AI Agents & Chatbots
An AI customer support agent that resolves routine tickets from your real policies and order data, and passes the rest to your team with full context.
What you get
In most support queues a large share of tickets are the same handful of questions: where is my order, how do I return this, can I change my address, why was I charged twice. Your team answers them well, but slowly at peak times, and the complex tickets wait behind them.
An AI customer support agent takes the routine ones off the pile. It reads the customer's message, retrieves the relevant policy, looks up the order or account through your systems, and either resolves the request or prepares everything a person needs to finish it. It works inside your existing helpdesk, so your agents see every AI conversation alongside their own.
We focus on the parts that decide whether customers trust it: answers grounded in your current policies, actions limited by rules in code, and a handoff that does not make the customer start again. This is one of the four agent types we build under AI agents and chatbots, and it fits especially well for ecommerce brands and subscription businesses with high ticket volume.
The problem
Support teams rarely struggle with the hard tickets. They struggle with volume. A sale, a shipping delay or a product launch doubles the inbox for a week, first-response times stretch to a day or more, and frustrated customers send follow-ups that make the queue even longer.
Older chatbots tried to fix this with decision trees. Customers learned to type "agent" until they reached a person. Macros and canned replies help your team move faster, but someone still has to read every ticket, open the order, and pick the right reply.
Meanwhile the information needed to answer is already in your systems: the tracking number in Shopify, the return window in your policy page, the subscription status in Recharge or Stripe. The gap is reading the question, finding the facts and writing a correct, polite answer. That is the gap an AI customer support agent fills, as long as it is built with care about what it may and may not do.
What we build
Retrieves the current help center article or policy and answers from it, quoting the relevant rule instead of paraphrasing from memory.
Fetches order status, tracking, subscription state or invoice history through APIs, so answers reflect what is true right now.
Creates return requests, updates shipping addresses before fulfillment or resends invoices, within limits enforced in code.
Classifies every incoming ticket by topic, urgency and sentiment so the ones it does not resolve land in the right queue.
Escalates to a person with a short summary, the data it found and what it already told the customer, so nobody repeats themselves.
In draft mode it writes the reply inside the helpdesk and your agent edits or sends it, which is how most rollouts begin.
Scenarios
A customer writes in Gorgias asking about an order placed last week. The agent finds the order in Shopify, reads the carrier status, explains the expected delivery date and, if the parcel has been stuck for more than your set number of days, opens a carrier claim and hands off.
The agent checks the purchase date against your return window, confirms the item is eligible, generates the return request and sends instructions. Final-sale items or anything outside policy go to a person with the reason noted.
A wholesale customer asks for a copy of last month's invoices and their current credit balance. The agent pulls both from QuickBooks or Xero and replies, and routes any dispute about the amounts to accounts receivable.
During a sale, the agent handles first responses across chat and email, resolving the routine requests and tagging the rest by urgency so your team works the important tickets first.
How we build it
We export a few months of tickets and group them by intent. That shows which request types are frequent, low-risk and resolvable with data you have. Those become the agent's first scope. Everything else is handoff from day one.
The agent answers from an index of your help center and internal macros, kept in sync automatically. Live data comes through narrow tools, such as an order lookup that requires the order number and the customer's email to match. Write actions like returns or address changes have rules in code: time windows, value caps, order states. Refunds above a threshold always go to a person. The model connection and tool layer follow the same patterns as our LLM integration work.
We turn historical tickets into a test set with expected answers and actions, then run it on every change. Launch usually starts in draft mode for one or two ticket types, where your team approves each reply. As the approval rate holds up, we switch those types to auto-send and add the next ones. A weekly review of sampled conversations keeps quality visible. You can read more about the stages in our process.
FAQ
Most often Zendesk, Intercom, Gorgias, Freshdesk and Help Scout, plus email inboxes and custom chat widgets. If your helpdesk has an API and webhooks, we can usually work with it.
It depends heavily on your ticket mix and how much of the answer lives in your systems. Stores with lots of order-status and returns questions see a bigger share than businesses with complex technical support. We estimate it from your actual tickets during scoping rather than quoting a generic number.
We recommend being upfront about it, and in some places disclosure is required. In practice customers care more about getting a correct answer quickly and being able to reach a person easily than about who wrote the reply.
It can if you want it to, within limits you set in code, such as a maximum amount, specific reasons and eligible order states. Many teams keep refunds human-approved and let the agent prepare everything for a one-click decision.
Then the agent will give out-of-date answers, so we review the content first. Ticket analysis usually reveals which articles are missing or wrong, and fixing them helps your human team too.
Current models handle most major languages well, and the agent can reply in the customer's language while reading English policies. We test the languages you need before turning them on.
Share a rough picture of your ticket volume, helpdesk and the most common request types. We will tell you which are a good fit for an AI customer support agent, which should stay with people, and how a draft-mode pilot would work. Also see the customer support solutions page.
We pick tools for your situation, not ours. These are the ones we reach for most on this kind of work.
Human-in-the-loop design lets you put AI to work on real processes while people stay in charge of the decisions that matter. Here are the patterns, thresholds and review screens that make it work.
Which parts of customer support AI handles well, which it doesn't, and a step-by-step way to roll it out without frustrating customers or your support team.
What should happen in the first hour after an inbound lead arrives, and how to automate capture, enrichment, qualification, routing and follow-up without losing the human judgment that closes deals.
Tell us how it works today. We'll tell you honestly what's worth automating, what it would take, and what it would cost.