Giving agents the right answer at the right time
AI-powered research and response tools for Shopify's support agents
Shopify's support agents were spending too much time searching for answers during live conversations. I directed the research effort to understand why, convening subject experts including help center supervisors, frontline agents, and VP-level support leadership. I traveled to BPOs and internal call centers to observe how agents actually work, then used those findings to design AI-powered tools that surface the right information at the right moment.
The research
I started by going to the source. I sat with agents as they worked live tickets, watched supervisors triage queues, and interviewed support leadership about where the gaps were. The pattern was clear: agents knew the answers existed somewhere, but finding them in real time was the bottleneck. They were toggling between knowledge bases, internal docs, and Slack channels while customers waited.
Field work
I traveled to multiple BPOs and internal call centers to see the full range of environments these tools would need to work in. The conditions varied wildly: different hardware, different workflows, different levels of experience. Any solution that only worked in ideal conditions would fail in the field. The research informed every constraint we designed around.
The solution
We designed an AI layer that listens to the live conversation and proactively surfaces relevant research summaries, suggested responses, and source material. Agents get what they need without leaving the conversation window. The system cites its sources so agents can verify before sending, and a feedback loop lets them flag bad suggestions to improve the model over time.
A two-way knowledge system
The assistant didn't just pull from existing help center content. It also learned from the agents themselves. When human agents resolved issues in ways the system hadn't seen before, those resolutions were captured and fed back into future help center articles. The help center got smarter because the agents were using the tool, and the tool got smarter because the help center was improving. Each side made the other better.
The impact
The assistant improved solution rates and first contact resolution across the board. The biggest surprise was onboarding: new agents ramped up 50% faster because the assistant acted as a real-time coach, guiding them through unfamiliar topics while they learned the product. Senior agents spent less time answering the same questions, and the quality of support stayed consistent regardless of who picked up the ticket.
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