AI Customer Service & Personalisation for Retail
Online shoppers expect quick answers about orders, returns and products. AI agents can resolve these conversations instantly, recommend products from your catalogue and pass complex cases to your team with the full context.
This is an example project. It shows how we would approach this kind of work in retail & e-commerce; it does not describe a specific client. Results depend on your process and data, and we agree on how to measure them with you before starting.
Common Challenges
- Support teams are flooded with order-status and returns questions, especially at peak times.
- Shoppers leave when they cannot get product answers quickly.
- Customer data is spread across the store platform, help desk and email tools.
- Recommendations are generic rather than based on what each shopper wants.
Our Approach
We deploy an Aurora support agent connected to your store platform and help desk. It handles order status, returns within policy and product questions from your catalogue, suggests relevant products, and hands refunds outside policy or complaints to your team.
- Order status, delivery and returns handled end to end within your policies.
- Product questions answered from your live catalogue.
- Personalised product suggestions in chat and email.
- Hand-off to agents with the conversation and order details attached.
Agents used: Aurora AI customer support agent, Orbis business analytics agent
Typical Rollout
Discovery
Map the current process, systems and data, agree on the baseline numbers and how success will be measured.
Pilot build
Build and connect the agent for one scope: order-status and returns conversations on chat. Set permissions, guardrails and hand-off rules.
Supervised pilot
Run on real cases with a person reviewing the agent's work, tuning it until results are reliable.
Review and scale
Compare results with the baseline and extend to more teams, sites or tasks only if the pilot clearly pays off.
Timings are indicative and depend on your systems and scope.
What We Measure
We record these before the pilot starts and compare them afterwards, so you can see whether it worked.
- First response time
- Share of conversations resolved without an agent
- Customer satisfaction score
- Conversion rate of chat sessions
Explore This for Your Business
Tell us about one process that takes too much of your team's time. We'll tell you honestly whether AI can help and what a pilot would look like.