Team
AI automation for customer support that answers the repetitive questions, drafts replies for your agents and hands the hard cases to a person with the context attached.
Sound familiar?
Look at a month of support tickets for most businesses and you'll find the same handful of questions over and over: where is my order, how do I return this, can I change my address, does this fit my model. Each one is easy. Together they fill the queue and push the genuinely tricky cases further down.
AI automation for customer support is most useful when it takes that repetitive volume off your team without pretending to be smarter than it is. We build a customer support agent that answers from your own policies and live order data, triage automations that tag and route tickets in your helpdesk, and reply drafts that agents check before sending.
The quality of the answers depends almost entirely on the quality of the source material. That's why many projects include a knowledge base assistant that keeps track of what customers ask that your help center doesn't cover, so the gaps get written instead of guessed at.
We design every support automation with a clear handoff. When the agent isn't confident, the question involves money or the customer is upset, it passes the conversation to a person with a summary and the order details already attached. Customers don't have to repeat themselves, and your team sees exactly what the AI said. For online stores, this pairs naturally with our wider work for ecommerce brands.
The problem
Support work is a mix of quick lookups and real problem-solving. The trouble is that the lookups crowd out the problem-solving.
Order status, delivery dates and return progress make up a large share of tickets for most online businesses. Answering each one means opening Shopify or the 3PL portal, finding the order, checking the tracking link and writing a reply. That's two or three minutes of pure lookup per ticket.
Someone has to read each new ticket, decide what it's about, tag it and send it to the right person or queue. When volume spikes after a launch or a sale, this is the step that backs up first, and urgent issues get buried under simple ones.
Even with macros, agents spend time adjusting templates to fit the situation and the customer's tone. New hires take weeks to learn which policy applies when.
The real answers to edge cases are often in old Slack threads or a senior agent's memory. When that person is off, quality drops.
Tags are applied inconsistently, so contact-reason reports are unreliable. Product and ops teams don't hear about recurring problems until they're large.
AI handles the lookup, sorting and first-draft parts of this well. It's much weaker at judgment calls on refunds, angry customers and anything outside your written policies, so we keep those with your team.
For example, a Shopify brand might start by letting an agent answer order-status and return-policy questions on chat only, with every other topic handed to a person. The team reviews transcripts daily for the first couple of weeks, fixes gaps in the help center and adjusts the handoff rules. Only once those answers are consistently right do they widen the scope to email or add new topics. Starting narrow is slower on paper, but it builds trust with both customers and the support team.
Where to start
When a customer asks where their order is, the agent identifies them, looks up the order in Shopify or WooCommerce and the carrier tracking, and replies with the real status. If the parcel looks stuck or the order isn't found, it hands off to a person.
Every new ticket in Zendesk, Gorgias or Intercom is classified by topic, urgency and sentiment, tagged consistently and routed to the right queue. Angry customers and possible chargebacks jump to the top instead of waiting their turn.
For tickets that need a person, the agent sees a suggested reply inside the helpdesk, written in your tone and grounded in the relevant policy, with the source linked. They edit and send, which keeps the human judgment and cuts the typing.
The agent collects the order number, reason and photos, checks the request against your return window and policy, and either starts the return or prepares the case for an agent to approve. Exceptions always go to a person.
Questions the AI couldn't answer from your help center are grouped and reported weekly, so your team knows exactly which articles to write or fix. Over time the share of questions the agent can answer correctly goes up for the right reason.
A voice or chat agent handles simple out-of-hours requests, takes structured messages for the rest and creates tickets with a summary, so the morning shift starts with context instead of voicemail.
Integrations
Zendesk, Gorgias, Intercom, Freshdesk, Help Scout and Front, so the AI works inside the queue your team already uses.
Shopify, WooCommerce, ShipStation, AfterShip and carrier APIs for live order, fulfillment and tracking data.
Loop, AfterShip Returns and our own WarrantyHub for Shopify, so return and warranty questions are answered from real records.
Your help center, Notion, Confluence, Google Docs and past resolved tickets, indexed so answers cite where they came from.
Email, website chat, WhatsApp, Instagram DMs and phone through Twilio or similar providers.
Claude or OpenAI models with retrieval over your content, typically stored in Postgres with vector search so you own the data.
FAQ
It can if it's built carelessly, which is why we restrict answers to your own policies and live data and tell the agent to say it doesn't know when the source doesn't cover the question. We test it against hundreds of real past tickets before launch and review transcripts closely in the first weeks.
You set the rules. Typical triggers are low confidence, refund or compensation requests above a threshold, legal or safety issues, and frustrated customers. The handoff includes a summary and the order details so the customer doesn't repeat themselves.
Order and contact details are fetched only when needed to answer a question, and we use model providers' business terms where data isn't used for training. We can redact payment details and other sensitive fields before anything reaches a model, and logs follow the retention rules you set.
That isn't how we'd frame it. The realistic aim is that your team stops answering the same lookup questions and spends more time on the conversations that keep customers. Most businesses use the capacity to improve response times or absorb growth.
There's a build cost and an ongoing model and hosting cost that scales with conversation volume. We estimate the running cost per conversation from your ticket history before you commit, so you can compare it with the time it saves.
Send us a rough picture of your ticket volume and top contact reasons. We'll tell you which ones AI automation for customer support can handle well and which should stay with your team.
Each solution is built from one or more of these. Follow a link for the detail on scope, stack and process.
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ExploreWe build support agents that work inside Zendesk, Intercom, Gorgias or your own chat widget, answer from your help center, look up live orders and accounts, and escalate cleanly.
ExploreWe design, build and run AI agents and chatbots that answer from your real documents, take approved actions in tools like Zendesk, HubSpot and Shopify, and know when to pass a conversation to your team.
ExploreWe build internal assistants in Slack, Microsoft Teams or a web app that search your company documents with retrieval-augmented generation, respect access permissions, and cite where each answer came from.
ExploreWe design and build custom web apps with AI where it helps: internal tools, portals, SaaS MVPs, LLM features and integrations. You own the code, the data and the accounts.
ExploreWe add large language model features to existing web apps and platforms, with evaluation, guardrails, cost controls and a design that lets you swap models later.
ExploreWe build phone agents on Twilio and voice platforms like Vapi or Retell, connected to your calendar, CRM or order system, with clear rules about what they handle and when a human takes over.
ExploreHow we designed a Shopify app that syncs supplier stock and prices from any file or API, shows a diff before writing, and refuses to import files it doesn't trust.
Read the storyHow we turned a Google Form, a serial-number spreadsheet and a claims inbox into one Shopify app that registers units, issues coverage and resolves claims with real Shopify objects.
Read the storyHow we built a WooCommerce plugin that takes a quote from an Add to Quote button through PDF quotations and negotiation to a paid order, without leaving the store.
Read the storyTell us how it works today. We'll tell you honestly what's worth automating, what it would take, and what it would cost.