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Industry solution — E-Commerce

Scale to 50K+ Calls During Peak Season

Handle order tracking, returns, and delivery inquiries without hiring a single seasonal agent — the same agent answers from phone, web, and your mobile app.

Intelina Voice· Phone

Order tracking, returns and delivery changes on the phone through peak season without seasonal hiring.

Intelina Desk· Web chat

Storefront widget answering 'where is my order' from your OMS in real time, opening a ticket only for exceptions.

50K+ calls
Peak Capacity
0 agents
Zero Hiring
90%+
Target Resolution

Figures on this page are planning targets and typical volume distributions for the sector, not measured results from a named customer. We size them against your own numbers during the demo.

The Seasonal Scaling Challenge

  • 10x call volume spikes during holidays
  • Hiring and training temporary agents
  • Inconsistent service quality
  • Order tracking system fragmentation
Customer
Menu or queue
Dead end
Contact mix

Where the volume actually goes.

Before deciding what to automate, it helps to see what people are actually contacting you about — and which module is the right home for each slice.

38%Where is my orderIntelina Desk

TodayAgent copies a tracking number

21%Returns, exchanges and refundsBoth modules

TodayMulti-step manual process

14%Delivery changes and reschedulesIntelina Voice

TodayPhone queue during peak

12%Product, stock and promo questionsIntelina Desk

TodayPre-sale chat, often unanswered

9%Payment and checkout failuresBoth modules

TodayCart abandoned before anyone replies

6%Complaints needing a humanBoth modules

TodayBuried in a shared inbox

The approach

Elastic capacity that scales with demand

01

Scales instantly

Volume spikes are absorbed automatically — no recruiting, onboarding, or overtime during peak season.

02

Knows every order

Connected to your OMS and carriers, the agent gives live tracking, ETAs, and delivery updates.

03

Processes returns end to end

Initiates returns, issues labels, and triggers refunds in the conversation — consistent every time.

04

Consistent on every channel

The same agent and context answer whether a shopper calls, chats on the site, or taps in the app.

What the agent handles

  • Real-time order tracking & ETAs
  • Returns, exchanges & refund initiation
  • Promo, stock & product questions
  • Delivery rescheduling & address changes
Playbooks

What each module actually does here.

Not capabilities in the abstract — the specific flows, in order, with the primitives each one relies on.

Intelina Voice

Phone · AI voice agents + customer CRM

Order status without a reference number

TriggerCaller asks where their order is

  1. 1Caller ID resolves to the customer record before they finish the sentence
  2. 2The agent calls your order system for live status and carrier ETA
  3. 3It reads back the actual delivery window rather than a generic range
read_customerhttp_webhook → OMS

Returns started and finished on the call

TriggerCaller wants to return or exchange an item

  1. 1The agent confirms the item from the order history
  2. 2It opens the return in your system and triggers the label
  3. 3The reference is spoken back and texted after the call
http_webhook → returnspost-call SMS

Peak volume without peak headcount

TriggerTen times the normal call volume in a holiday week

  1. 1The audio bridge and the API scale independently of each other
  2. 2Every call gets the same persona, knowledge and tools as the first
  3. 3Nothing depends on training a seasonal cohort
horizontal scalingshared agent configuration

Intelina Desk

Web chat · AI chat + ticketing tool

Tracking answered inside the storefront

TriggerSigned-in shopper opens the widget

  1. 1The widget knows who they are from your own session endpoint
  2. 2A registered endpoint returns their live order list
  3. 3The bot answers before the shopper types an order number
signed-in user detectiondata endpoints

Refund policy that quotes your policy

TriggerQuestion about returns windows or eligibility

  1. 1The knowledge base is searched over the policy documents you uploaded
  2. 2The answer stays inside what those documents actually say
  3. 3Edge cases the documents do not cover surface as knowledge gaps
knowledge baseknowledge gaps

Only the exceptions reach a human

TriggerDamage, dispute, or a request for a person

  1. 1The chat becomes a ticket in the right queue with the full thread
  2. 2SLA tracking starts immediately so peak-season backlogs stay visible
  3. 3The agent sees the order data the bot already fetched
escalationqueuesSLA tracking
Integrations

What it plugs into.

Every connection is something you register in the dashboard — no bespoke integration work on our side.

Order management system

Registered data endpoint plus an HTTP webhook tool for writes

Carrier and tracking APIs

Data endpoint with a field whitelist on the response

Returns and refunds platform

HTTP webhook tool with idempotency on submit

Storefront

One widget script tag, themed to your brand

Support inbox and queues

Ticketing workspace with per-team routing

Guardrails

What it refuses to do.

The limits that matter in this sector, enforced by configuration rather than by prompt wording.

No invented delivery dates

Dates come from a live tool call. If the call fails, the agent says so rather than estimating.

Refunds are idempotent

A retried refund replays the original result instead of issuing a second one.

Peak-season isolation

Every project's queries, caches and vector searches are scoped, so one tenant's surge cannot reach another's data.

Approvals on goodwill credits

Discretionary refunds and credits can be held for a supervisor rather than granted by the AI.

Rollout

How this actually goes live.

Read-only first, writes behind approvals, then widened once the record justifies it. No big-bang cutover.

  1. 01Day 1

    Drop in the widget

    One script tag on the storefront, themed to your colours and logo. No rebuild, no SDK.

  2. 02Days 2–5

    Point it at orders

    Register the order and tracking endpoints, whitelist the fields, and test each one against a sample user.

  3. 03Week 2

    Load the policies

    Upload returns, shipping and promo documents so answers are quoted rather than improvised.

  4. 04Week 3

    Open the phone line

    Map a number, import customer records, and turn on the order-status and returns tools for voice.

  5. 05Before peak

    Rehearse the surge

    Load-test ahead of the season, set queue routing and SLA thresholds, and decide which actions stay behind approvals.

Measurement

What to hold it accountable to.

Each target maps to something the product already reports, so nobody has to build a spreadsheet to find out whether it worked.

Metrics, current typical state, target, and where each is reported
MetricTypically todayTargetReported by
Self-service resolutionMost contacts reach an agent90%+ of order enquiriesTicket volume by category and escalation rate
Seasonal hires neededA cohort every peakNoneContacts handled per human agent
First response time at peakDegrades with volumeFlat regardless of volumeProject analytics, first-response metric
Repeat contacts per orderMultiple 'where is it' checksOne, or noneTickets grouped by order reference
Projected outcomes

What success looks like

50K+
Peak calls handled

Holiday surges absorbed with zero new hires.

90%+
Self-service resolution

Most inquiries resolved without an agent.

0
Seasonal agents needed

Capacity scales on demand, not on headcount.

Black-Friday-scale volume, answered instantly and consistently — without a hiring scramble.

E-Commerce questions

What buyers in this sector ask first.

Chat is same-day: a script tag, a knowledge upload, and one registered order endpoint. Voice takes longer because a number has to be mapped and customer records imported — plan a short onboarding rather than a project.

See this running on your own e-commerce data.

Bring one line or one queue and your real numbers. We will size the plan against them on the call rather than quoting the targets on this page back at you.