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Ecommerce Customer Service Automation
Ailoitte builds AI chatbots for customer service that resolve ecommerce tickets end to end: where is my order, returns, exchanges, address changes and product questions. They read live data from your storefront, order system and helpdesk, act within the limits you set, and answer only from your own policies. When a case needs judgment, they hand it to your team with the full conversation and order attached.
Send a sample of last month's tickets. Within a week: which ones an agent can resolve safely, how it should work, and the fixed price.
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Answers from your data
Live order, stock and policy. It does not guess.
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Affordable by design
Fixed price from $4,000. No per-resolution fee.
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A person when it matters
Handoff with the chat, the order and a summary.
AI customer support automation, in six answers.
What we build
Support agents for chat, email and messaging that resolve order, return and product tickets by acting in your store, order system and helpdesk.
Why it gets answers right
It reads the live order before it replies, and it quotes your policy rather than writing one. If the data is missing, it says so and hands over.
How long it takes
A first agent in production in 4 to 8 weeks, including the store, order system and helpdesk work, and two weeks on real tickets in shadow mode.
What it costs
A fixed price per agent, $4,000 to $39,000 by scope and AI complexity, quoted after a free ticket audit. No per-resolution fee.
How it treats customers
It says it is an AI assistant, offers a person at any point, and never blocks the way to your team.
What you keep
The code, prompts, settings and test sets, assigned to you on payment. You pay AI usage directly, usually far less than a per-ticket rate at volume.
Sunil Kumar 
What AI customer support for ecommerce is, and what it is not.
An ecommerce support agent understands a customer’s question, looks up their real order, stock or policy, and resolves the request inside your systems: it shares tracking, starts a return, changes an address or answers a product question. It is not a decision tree with predetermined buttons, because customers rarely ask questions in the sequence you anticipated. And it is not a general chatbot that writes fluent answers from memory, because in commerce a confident wrong answer about a refund is worse than no answer.
Two facts shape how we build. Customers are cautious: in a Gartner survey of 5,728 people, 64 percent said they would prefer companies did not use AI for service, and the top worry was not being able to reach a person (Gartner). And complete automation can backfire: Klarna said in 2024 its assistant did the work of 700 agents, then in 2025 began hiring people again after quality slipped (Entrepreneur).
So our ecommerce customer service automation resolves the repetitive tickets fully, keeps a clear route to your team, and is measured on resolved tickets and complaints, not on how many chats it deflects.
Already running a support chatbot?
We can check what it tells customers about refunds, delays and returns against your real policy, before a customer screenshots the gap.
Two jobs, one agent
Where does your support queue cost you most?
Most ecommerce support tickets fall into two categories. After the order, customers want to know where it is and how to send it back. Before the order, they have a question that stands between them and checkout. We scope each job separately, because they need different data and different limits.
Job 01
After the order: tracking, returns and changes
For support and operations teams.
- Where is my order, with live carrier status
- Returns and exchanges started in chat
- Address and item changes before dispatch
- Refund status from your payment records
- Damaged item reports with photos attached
Job 02
Before the order: questions that block a sale
For ecommerce and merchandising teams.
- Product, size and fit questions from your catalog
- Stock and delivery date checks
- Comparisons inside your own range
- Help at checkout, without pressure
- Never invents a discount or a stock level
What changes
Your support queue, before and after automation.
Most tickets repeat the same few questions. Your team’s expertise belongs on the exceptional ones.
| Moment | Today | With a support agent |
|---|---|---|
| "Where is my order?" | An agent opens three tabs, copies the tracking link and pastes it back. | The customer gets live status and a delivery estimate in seconds, at any hour. |
| A return request | Back-and-forth emails to collect the order number, reason and photos. | Eligibility checked against your policy, label issued, return logged. |
| A late shipment | Customers chase you, and the delay notice goes out late or not at all. | The agent tells affected customers first, with the new date and the option to cancel. |
| A question at 11pm | It waits until morning, and the shopper buys elsewhere. | Answered from your catalog and stock, with a person the next morning if needed. |
| Handoff to a person | The customer explains everything again from the start. | Your team picks up with the chat, the order and a short summary. |
Why answers stay correct
AI chatbots for customer service fail on facts, not on tone.
Any language model can sound friendly and professional. The difficult part is being accurate about this particular customer’s order and your current policy, every single time. So the agent looks things up before it speaks, acts only within limits you set, and stops when a case needs a person.
