AI Customer Support
Answer the routine questions instantly.
Most support messages are the same handful of questions. An AI agent trained on your own material can settle those in seconds, and knows when to step aside.
The problem
Support volume on WhatsApp is lumpy and mostly repetitive. "Where is my order", "can I exchange this", "do you deliver to my pin code" arrive at all hours and account for a large share of the queue, while the genuinely difficult conversations wait behind them. Hiring for the peak is expensive and leaves people idle at the trough. The useful split is not human versus automated, it is routine versus not — and only the routine half should ever be automatic.
How it works
Set it up once. Then leave it running.
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Train it on your material
Point the agent at your knowledge base, product catalogue and past orders. It answers from what you give it, not from general knowledge.
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It handles the routine
Order status, returns policy, sizing and delivery questions get answered immediately, at any hour, in the customer's own thread.
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A human takes the rest
Anything outside its confidence goes to an agent in the shared inbox with the whole conversation attached, so nobody repeats themselves.
Message sequence
What your customer actually receives.
Every step is configurable — change the timing, drop a step, or keep it to a single message.
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Immediately
Customer asks
Where is my order #8821? It was supposed to arrive yesterday and I have not had an update.
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Seconds later
AI answers
Your order #8821 is out for delivery and should arrive today by 6 PM. Here is the tracking link if you would like to follow it.
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If it cannot answer
Handoff to a human · Optional
Let me get a colleague to help with that — they will pick this up here in a moment.
Automate the boring half, not the hard half
The temptation with support automation is to aim it at volume. The better target is repetition. A question that has one correct answer, the same every time, is a good candidate. A question where the right answer depends on judgement, or on how annoyed the customer is, is not.
The handoff is the feature
Most of the damage done by support bots comes from refusing to give up. A customer who has explained a problem three times to something that cannot help is worse off than if they had waited in a queue.
Setting the handoff threshold generously — erring towards passing things to a person — costs a little efficiency and buys a lot of goodwill.
Keep the source material current
An AI agent trained on last season’s returns policy will answer confidently and wrongly. The material it reads from is the actual product here, and it needs the same maintenance as any other customer-facing page.
Works with your store.
Order and cart data flows straight into the conversation.
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Shopify
Sync orders, carts and customers.
Frequently asked questions
Will the AI make things up about my products?
It answers from the material you give it — your knowledge base, catalogue and order data — rather than from general knowledge, and hands off when it is not confident. Keeping that source material accurate and current is the main thing that governs answer quality.
Can I limit when the AI replies?
Yes. You can restrict it to out-of-hours cover, to particular question types, or to a first response before a human takes over. It does not have to handle everything or nothing.
What happens to a conversation after a handoff?
It moves into the shared inbox with the full thread intact, so the agent can see exactly what was asked and what the AI already said. The customer does not have to start again.
How to set this up
The guides and articles behind this use case.
Guide
Setting Up an AI Agent
Configure an AI Agent to answer customer messages from your knowledge base, and set when it hands over to a human.
Guide
Building Your Knowledge Base
Organise your content into Collections so your AI Agents can answer customer questions accurately.
Product
AI Agents
Train an AI agent on your knowledge base, let it handle routine queries automatically, and hand off to a human the moment things get complex.
Put this to work on your store.
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