Worked example. This is an illustrative worked example, not a client result: the support team and its numbers are assumptions. It shows agent-side retrieval, a tool that sits behind the support agent, not in front of the customer. The agent asks a plain-english question, gets a cited answer, stays in the ticket and sends the reply.
Picture a mid-market e-commerce CX team that wants to move first-response time on complex tickets. This post covers what the design consists of, what to measure and what it does not do. It reports no result, because there is no client behind it.
The team is hypothetical, and so is every number you might expect here. The design is grounded in the customer-support work we describe on /ai-for/customer-support. One more thing up front: this is agent-assist only. No chatbot talks to a customer at any point.
#What was slowing the agents down?
The answers exist. Finding them does not scale. A complex ticket means an agent opening Zendesk history, a product doc, and a shipping-policy PDF, then stitching the answer by hand. Years of past tickets hold the precedent, and nobody can search that archive in the time a customer waits.
So the first reply sat in a queue while the agent hunted. That wait is the number the support lead gets asked about. First-response time, not ticket volume.
Can AI reduce first-response time in customer support without a chatbot?
Yes, that is the aim. An internal retrieval tool lets agents ask plain-english questions across historical tickets, product docs, and policy pdfs and get one cited answer. The agent still writes and sends the reply. A team would measure first-response time on complex tickets before and after, with no customer-facing bot.
#What does the design consist of?
One internal retrieval tool over three sources: historical tickets, the product docs, and the shipping-policy pdfs. An agent types a question the way they'd ask a senior teammate. The answer comes back with the source cited, so the agent can check it before it goes to a customer.
- Search across all past tickets in one query, not tab by tab.
- Cited answers, so the agent verifies the source instead of trusting a black box.
- Answers grounded in the shipping-policy pdfs, where the edge-case rules actually live.
- The agent stays in the ticket. The tool never replies to a customer on its own.
Training the support team is part of the design, not a handoff at the end. The KPI to agree on is first-response time on complex tickets, measured before and after. What it moves is for your own data to say, not this post.
#Isn't this the same as a deflection bot?
No, and the difference is the whole point. A deflection play tries to lower ticket volume by answering customers directly. This design targets a different number: first-response time, by putting retrieval behind the agent. The customer still talks to a person.
We are
Stennir builds AI that sits behind your support agents: retrieval, triage, draft-and-review, measured against first-response time and resolution quality.
We aren't
Stennir is not a customer-facing chatbot vendor and does not sell deflection as the goal; the agent stays in the ticket and your customers keep talking to humans.
Tools like Zendesk AI, intercom fin, or gorgias suggestions work inside their own walls: your macros, your help articles, your saved replies. This design adds retrieval across the rest. The historical tickets, the product docs, the shipping-policy pdfs, the runbooks. That's where the complex-ticket answers hide.
The KPIS we move are first-response time and resolution quality on complex tickets, not headcount. We say that on day one and we measure it.
#Would this work for our support team?
The fit test is simple. Do your answers already live in places that are slow to search? Years of tickets, a stack of policy pdfs, product docs that shift. If agents lose minutes hunting before every complex reply, that's the time retrieval is meant to give back.
This design sits in our AI consulting work, with the support-team training folded in from our AI training side. If you want to see how the pieces fit, the journal has more worked examples like it.
If your agents are hunting for answers before every complex reply, that is the delay this design goes after. tell us what you're building and we'll tell you whether retrieval is the lever, or whether it isn't.
