AI & INTELLIGENT SYSTEMS ENGAGEMENT STORY
Piloting an AI support assistant for a growing commerce business
A carefully scoped AI pilot that helped a support team find trusted answers faster while keeping people in control of customer conversations.
Client names and commercially sensitive details are withheld. This story focuses on the delivery approach and the type of progress the engagement supported.
CASE STUDY OVERVIEW
A support assistant designed around trusted information and human review.
A commerce business was receiving a growing volume of repeat customer questions while its support team searched across policies, product information, and order tools for answers. Rather than launch a broad autonomous chatbot, Yuowl Labs helped the team test a smaller internal assistant. The pilot focused on answering routine questions from approved sources and making it clear when a person needed to take over.
Making AI useful without asking it to do too much
The team wanted to reduce repeat work while protecting accuracy, customer trust, and operational control.
Answers spread across tools
Policies and product details were stored in several places and changed regularly.
High volume of repeat questions
Support agents spent time answering routine queries before they could help with exceptions.
Accuracy concerns
The business needed a safe way to handle uncertainty and avoid unverified responses.
No agreed pilot measure
The team needed to define what a useful, responsible AI trial would look like.
A grounded assistant connected to approved support knowledge
The pilot was deliberately narrow: help agents find and draft better answers, then learn from real use.
Choose the first use cases
Selected repeat question types where the team had reliable source information and clear ownership.
Prepare the knowledge base
Organised approved content, permissions, and update responsibilities around the pilot scope.
Build review into the workflow
Designed the assistant to show sources and hand the final response back to the support team.
Evaluate and refine
Reviewed answer quality, gaps, and agent feedback before considering wider use.
Capabilities used in this engagement
The work combined AI product design, knowledge preparation, and integration thinking around a single support workflow.
AI workflow design
A focused assistant experience designed around the support team’s actual process and review needs.
- Use-case selection
- Human-in-the-loop design
- Escalation paths
Grounded knowledge retrieval
A structure for retrieving from approved, maintainable business content rather than relying on unsupported answers.
- Source-aware responses
- Content boundaries
- Update ownership
Pilot evaluation
A practical way to review usefulness, answer quality, and the right next investment.
- Representative test set
- Agent feedback
- Improvement backlog
A controlled way to learn where AI can help support teams
The pilot gave the business a useful starting point without making unsupported promises about automation.
Created a single, more accessible route to approved support information
Helped agents draft routine answers with clear source context and human review
Identified the content gaps and ownership needed for future automation work
Established practical evaluation criteria for answer quality and safe escalation
Produced a measured roadmap for extending AI only where it adds value