Read the live order
- Order, payment and shipment looked up first
- Carrier status fetched, not assumed
- Customer identity checked before details
- No answer when the data is missing
Quote your policy
- Return and refund rules from one source
- Policy text cited in the reply
- Old policy versions retired on purpose
- Changes reviewed before they go live
Act within limits
- Refunds up to a value you set
- Address changes only before dispatch
- Every action written to the ticket
- Anything above the limit goes to a person
Know when to stop
- Hands over on anger or repeat contact
- Hands over on legal or safety words
- Says a person is coming, then goes quiet
- Tested on your hardest past tickets
What it handles
Sort my tickets
The tickets that fill an ecommerce queue.
Each agent begins with the ticket categories you answer most frequently, with its own data, limits and handoff rules. A focused agent that performs one job reliably outperforms a general-purpose bot that attempts everything.
Order tracking
Live status, delivery estimates and proactive delay notices, across chat, email and WhatsApp.
Returns and exchanges
Eligibility checked, labels issued and exchanges offered first, all within your policy.
Order changes
Address, size or item changes before dispatch, written straight to your order system.
Product questions
Size, fit, materials and compatibility, answered from your catalog and reviews.
Phone support
The same agent on the phone line for customers who would rather call.
Checkout help
Stock, delivery dates and payment questions answered before the cart is abandoned.
How many of your tickets repeat?
Send last month's ticket export, or just volumes by type. We will show which ones an agent could resolve, under your policies.
How we deliver
From ticket audit to a live support agent, in five stages.
A first agent in 4 to 8 weeks. Your team approves its replies before customers see them, so go-live is a decision based on evidence. Delivered on Clarity, our delivery method.
Stage 01
Ticket audit
A sample of real tickets sorted by type, volume and risk. Together we pick the first types to automate and the ones that must stay with people.
Stage 02
Data and policy wiring
Storefront, order system, carriers and helpdesk connected. Return, refund and delivery policies gathered into one source the agent quotes from.
Stage 03
Build and tune
Conversation flows, limits and handoff rules built in your brand voice. Tested against your hardest past tickets before any customer sees it.
Stage 04
Shadow on real tickets
For about two weeks the agent drafts replies your team approves or edits. We fix every error and agree the bar for going live.
Stage 05
Go live by ticket type
One ticket type at a time, with rollback ready. Resolution, reopen and complaint rates watched against your baseline. Then the next type.
Before it talks to a customer
Your chatbot's answer is your company's answer.
A support agent speaks for your brand in writing, thousands of times a day. That makes accuracy a legal matter as well as a service one, and it is why we ground every answer and keep a person within reach.
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01
Its promises bind you
In February 2024 a Canadian tribunal held Air Canada liable after its chatbot invented a refund rule. The airline argued the bot was responsible for its own words; the tribunal disagreed (American Bar Association).
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02
Customers must know it is a bot
California has banned bots that hide what they are to drive a sale since July 2019 (Perkins Coie). EU rules require people to be told they are talking to AI from 2 August 2026 (McCann FitzGerald).
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03
Delay answers carry duties
Under the FTC shipping rule, a seller who cannot ship on time must tell the customer, give a new date and offer to cancel with a prompt refund (FTC). The agent has to say the same.
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04
People want a way out
Not being able to reach a person is the top fear customers have about AI service (Gartner). Hiding the route to your team turns a saving into churn.
Controls
Automation your support lead can sign off on.
A bot that deflects tickets while customers get angrier is a cost, not a saving. Our AI customer support automation includes these controls by default. See security and compliance and private AI deployment if customer data must stay inside your own cloud.
Clear handoffs
- A person on request, any time
- Handoff on frustration or repeat contact
- Chat, order and summary passed on
- Customer told who picks up and when
Money limits
- Refund and credit caps you set
- Approval queue above the cap
- No payment details in the chat
- Every action in the audit log
Your brand voice
- Tone written from your guidelines
- Banned words and claims listed
- Replies checked on a sample each week
- Same voice on every channel
Watching it work
- Resolution and reopen rates
- Complaints and handoffs tagged
- Wrong answers reviewed weekly
- Results reported against your baseline
In production
View Portfolio
Commerce and conversational systems we have shipped.
AI customer support for ecommerce is only as reliable as the commerce systems behind it. We have built the stores, order tracking and inventory sync it reads from, and assistants that resolve routine questions with people in the loop. See ROI case studies.
Pricing
What ecommerce customer service automation costs.
A support agent costs $4,000 to $39,000, fixed, depending on the ticket types and how much AI it needs. A tracking and returns agent on one store sits toward the low end; an agent across several channels, languages and order systems, with refunds and exchanges, sits toward the top.
There is no per-resolution fee. Packaged tools often charge per outcome: Intercom lists its Fin agent from $0.99 per outcome (Intercom pricing). At 20,000 resolved chats a month that is about $19,800 a month. Below a few thousand a month, a packaged tool is often the better buy, and we will say so. Larger programs can run as an AI Velocity Pod from about $15,000 a month.
