E-commerce chatbot and customer-service AI.
An e-commerce chatbot for the questions that fill your inbox — where is my order, can I return this, does it ship to my country. Grounded in your help centre and order data, with a handoff to a human agent that actually carries the context.
Most e-commerce support volume is a short list of questions asked thousands of times: order status, delivery date, returns eligibility, refund timing, size and fit, does-it-ship-here. Those are the tickets an e-commerce chatbot should take, because the answer already exists in your help centre, your order system or your shipping data. Everything else should reach a human quickly and with context attached.
The failure mode of customer-service AI is not rudeness, it is confident wrongness — promising a refund policy you do not have, or a delivery date the carrier never quoted. So we ground every answer in retrieved documents and live order data, and we make the bot cite what it used. Where it cannot ground an answer, it hands off. A deflection that creates a second angrier ticket is not a deflection.
This is one strand of our AI for e-commerce work. If your problem is product discovery rather than service volume, look at the AI shopping assistant; if it is the back-office work behind the tickets, see e-commerce automation.
Six parts of a chatbot you can put in front of customers.
Grounded answers
Retrieval over your help centre, policy pages, shipping matrix and product data. Answers cite their source so your team can audit them, and so a wrong article gets fixed rather than re-prompted.
Live order lookup
Authenticated access to order status, tracking and returns eligibility from Magento, Shopify or your OMS — so “where is my order” gets an actual answer, not a link to a form.
Handoff that carries context
Escalation into Zendesk, Gorgias, Freshdesk or Intercom with the transcript, the retrieved sources and the customer record attached. The agent starts informed, not from zero.
Guardrails and refusals
No invented policy, no discount it is not authorised to offer, no advice on payments disputes or health claims. Sensitive intents route straight to a human by design.
Evaluation harness
A golden set built from your real historical tickets, scored for correctness and for tone, re-run on every change. Deflection rate is meaningless without a correctness number beside it.
Multilingual by default
One knowledge base, answers in the languages your markets buy in — with a per-language accuracy score, because quality does not transfer automatically.
How we build one.
Mine the tickets
We cluster twelve months of your support history to find the real top intents and their real volumes. That decides what the bot handles — not a list of features from a vendor deck.
Fix the source of truth
Retrieval is only as good as the help centre behind it. We flag the policies that are missing, contradictory or out of date before the bot starts quoting them.
Shadow on real tickets
The bot drafts answers your agents see but customers do not. You compare its draft with what the agent sent, on your own volume, before any customer meets it.
Ramp by intent
Live on the safest intents first, widening as the correctness score holds. Every intent has an explicit escalation path and an off switch.
The four numbers we report on.
We treat reopen rate as the honest counterweight to deflection: a bot can “resolve” everything and simply move the work to tomorrow. Client-specific figures are shared under NDA — see our work for named engagements.
The stack.
Common questions.
How is this different from the chatbot our helpdesk already offers?
Bundled bots are usually decision trees with a text box, tuned for coverage rather than correctness, and they rarely reach your order data. We build retrieval over your actual content, wire in authenticated order lookup, and hold the thing to a correctness score on your own historical tickets. If your bundled bot already deflects well, we will tell you to keep it.
What happens when it does not know?
It says so and escalates, with the transcript and retrieved sources attached. We tune deliberately toward escalation rather than toward coverage, because a wrong policy answer costs far more than a handoff.
Can it access customer orders safely?
Only behind authentication, only the fields it needs, and with every lookup logged. The model never receives your full customer database — it receives the specific record for the authenticated session, scoped to that conversation.
Do you use our tickets or customer data to train models?
No. Your data is used for retrieval at query time and for building the evaluation set, both inside your own environment. It is not used to train third-party models, and data residency is settled in writing before we start.
How long until it is answering real customers?
Typically six to eight weeks to a shadow launch on real tickets, then a staged ramp by intent. The slow part is not the model, it is agreeing what the correct answer is for your top forty intents.
Where to next.
Bring us your last thousand tickets.
We will cluster them, tell you what share a chatbot could safely answer today, and what your help centre needs fixed first. No deck required.