Mental BoundMental Bound
EL

We build AI systems for eCommerce teams in the EU.

Personalization, support, and merchandising AI that moves revenue, not vanity metrics.

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Behind feature flags, alwaysMeasured against revenue per sessionIntegrates with the platform you already run
The 30-second answer

What does Mental Bound build for eCommerce?

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.

  • Personalization for product, search, and email
  • AI customer support that deflects without losing the customer
  • Inventory and demand forecasting tied to real margin
  • Content automation: PDPs, alt text, lifecycle email at SKU scale
  • Returns prediction and prevention scoring
  • Conversion optimization with proper causal measurement

What eCommerce teams are solving in 2026

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.

What we build for eCommerce

Personalization engines

Product, search, and email recommendations tied to your real conversion data — not a vendor's black box.

AI customer support

Deflection on the easy questions, intelligent routing on the hard ones, full handoff context for your agents.

Inventory forecasting

Demand and reorder predictions tied to margin, lead time, and seasonality — not just last year's sales.

Content automation

PDPs, alt text, lifecycle email, and ad copy generated and quality-checked at SKU scale.

Returns prediction

Score returns risk at checkout and post-purchase so you can act before the box ships back.

Conversion optimization

A/B test infrastructure with proper causal measurement — not just lift-only dashboards.

Representative engagement

EU D2C Shopify Plus retailer — inventory forecasting MVP in 8 weeks

The problem

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.

How we built it

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.

The outcome

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.

How we work

  1. 01

    Scoping

    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.

  2. 02

    Prototype

    A working slice end to end — the model, the integration, the UI, and the observability. Built to be evaluated, not to demo.

  3. 03

    Build

    Production engineering: data contracts, decision logs, deployment, monitoring, runbooks. The thing your team can own after we leave.

  4. 04

    Ship

    Cutover, training, and a handover that includes the parts most teams skip — change-management notes, audit-ready docs, and a 30-day support window.

Frequently asked

Which platforms do you integrate with?
Shopify (Plus and standard), Magento, BigCommerce, Salesforce Commerce Cloud, and headless setups built on Next.js, Remix, or custom React. We've also worked with bespoke Greek platforms. The integration pattern is the same: clean events out, clean recommendations in, no platform lock-in on our side.
Do you replace our personalization vendor or work alongside it?
Either. For most teams the right answer is: keep the vendor for the boring 80% (related products, abandoned cart) and build custom for the 20% where their data model can't represent your business — bundles, B2B pricing, regional inventory, gifting flows.
How do you handle GDPR and customer data?
We default to EU-region storage and processing, anonymize where the use case allows, and never train shared models on a single customer's data without explicit contractual permission. Your customer data stays yours.
How do you measure whether AI is actually helping?
Every system we ship is launched behind a feature flag with a holdout group and a single metric — usually conversion rate, average order value, or contribution margin. We don't claim a number we can't measure causally, and we'll tell you when a model isn't worth shipping.
Can you ship AI customer support without firing our team?
Yes. The pattern that works is deflection on FAQ-style tickets and intelligent routing on everything else, so your agents stop drowning in resets and refunds and spend their time on the conversations that actually need them. Headcount changes are your call, not ours.
What about content automation and SEO penalties?
We treat AI-generated PDP and editorial content as drafts that ship through your existing review and template flow, not as a hose pointed at the catalog. Schema markup, originality checks, and human approval on category pages stay in place. Google's policy is about quality and spam, not about whether a model touched the text.
Do you build agents that act on customer accounts or orders?
We build agents that recommend, route, and pre-fill — and we draw a line at autonomous refunds, exchanges, or order modifications without an explicit human approval step. Customer trust is your moat, and we don't ship things that erode it for a demo.
How long does a typical engagement take?
Discovery and scoping is 2–3 weeks. A first feature shipped behind a flag with measurement is usually 6–10 weeks. From there it's iteration. We don't sell year-long contracts up front — you should be able to leave after each phase.