I
Catalogue & product data
The system of record for products: attributes, variants, media and the rules that keep them consistent across channels. Everything downstream inherits the quality of this layer.
PIMAttributesChannels
Commerce platforms · Software · AI · Cyprus
Services
Four layers beneath the storefront, and the audit that decides whether any of them need work.
01 — What we build
Four layers of the commerce stack
We work below the storefront, where catalogues, stock and integrations decide whether the shop above them can function at scale.
II
Integrations
Shop, warehouse, ERP and payment provider exchanging data reliably — including the difficult half, which is what happens when one of them is unavailable.
ERPWarehouseResilience
III
Stock, pricing & availability
The logic that decides what is sellable, at what price, deliverable when. It is the part customers notice instantly when it is wrong.
Stock logicPricing rulesAvailability
IV
Applied AI
Classification, attribute extraction, description drafting and duplicate detection across large catalogues. Used where it removes measurable manual hours — and not used where a rule is cheaper.
ClassificationEnrichmentDeduplication
05 — The boundary
What we do not do
Four things we decline, stated up front.
No AI without a baseline
If we cannot measure the manual effort before, we cannot honestly claim to have reduced it after.
No storefront-only projects
Design refreshes on unfixed data postpone the problem at your expense.
No proprietary lock-in
The product model, code and documentation are yours. No hosted black box you cannot leave.
No unaudited estimates
We do not price a platform project before measuring the data. Numbers produced that way are fiction.
02 — Approach
How we work
Measure first, model second, build third. Reordering those is the usual reason platform projects overrun.
Phase 01
Audit
Real data, measured. Cheap, fast and frequently decisive.
Phase 02
Model
The product structure, agreed in writing before code exists.
Phase 03
Build
In slices that go live independently, so value arrives before the project ends.
Phase 04
Operate
Monitoring, reconciliation and an honest count of the manual work removed.
In detail
How a platform engagement runs
Four stages. The first one frequently ends the project, which is a good outcome delivered early.
Stage 01 · Audit
What the data actually looks like
We take an export of the real catalogue and measure it: missing attributes, inconsistent units, duplicates. It is usually worse than anyone expected and always better to know now.
ExportMeasureReport
Stage 02 · Model
A structure that holds
A product model that survives new categories and new channels without a rebuild. Get this wrong and every later problem traces back to it.
ModelAttributesExtensibility
Stage 03 · Integrate
Make the systems agree
Interfaces with real error handling and a reconciliation path, so a failed sync surfaces as an alert rather than as a customer complaint.
InterfacesReconciliationAlerting
Stage 04 · Operate
Measured, then improved
Throughput, error rates and the manual hours actually removed. If AI did not reduce them, we say so rather than presenting activity as progress.
ThroughputError ratesHours saved
Catalogue audit · illustrativeActive
Typical finding
Data
Not design
First stage
Audit
Before any build
Missing attributesMeasured
Unit inconsistenciesMeasured
Duplicate productsMeasured
Channel gapsMeasured
Illustrative audit dimensions — not a customer report
04 — FAQ
Clearly answered
Do you build storefronts?
Rarely, and not as the main event. We work on the catalogue, integration and logic layers beneath. Plenty of shops have an excellent storefront sitting on data that cannot support it.
Where does AI genuinely help?
Classifying products into categories, extracting attributes from supplier documents, drafting descriptions for review and finding duplicates across large catalogues. All of those have a measurable before and after.
Where do you refuse to use AI?
Anywhere a deterministic rule is cheaper and more predictable, and anywhere a wrong answer would ship to a customer without a human seeing it first.
How long does an audit take?
Usually one to two weeks. It is deliberately cheap, because its job is to tell you honestly whether a bigger project is warranted.
Who owns what you build?
The client. Source code and documentation transfer with the engagement, with no licence held back.
Which markets do you serve?
Registered in Cyprus, delivering across the EU, in English and German.
Let’s talk
A catalogue that has outgrown its structure?
Send an export and the number of products. An audit answers more in two weeks than a workshop does in two months.