Support volume scales with orders
Every 10% growth in orders brings roughly 10% more “where is my order”. Headcount is the only lever most teams have.
Stratgik — technology strategy and business systems engineering.
Delivery across the USA, UK, UAE and India.
Ecommerce & Retail
Technology, Software and AI Automation for Ecommerce and Retail
Retail support volume is predictable, seasonal and dominated by a handful of intents. The constraint is not whether AI can answer — it is whether it can see the order.
Platform decisions, integration architecture between store, ERP, warehouse and payments, and whether a replatform is genuinely needed or the current stack can be made to behave.
Explore 02BuildCustom commerce where the operation is the hard part: collection windows, stock across locations, account pricing, fulfilment rules and the exceptions a standard cart never models.
Explore 03AutomateOrder status, returns, refunds, failed payments and post-purchase communication answered from live data instead of a help centre.
Explore 04RunManaged commerce: uptime through campaigns and peak season, integration monitoring, patching, and capacity planned before Black Friday rather than during it.
ExploreThe short answer
In ecommerce, AI automation connects to the commerce platform, ERP, payment gateway and carrier so it can answer customer questions from live order data instead of a help centre, and watch order state so that stalled or at-risk orders are dealt with before the customer complains. The highest-return starting point is almost always the order-status intent, because it is the largest single share of contact volume and it is fully answerable from data the business already holds.
What we usually find
Every 10% growth in orders brings roughly 10% more “where is my order”. Headcount is the only lever most teams have.
Temporary staff are hired and trained just as volume and error rates are highest.
Platform, ERP, payment gateway and carrier — agents alt-tab between them for a question with one right answer.
A stuck order is discovered when the customer emails, not when it stalled.
Eligibility gets decided inconsistently, and refunds get chased in three separate channels.
Nobody can say which product, courier or page is generating the tickets.
Workflows
Pick a workflow to see each step. Every one reads from your real systems, and hands over to a person where the rules say it should.
Order status enquiry
Identify order
Fetch live state across platform, ERP and carrier
Answer with the real date
Schedule follow-up if the order is at risk.
Every step is logged, and anything outside policy goes to a person.
Return request
Check eligibility against policy and order state
Create the return
Issue the label
Update the customer and the system.
Every step is logged, and anything outside policy goes to a person.
Refund status
Read the payment and settlement position
Explain accurately
Execute or route for approval based on your threshold.
Every step is logged, and anything outside policy goes to a person.
Stalled order sweep
Detect paid-but-not-progressing orders
Diagnose cause
Chase the missing input or escalate to fulfilment.
Every step is logged, and anything outside policy goes to a person.
Failed payment recovery
Detect failure
Contact the customer by message, then by call
Provide a secure completion path.
Every step is logged, and anything outside policy goes to a person.
Stock and alternatives
Answer availability from live inventory
Offer genuine alternatives
Capture demand for out-of-stock items.
Every step is logged, and anything outside policy goes to a person.
Reference architecture
Automation is only as good as what it can see. This is the shape we build towards in ecommerce & retail: channels on top, your systems of record underneath, and people where judgement is needed.
Where the line sits
Handled automatically
Always a person
Advice before you spend
Most retailers who want to replatform actually need their store, ERP and warehouse to agree on stock and order state. Fix that first; replatforming a broken data flow just moves the problem.
Only when the storefront experience is a real competitive advantage and you have the team to run it. Otherwise a well-configured platform theme is faster and cheaper to own.
Order status. It is the largest single share of contacts, fully answerable from data you hold, and easy to measure.
What to measure
Four numbers that tell you whether technology is helping. We take a baseline before any work starts.
Shows whether support load is growing faster than the business.
Where most "where is my order" contacts are really created.
A second contact about the same order costs twice and annoys the customer twice.
Tells you whether the platform holds up when it matters most.
Where it fits
Absorb the predictable intents so the team handles genuine exceptions instead of drowning.
Where a missed collection window cancels the order and loses the revenue.
One automation layer across stores with different policies, languages and currencies.
Consistent answers across your own site and marketplace channels.
Related work
A full pre-order and click-and-collect e-commerce platform for one of India's largest airport duty-free operations - thousands of SKUs, high...
Read the case study Case studyAn AI-powered support chatbot that answers routine customer questions instantly and hands complex cases to humans with full context - cuttin...
Read the case study Case studyA centrally-managed digital signage network across retail stores: remote content scheduling, promotions synchronized chain-wide, and screens...
Read the case studyYes. Large multi-store Magento 2 with custom APIs and ERP integration is directly in our experience — see the airport travel-retail case study. Magento’s API surface is usually sufficient without invasive changes to the store.
We would rather measure than guess. Classifying one month of real tickets shows the intent mix and the ceiling before anything is built — and it sometimes shows the answer is process change rather than AI.
It changes what they do. The repetitive intents stop reaching them; complaints, exceptions, high-value customers and judgement calls still do. Most teams we work with redeploy rather than reduce, because the exception work was previously being done badly under time pressure.
Peak is the strongest case, because the automated share does not degrade under volume. It is also the worst time to deploy something new — build and prove it in a quiet period.
One process, one system or one decision. An engineer who understands ecommerce & retail replies within one working day with how we would approach it, and whether it needs building at all.
The Stratgik model
Four stages, in order. Most businesses need them one at a time.