By industry
AI & Automation for Retail & eCommerce
Handle peak without hiring for peak
We design around
- DPDP Act 2023
- PCI DSS
- GDPR (EU-facing sales)
- Consumer protection e-commerce rules
- 65%
- Support tickets auto-resolved at peak
- 99.98%
- Availability through sale events
- 4x
- Catalogue listing throughput
With rehearsed load testing beforehand
What we hear
The problems that bring people to us
If several of these describe your week, there is almost certainly something worth automating.
- Support volume spikes on sale days and overwhelms the team
- Catalogue data inconsistent across marketplaces and channels
- Returns and refunds processed manually, slowly
- Infrastructure that struggles at exactly the wrong moment
- Stock and pricing decisions made on stale reports
What we build
Where automation pays off in retail & ecommerce
Support automation for the top ticket types
Order status, returns initiation, address changes and refund eligibility resolved automatically, including the action in your commerce and payment systems. Peak-day volume stops being a staffing problem.
Catalogue enrichment
Generate and normalise titles, attributes and descriptions per channel from source product data, with brand rules enforced. Removes the manual work behind every marketplace listing.
Returns and refunds
Policy eligibility checked automatically, refund issued within agreed limits, exceptions routed to a person with the case assembled. Faster resolution, consistent policy application.
Peak-ready platform
Load-tested infrastructure with autoscaling proven before the sale, not during it. We rehearse peak in a copy of production and fix what breaks while it does not matter.
Demand and stock signals
A modelled data layer combining sales, returns, marketing and stock so decisions come from current numbers rather than last week's export.
Retail's problem is variance
Average load is easy. The difficulty is the Tuesday in October when a campaign lands and volume goes up eightfold — for support, for checkout, for the warehouse.
Staffing for peak is expensive and staffing for average means peak hurts. That asymmetry is exactly what automation and elastic infrastructure address, and it is why retail sees returns faster than most sectors.
Support first, then catalogue
Support automation gives the fastest visible return because the top three ticket types are usually a large share of volume and all have verifiable answers from your commerce data. Order status, returns and address changes are close to ideal candidates.
Catalogue enrichment comes second and is the quieter win: consistent, complete attributes across channels improve conversion and search placement, and the manual version never quite gets finished.
Where the platform work matters
An outage during a campaign costs more than the campaign. NoOps covers the 24x7 side of this, which matters in retail more than most sectors — traffic does not respect business hours, and neither do failures.
What a peak rehearsal actually involves
"We load tested it" covers a wide range of rigour. The version that prevents outages looks like this.
A realistic traffic shape, not a flat curve. Real sale traffic arrives as a spike at the announced hour, with a browse-heavy first ten minutes and a checkout-heavy second twenty. Flat load at the average tells you almost nothing about the shape that breaks you.
Against a copy of production, with production-scale data. A test environment with a thousand products does not exercise the query that gets slow at eighty thousand.
Measuring scale-out latency, not just capacity. The common failure is autoscaling that works and takes eleven minutes. Capacity you get after the spike has passed is not capacity. This number is the one most teams have never measured.
Including the dependencies you do not own. Payment gateway, courier rate APIs, your ERP. Your checkout is only as available as the slowest thing in its path, and third parties have their own bad days on the same date as yours.
With a rehearsed degradation plan. What you switch off to protect checkout — recommendations, live inventory counts, non-essential webhooks — decided and practised in advance, not improvised at 11pm.
Where the work overlaps other sectors
Support deflection is support automation with a commerce integration behind it. Catalogue enrichment and returns handling are document processing and back-office automation respectively. The peak-readiness work is platform engineering rather than AI, which is worth being clear about — it is often the highest-value thing on the list and involves no models at all.
If you fulfil your own orders, the exception handling described under logistics and supply chain applies to your warehouse and courier integrations on those same peak days.
FAQ
Questions we get asked
Can you get us through a big sale day?
Yes, provided we start more than a fortnight beforehand. The work is load testing against a realistic traffic model, fixing what breaks, verifying autoscaling actually scales in time, and having a rehearsed degradation plan. Turning autoscaling on without rehearsing it is not preparation.
Will AI-written product copy hurt our SEO?
Generic copy at volume will. Generated-then-edited copy built from real product attributes with your brand rules enforced does not, and it beats the placeholder text most catalogues actually ship with. The failure mode is publishing unedited output at scale.
Our data sits in Shopify, an ERP and three marketplaces. Where do we start?
With the modelled layer, usually. Almost every retail automation depends on trustworthy product, order and stock data, so getting that right makes the following three projects substantially cheaper.
How fast can support automation go live?
Four weeks for one high-volume ticket category, integrated with your commerce platform and monitored. Additional categories come faster because the integration and evaluation scaffolding is reused.
What does a first engagement cost and how long does it take?
Two weeks for an audit that models savings against your actual ticket mix and traffic shape, then four weeks to put one ticket category into production. If a sale event is the driver, work backwards from the date and start at least six weeks out — load testing that finds a problem you have no time to fix is not useful.
What happens when the automation gets a refund decision wrong?
It costs you the refund, which is why the authority limits matter more than the accuracy number. Refunds above an agreed value, anything irreversible and anything touching a flagged account route to a person regardless of confidence. We set that ceiling with your finance team before launch and review it against actual outcomes after a month. The correct ceiling is the one where the occasional wrong automated refund costs less than the human review of every refund would.
Our support team will assume this is about cutting headcount. Is it?
Sometimes it is, and if so you should say so rather than let people work it out. More often what actually happens is that volume grows without the team growing, and the team stops doing the forty-times-a-day copy-paste work. That is a genuinely better job, but only if you say it plainly and involve the agents in defining escalation rules — they know which tickets go wrong, and that knowledge is the specification.
Do marketplace rules restrict what we can automate?
On listings, yes, and they differ per marketplace. Generated content is generally acceptable where it is accurate and attribute-derived, but some marketplaces restrict specific claim types and all of them enforce accuracy. We build the brand and compliance rules into the generation step rather than relying on review to catch violations, because at catalogue volume review does not catch them.
Talk to someone who has worked in retail & ecommerce
Bring a process that annoys you. In 30 minutes we will tell you whether AI helps, what it would cost, and where it would fail — even if the answer is don't bother.
Or email [email protected] · we reply within 1 business day