal queries, including directing users to appropriate support resources.

Option	Risk	Mitigation
Sensitive input risk	Misinterpretation of user inputs related to identity, beliefs, or personal context could lead to irrelevant or inappropriate recommendations.	Custom response handling ensures inclusive, context-aware interactions without stereotyping.
Distress response risk	Standard LLM behavior may deflect or block conversations involving self-harm or distress signals.	Purpose-built flows enable empathetic responses and guide users to appropriate local support resources.
Brand voice risk	Inconsistent tone could dilute the retailer’s distinct, consultative customer experience.	Clearly defined conversational guidelines enforce a warm, knowledgeable, and non-assumptive brand voice.