Stack and integration
Built into the store and helpdesk you already run.
Every action lands on the order and the ticket, so your team sees what the agent did. An agent operating in a separate inbox creates additional work instead of removing it.
Storefronts
Shopify, Adobe Commerce (Magento), WooCommerce, BigCommerce or a custom store.
Orders and ERP
Your order management system or ERP, read and written through its API.
Helpdesks
Zendesk, Gorgias, Freshdesk or Intercom, with every action on the ticket.
Channels
Web chat, email, WhatsApp and Instagram, handled by one agent.
Shipping
Carrier and tracking APIs for live status and delivery estimates.
Payments
Refund status and capped refunds through your payment provider.
AI models
Your choice of provider, or a private model in your own cloud.
Monitoring
Resolution, reopen and handoff rates, with weekly answer review.
FAQ
Ask a commerce AI lead
Ecommerce support automation, answered.
What is ecommerce customer service automation?
Software that resolves routine store tickets without a person, by looking up real order and policy data and acting in your systems. Typical jobs are order tracking, returns, exchanges, address changes and product questions. Anything that needs judgment goes to your team.
How are AI chatbots for customer service different from older chatbots?
They understand free text and can act, instead of offering fixed buttons. Older bots followed decision trees and dead-ended when customers went off script. A modern agent understands the question, checks the order and resolves it, within limits you set.
Which tickets can be automated first?
Order tracking, returns and simple order changes. They are frequent, follow clear rules and depend on data your systems already hold. We confirm the order of work from a sample of your real tickets.
Will the AI give customers wrong answers?
It can, so we design against it. The agent reads the live order before replying, quotes your policy from one source, and hands over when data is missing. Two weeks of shadow mode on real tickets catches most errors before launch.
Are we liable for what the chatbot says?
Yes, in most cases you should assume so. In February 2024 a Canadian tribunal held Air Canada liable for a refund rule its chatbot invented. This is general information, not legal advice.
Do we have to tell customers they are talking to AI?
Often, and we always do. California bans bots that hide their nature to drive a sale, and EU rules require AI disclosure from August 2026. Being open also builds trust.
Can customers still reach a person?
Always, on every channel, at any point in the conversation. Customers can ask for a person directly, and the agent hands over on frustration or repeat contact. Your team gets the chat, the order and a summary.
Which platforms does it work with?
Shopify, Adobe Commerce (Magento), WooCommerce, BigCommerce and custom stores. We also connect order systems, ERPs, carriers and helpdesks such as Zendesk, Gorgias and Freshdesk.
Can it issue refunds?
Yes, up to a limit you set. Above that limit it prepares the refund and sends it to a person to approve. Every action is logged on the order and the ticket.
Can it sell as well as support?
Yes, within honest rules. It answers product, size and stock questions and can suggest items from your range. It will never invent stock levels, countdowns or discounts.
Which channels does it cover?
Web chat, email, WhatsApp and Instagram, plus phone if you add a voice agent. One agent and one set of policies serve every channel, so answers stay consistent.
Does it speak in our brand voice?
Yes. We write its tone from your guidelines, list words and claims it must avoid, and review a sample of replies each week.
How long does implementation take?
A first agent in production in 4 to 8 weeks. That includes systems work and about two weeks of shadow mode. A program across many ticket types takes 3 to 6 months.
How much does AI customer support automation cost?
Between $4,000 and $39,000 per agent, fixed, depending on the ticket types and the AI involved. There is no per-resolution fee; you pay AI usage directly. We quote after a free ticket audit.
Do you publish deflection or cost-per-ticket figures?
No, not as general claims. Results depend on your ticket mix, policies and data quality. We measure resolution, reopen and complaint rates in the pilot and report them with the conditions stated.
A question about your own store?
Send it over. A commerce AI lead replies within 12 hours.
Start with the tickets you answer most.
Tell us your platforms, channels and ticket volumes. A senior architect will tell you which tickets an agent can resolve safely, what it should never touch, and whether a packaged tool would serve you better.
- Mutual NDA before we see ticket data
- A commerce AI architect on the call, not a salesperson
- A free ticket audit and fixed price within a week
- No obligation, and nothing to pay to find out
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My experience working with Ailoitte was highly professional and collaborative. The team was responsive, transparent, and proactive throughout the engagement. They not only executed the core requirements effectively but also contributed several valuable suggestions that strengthened the overall solution. In particular, their recommendations on architectural enhancements for voice‑recognition workflows significantly improved performance, scalability, and long‑term maintainability. They provided data entry assistance to reduce bottlenecks during implementation.
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