Reads live from your commerce platform and logistics provider to answer order, delivery, return and exchange questions, and escalates the ones that need judgement.
The number that moves
Containment rate · cost per contact · first-response time
Industries · Retail & D2C
Retail, D2C & Ecommerce
Two things changed for retail at the same time: customers started asking on WhatsApp instead of raising a ticket, and they started asking ChatGPT instead of Google. Most storefronts are built for neither.
Support that scales without headcount, and a storefront the AI engines can actually read.
What this sector loses today
The leak is rarely where the budget goes.
Four patterns that recur here. If two are recognisably yours, the answer is usually smaller than the programme you were being sold.
"Where is my order" absorbs most of the support queue.
Support headcount scaling with order volume for a question a system can answer.
Pre-purchase questions on WhatsApp and Instagram go unanswered for hours.
The highest-intent moment in the funnel, missed.
Product content produced one asset at a time.
Catalogue expansion limited by copywriting throughput.
AI engines describe your category and name a competitor.
Lost consideration that never appears in any analytics you own.
What changes
How the system behaves, before and after.
These are properties of the architecture, true from the day it goes live. We baseline the numbers underneath them before we build, so the delta is measured against your own starting point rather than against an industry average.
Applications
Where AI actually pays here.
Each is a defined build with a number against it. They can be scoped and bought individually.
Sizing, materials, compatibility, stock and delivery estimates from your own catalogue, with the ability to complete or recover a cart.
The number that moves
Enquiry-to-order rate · after-hours capture
How six AI engines describe your brand and products against named competitors, where they cite others, and the structural fixes to your content and data.
The number that moves
Share of voice in AI answers · citation rate
Product copy, variants, translations and campaign assets through a brand-trained pipeline with human approval on every piece.
The number that moves
Cost per asset · time to publish · catalogue coverage
Shopify or custom, built for Core Web Vitals, structured so engines can parse your products, and integrated with your CRM and logistics.
The number that moves
Conversion rate · Core Web Vitals · time to publish
What we would build first
Order matters more than ambition.
Wrong order is the commonest reason AI work here stalls. This is the order we would argue for.
01
Take the repetitive contacts out
Order status first. It is the largest, most mechanical share of the queue and it needs no judgement at all.
02
Answer before the purchase, not after
Pre-purchase questions on the channel they actually arrive on. This is the one that moves revenue rather than cost.
03
Fix how the engines see you
Structured product data and answer-shaped content, so you are named when somebody asks an AI engine for your category.
Before your risk function signs
What they are going to ask.
In this sector the review is the schedule. All of it is designed in from the first architecture session.
- Customer data handled under a documented retention and deletion policy
- The agent never invents stock, delivery dates or specifications — it reads live or it escalates
- Refund, return and goodwill decisions gated to a person above a threshold you set
- Brand voice enforced through the content pipeline, with human approval on every published asset
- Model and infrastructure costs billed at cost against a ceiling you approve
Questions we get in this sector
The ones that decide it.
Will it make up a delivery date if the courier data is missing?
No. It reads live from your platform and your logistics provider, and where the data is not there it says so and escalates. Refusing is a designed behaviour, not a failure — an agent that invents a delivery date creates a worse problem than the one it solved.
Can it handle returns and refunds?
It can handle the process and the information. The decision is gated to a person above whatever threshold you set, because the cost of being wrong is real money and a customer relationship.
What does AI search visibility actually change for a D2C brand?
A growing share of category research now happens inside AI engines, which name one or two brands and never send a click. We measure how six engines describe you against named competitors and fix the structural reasons you are skipped — product data, entity clarity, answer-shaped content. It runs on GenAI Ranker, the platform we built and operate, which is why the first scan costs you nothing.
Put a number on it
The support-queue cost, as arithmetic
Four lines, and the blanks are deliberate — they are your numbers, not an average we invented. We fill them in with you on the call.
Support contacts per month____
Share that are order status or delivery____ %
Fully loaded cost per contact₹____
Containable spend= ____ × ____ % × ₹____
The second number is usually between half and two thirds, and almost nobody has measured it. The demo measures it on your real ticket mix.
Free working prototype
Point us at your store. We will show you an agent answering “where is my order” from live data — and what six AI engines say about your brand.
Two demos in one, because in retail the cost problem and the demand problem are the same conversation.
48 hours
Order status, answered from live data
48 hours
A pre-purchase agent on your catalogue
24 hours
What ChatGPT says about your category
48 hours
Product copy at volume, in your voice
This is the same free prototype that comes out of the 30-minute call and plan. It arrives faster here because the four scenarios above are already scoped.
What does the work
Stop reading about it. Watch it work.
Tell us the one thing that is costing you most, and we build it on your own material within 48 hours. Free, yours to keep, and the fastest way to find out whether any of this applies to you.