Shopify or WooCommerce, and “where is my order?” arrives at 11 pm on Instagram.
Ten to five hundred people, ticket volume growing faster than the team.
Clinics, salons, repair shops and logistics firms where the same person books, answers and bills.
Customer questions land in social inboxes nobody checks on a schedule.
Numbers from Gartner, HubSpot and Zendesk customer-service studies. Most automated customer service fails for one reason: the bot answers without knowing the customer, the order or the policy.
of customer service issues are fully resolved in self-service; even for very simple issues it is only 36%.
Gartner, 2024 ↗of consumers would switch to a competitor after a single bad service experience.
Zendesk CX Trends, 2025 ↗of support staff say customers expect an immediate resolution; the wanted time to fix is under three hours.
HubSpot State of Service, 2024 ↗of customers said the company did not understand what they were trying to do; in 43% of failed cases no relevant content existed.
Gartner, 2024 ↗is the cost of one ticket in retail and e-commerce; in SaaS it is roughly $25–$35.
LiveChatAI, citing MaestroQA and SaaS Capital ↗of consumers contacted support through social media DMs in the last three months; among millennials it is 30%.
HubSpot State of Service, 2024 ↗Put your monthly volume into the calculator and see hours and dollars.
Customer service automation is a workflow that receives every customer message — email, chat, social DMs, WhatsApp, web forms — in one queue, finds out who the customer is and what they bought, answers the questions it can verify from your own data, and hands the rest to an agent with a draft and the context already gathered. The point is not to replace the support team; it is to stop the team spending most of its day on “where is my order” and on hunting for customer data across tabs.
We build customer support automation around the helpdesk and store you already run: Zendesk, Gorgias, Help Scout, Freshdesk or plain Gmail, with Shopify, WooCommerce or your CRM behind it. A language model classifies each ticket by topic, urgency, sentiment and language. Order questions are answered with the carrier’s real tracking status. Policy and FAQ questions are answered from your knowledge base with the source cited, and only when the model is confident. Refunds, complaints and anything legal or medical go straight to a person.
First response drops from hours to seconds on the routine share of volume, agents open tickets that already contain the customer, the order and a draft, and nothing arriving at night on Instagram waits until someone remembers to check. Customer care automation done this way leaves people with the conversations that need judgement, and gives the support lead a weekly view of what customers keep asking.
The run path of a typical automated support workflow. Each message travels it on its own; the flow answers what it can prove and hands over the rest.
Email, website chat, Instagram or Facebook DM, WhatsApp Business or a web form — every message from every channel starts a run in one queue.
Looks up the sender in Shopify, WooCommerce or your CRM by email or phone and attaches orders, past tickets and plan.
A language model sets topic, urgency, sentiment and language and writes them as tags into Zendesk, Gorgias or Help Scout.
Negative tone, a legal or medical word, or a VIP account goes straight to the support lead in Slack with the thread attached.
For “where is my order” the flow pulls the order and the carrier’s tracking status and builds a reply with the exact date and link.
Searches your FAQ, return policy and past approved answers and writes a reply in your tone, citing the source it used.
A simple, well-sourced answer is sent; anything with low confidence, a refund or an exception becomes an internal note for an agent.
Replies in the customer’s channel, or assigns the ticket to an agent with the draft and the customer data already in place.
Compares the purchase date and item with your return rules and creates the RMA; anything outside the rules goes to a manager.
If the customer answers, the ticket reopens and runs again with the full thread; if not, it closes after your waiting period.
Sends a one-question survey by email or SMS and writes the score to HubSpot or Google Sheets.
Questions the model could not answer are collected for the person who maintains the knowledge base.
Volume by channel, first response and resolution times, share answered without an agent and the top recurring topics.
Typical minutes per ticket for a small support team. Your own numbers go into the calculator below.
Based on 13 min by hand and 1 min with the flow per item, from the table above.
The flow triages, looks up, drafts and answers the routine. People keep every decision that touches money, feelings or risk.
Any exception to the policy is approved by a person.
Escalated immediately, never answered by the model.
Routed to a named agent with the full history.
Agents review every draft until the accuracy is proven on your tickets.
Retention conversations and upset customers are handled by people.
Someone on your team owns what the model is allowed to say.
Flip a switch to hand a step to the flow or take it back.
Benchmarks from HubSpot’s State of Service 2024, Gartner’s self-service study and QuickBooks’ small-business survey: teams that use AI resolve a meaningful share of volume without an agent, and the rest reaches a person faster and with context.
We map the process as it runs today, count the minutes and agree what the flow must never do on its own.
A working flow on your real data, in a sandbox. You see every run and every exception.
Edge cases, approvals and alerts, then the switch-over — with the old way kept as a fallback.
Monitoring, fixes when a vendor changes a format, and a monthly report of hours saved.
Yes. The flow works through the APIs of Zendesk, Gorgias, Help Scout, Freshdesk, HubSpot and plain Gmail or Outlook. Tickets, tags and agents stay where they are; the automation adds triage, lookups and drafts around them.
It depends on your mix. Order status, delivery dates, opening hours and policy questions are the bulk of it; HubSpot reports 11–30% of volume at teams using AI. We measure your share in the first month instead of promising a number.
Every answer carries a confidence score and a source. Below the threshold, or for anything about refunds, complaints or legal topics, the ticket goes to an agent with the draft and the customer data already attached, and the customer is told a person is on it.
Order and customer data stay in your helpdesk and store. The language model receives only the ticket and the facts needed to answer, through an API that does not train on your data, or runs on your own server.
A single channel with order lookups and FAQ answers typically runs in a few weeks. First response time, resolution time, share handled without an agent and CSAT are tracked from day one, so the before-and-after is visible in the weekly report.
Yes. The model detects the language and answers in it, and Instagram, Facebook Messenger and WhatsApp are connected through the Meta and WhatsApp Business APIs, so a DM is a ticket like any other.
They are annoyed by a bot that cannot see their order or ignores “I want a human”. This one answers only what it can verify from your data, says when it is handing over and never argues with someone who asks for a person.
Describe it in two sentences — we reply within a day with a workflow sketch.
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