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Aplora
Case available

Serve a growing flow of orders and enquiries with the same team.

Online stores and retail chains running their own support, warehouse and repeat purchases: order, delivery, return, next order.

An online-shop worker at a laptop, surrounded by cardboard boxes of orders.

Key constraints

  • load arrives in spikes: season, sale, a collection launch

  • margin per order is thin, and every extra touch eats into it

  • part of the data sits with carriers and marketplaces, not with you

Industry economics

  • Profit is order margin minus acquisition cost minus service cost, so the economics break not when demand drops but when the number of touches per order grows.

  • Capacity is limited by support and the warehouse, not by the storefront: double the orders and enquiries roughly double too, forcing linear hiring.

  • Typical leaks: repeat questions eating the first line; returns processed by hand; stock reservations left hanging; the customer who never comes back after the first order.

  • The metrics that matter: share of enquiries reaching an operator, handling time, CSAT, repeat purchase rate, cost to serve an order, return rate.

Typical processes and pains

  • Symptom

    The support front line goes on repeat questions: where is my order, how do I return it, will this size fit.

    Economic consequence

    Complex enquiries get lost in the queue, and the only way to scale support is to hire.

    What share of your enquiries closes without an operator?

  • Symptom

    The knowledge base goes stale: it gets updated whenever someone finds the time.

    Economic consequence

    Answers diverge in wording and in substance, and the customer gets a different one depending on who replied.

  • Symptom

    Returns and exchanges are handled by hand: carrier label, status, customer notification, stock movement.

    Economic consequence

    Every return costs operator and warehouse time, and the stock reservation is released late — the item shows as taken while it could already be sold.

  • Symptom

    Customer communication is the same for everyone: one campaign to the whole list.

    Economic consequence

    The repeat purchase stays accidental while the cost of acquiring a new customer keeps rising.

Priority AI scenarios

Close routine enquiries without an operator

  1. 1Enquiry
  2. 2topic and urgency detection
  3. 3answer from the knowledge base or a clarifying question
  4. 4handover to an operator with a ready summary
  5. 5closure
Inputs
Ticket history, knowledge base, order statuses, return policy.
Output
A consistent answer to the customer, plus a summary for the operator when the topic is complex.
Where the human stays
The operator sees and can correct any answer; critical topics — payment, complaint, personal data — go to a person immediately.
Integrations
Helpdesk, store platform, knowledge base
Metrics
Share of enquiries reaching an operator, handling time, CSAT.
Limitations
With no knowledge base, or one that contradicts itself, the first job is to fix it — there is nothing to automate yet.

Keep the knowledge base alive from the enquiries themselves

  1. 1Collect repeating topics
  2. 2prioritise by frequency and cost to serve
  3. 3draft the article
  4. 4human review
  5. 5publish
Inputs
The enquiry stream and the topics the assistant could not answer.
Output
A ranked list of what to rewrite first, with a draft for each item.
Where the human stays
A person publishes. Return and warranty policy wording is never left to the model.
Integrations
Helpdesk, knowledge base CMS
Metrics
Share of enquiries with no answer in the base, time to update an article.
Limitations
Only works where someone reviews: with no content owner the draft queue simply grows.

Remove manual work from returns and stock reservations

  1. 1Request
  2. 2policy check
  3. 3carrier label generation
  4. 4customer notification
  5. 5warehouse intake
  6. 6reservation released and the item returned to sale
Inputs
The return request, order data, carrier rules, stock levels.
Output
A processed return with its label, and an accurate stock level without a manual recount.
Where the human stays
Contested cases — damage, broken seal, expired window — go to a person; the system never decides about money.
Integrations
Store platform, warehouse, delivery carriers, e-mail and SMS
Metrics
Time to process a return, share of returns needing manual work, time to put an item back on sale.
Limitations
Return rules are a legal document. While they read as “we decide case by case”, there is nothing to automate.

Where teams usually start

This is an observation across similar companies, not a universal recommendation: the order follows where your bottleneck actually is.
  1. 1Support is the usual starting point: it carries the most repetition and shows fastest whether the hypothesis holds.
  2. 2The knowledge base comes next — it feeds on the same enquiry stream and makes the first step stick.
  3. 3Returns and stock come later: they involve carrier systems, and the cost of a mistake is higher.
  4. 4Personalised communication comes last: it needs accumulated purchase history, not the first month of data.
  5. 5This is an observation, not a universal recommendation: the order follows where your cost to serve is actually growing.

Industry systems and data

  • store platform and catalogue
  • CRM and campaigns
  • helpdesk and support channels
  • warehouse and stock accounting
  • delivery carriers and tracking
  • marketplaces and ad platforms

Risks and constraints

  • Seasonality: comparing before and after across different seasons doesn't work — a sale changes both the volume and the mix of enquiries by itself.

  • Enquiries carry personal and payment data; the access and retention model is settled before work begins.

  • Some data lives with carriers and marketplaces: their availability and API limits are checked during assessment, not along the way.

  • Return and warranty policy wording stays with the lawyer. What gets automated is executing the rule, not writing it.

Cases

  • An agent in a headset working at a laptop in an open-plan office
    eCommerce and retail
    AI Workflow Automation

    Client under NDA

    An online store's support front line stopped growing with its order volume

    Operators were absorbed by repeat questions, complex enquiries got lost in the queue, and the knowledge base went stale faster than anyone could fix it.

    Enquiries that reach an operator
    −25–40%
    Ticket handling time
    −30–50%
    Support satisfaction (CSAT)
    +10–18%
    Read the case
  • A shop assistant with a laptop taking a customer's order on the sales floor
    eCommerce and retail
    AI Workflow Automation

    Client under NDA

    Nurturing stopped being one campaign to the whole list

    The customer profile was described from gut feel, nurturing was launched by hand and identically for everyone, and hypotheses took weeks to test. Sales received leads with no readable warmth and spent time on people who were not ready.

    Stage-to-stage funnel conversion
    +30–40%
    Conversion to sale
    +20–50%
    Read the case

Which solutions apply

Frequently asked questions

Our whole load lands in two months of the year. Is it worth it?

That is exactly the case where it pays off: hiring for the peak is expensive, and not hiring means losing orders. Measuring it takes care, though — comparing a peak month with a quiet one shows nothing. We fix the baseline against a comparable period a year earlier and say so upfront.

We sell mostly through marketplaces. Does this work?

Partly. Support and returns inside a marketplace follow its rules and its API — some of it is available, some is not, and that gets checked before work starts. Everything on your side — stock, reservations, repeat sales into your own channel — automates the same way as for a store with its own storefront.

Let's work through one workflow in ecommerce and retail

Thirty to forty-five minutes on your specific case. If it isn't a fit, we'll say so on the call.