Help customers find and buy with confidence. AI that answers product questions, assists purchases, and handles post-sale support.
Online shoppers have questions. They want to know if a product is right for them, when it will arrive, how to return it, and dozens of other things. Answering these questions quickly and accurately drives conversion, satisfaction, and repeat business. AI makes this possible at scale.
E-commerce creates specific needs:
Conversion pressure: Visitors who cannot find answers leave and buy elsewhere.
High volume: Popular retailers handle thousands of enquiries daily.
Peak seasons: Demand spikes dramatically during sales and holidays.
24/7 expectations: Shoppers buy at all hours and expect support to match.
Thin margins: Customer service costs must be managed carefully.
AI addresses these pressures directly.
AI helps customers decide to buy:
Product questions: Answering queries about features, specifications, compatibility, and suitability.
Recommendations: Suggesting products based on needs, preferences, and behaviour.
Comparison assistance: Helping customers understand differences between options.
Availability and delivery: Providing stock status, delivery times, and options.
Size and fit guidance: Helping customers choose correctly, reducing returns.
AI supports customers after they buy:
Order tracking: Real-time status on orders, shipments, and deliveries.
Returns and exchanges: Guiding customers through return processes.
Product support: Answering questions about using purchased products.
Issue resolution: Handling complaints and problems efficiently.
Loyalty and retention: Engaging customers with relevant offers and information.
Retailers implementing AI typically see:
Conversion improvement: More visitors become buyers when questions are answered.
Support cost reduction: Routine enquiries handled without human involvement.
Customer satisfaction increase: Faster answers improve experience.
Return rate reduction: Better information means better purchase decisions.
Staff efficiency: Human agents focus on complex issues, not routine queries.
Retail AI works best when connected to:
E-commerce platforms. Shopify, Magento, WooCommerce, or custom storefronts.
Product information systems. Accurate specs, compatibility, and policy content.
Inventory and warehouse systems. Availability, locations, and allocation rules.
Order management and fulfilment. Tracking, exceptions, and delivery status.
CRM and customer databases. Context for personalisation and service.
Helpdesk and ticketing. Smooth escalation and handover with conversation context.
We build integrations that give AI the information it needs.
Retail demand is not constant. AI helps manage variability:
Elastic capacity: Handle volume spikes without adding staff.
Consistent experience: Quality does not degrade under load.
Instant availability: No queues during busy periods.
Cost efficiency: Pay for capacity when needed, not all year.
AI enables personalised retail experiences:
Product recommendations: Based on browsing history, purchase history, and behaviour.
Dynamic content: Website experience adapted to individual visitors.
Targeted communications: Relevant messages and offers.
Customer recognition: Remembering preferences across interactions.
We have built AI for retail and e-commerce clients including luxury brands and direct-to-consumer businesses. We understand the commercial pressures and integration requirements.
Retail systems work best when product truth and policy truth are explicit.
Ground answers in authoritative data. Product information, stock, delivery timelines, and policies must come from systems of record.
Design for handover. If a shopper is stuck or upset, escalation should be fast and pass context so humans can resolve issues.
Measure outcomes end-to-end. Conversion, return rate, support load, and time-to-resolution are better measures than “messages handled”.
Use these prompts to design a retail assistant that improves outcomes.
“What are the top pre-purchase questions that block conversion, and what is the correct answer for each?”
“Which post-purchase journeys should be automated: tracking, returns, exchanges, and issue triage?”
“What integrations do we need so answers are accurate, and what escalation rules keep customers safe?”
Yes, when recommendations are based on accurate product data and clear rules, with safe fallbacks when information is missing.
Often yes, by handling routine questions and providing self-service for common journeys like tracking and returns.
Better size/fit guidance, clearer product education, and proactive clarification questions before purchase.
Automation absorbs volume and routes exceptions to humans with context, maintaining quality under load.
Higher conversion, lower returns, reduced routine support load, and faster resolution when humans are needed.