Personalization, support, and merchandising AI that moves revenue, not vanity metrics.
We build production AI for eCommerce teams across the EU and Greece — personalization engines, AI customer support, inventory forecasting, and content automation that moves margin. Every system we ship integrates with the platform you already run, ships behind feature flags, and is measured against the conversion or margin number it's supposed to move.
eCommerce in 2026 has more AI tools than any team can ship. Every platform vendor promises personalization, every support tool promises deflection, every analytics tool promises uplift. The result is a stack of overlapping pilots and no clear answer to what's actually moving the number.
The teams we work with have usually been through one or two AI initiatives that produced demos but not revenue. They're not looking for another vendor — they're looking for the engineering that ties customer data, product catalog, support transcripts, and order history into one place where decisions are made and measured.
What works is the opposite of the vendor pitch: small AI features wired into the existing customer journey, behind feature flags, with the conversion or margin number tied to the rollout. That's what we build.
Product, search, and email recommendations tied to your real conversion data — not a vendor's black box.
Deflection on the easy questions, intelligent routing on the hard ones, full handoff context for your agents.
Demand and reorder predictions tied to margin, lead time, and seasonality — not just last year's sales.
PDPs, alt text, lifecycle email, and ad copy generated and quality-checked at SKU scale.
Score returns risk at checkout and post-purchase so you can act before the box ships back.
A/B test infrastructure with proper causal measurement — not just lift-only dashboards.
Representative engagement
Overstock was running ~12% of GMV with seasonal SKUs particularly bad. The Shopify-native forecast was unreliable beyond a 2-week horizon. Reorder cycles were reactive — buying decisions made on rolling 4-week sales without weather, traffic, or marketing-spend signal.
We built a demand-forecasting service ingesting Shopify orders, GA4 traffic, weather data, and marketing-spend events. Daily reorder recommendations land in the team's existing NetSuite workflow — no new dashboard to log into. The model retrains weekly; per-SKU confidence intervals tell merchandisers when to override the forecast.
Overstock cost reduced ~28% in the first quarter post-launch. Reorder cycle time halved. Forecasting horizon extended from 2 weeks to 8 weeks with usable confidence. Merchandising team reports spending 30% less time on weekly demand-planning calls.
On attribution · Client and exact metrics anonymized at the client's request. Engagement details (timeline, platform stack, data sources, model behavior) are accurate.
Two to three weeks. We map the buyer question, the data, the regulatory shape, and what shipping looks like. Output is a written brief with a fixed-scope first phase.
A working slice end to end — the model, the integration, the UI, and the observability. Built to be evaluated, not to demo.
Production engineering: data contracts, decision logs, deployment, monitoring, runbooks. The thing your team can own after we leave.
Cutover, training, and a handover that includes the parts most teams skip — change-management notes, audit-ready docs, and a 30-day support window.
Mental Bound