Question 1

What is AI demand forecasting for ecommerce?

Accepted Answer

It predicts future sales at SKU, variant and location level, blending seasonality, trend, promotions and live storefront demand. In Sellarix the forecast reads the same spine as search and merchandising, so it anticipates spikes a tool working only off history would miss.

Question 2

How do I reduce stockouts and overstock with automated replenishment?

Accepted Answer

The forecast feeds straight into replenishment: Sellarix calculates reorder points and quantities against lead times, MOQs and safety stock, then drafts purchase orders to review or auto-release. The continuous loop means you hold less buffer and stock out less often.

Question 3

How does ecommerce fraud detection avoid false declines?

Accepted Answer

Sellarix scores every order on hundreds of signals and the customer's history rather than firing brittle block rules. Weighing evidence on a unified customer graph catches sophisticated fraud and account takeover while passing genuine buyers that rule-based screens wrongly reject.

Question 4

How does returns prediction reduce returns and wardrobing abuse?

Accepted Answer

It flags the orders, products and customers most likely to result in a return before the parcel ships, scoring size and fit risk, serial return behaviour and problem SKUs. Catching them early lets you intervene with better fit guidance, a clearer PDP or a hold.

Question 5

How does Sellarix stop account takeover and bot attacks?

Accepted Answer

Sellarix watches for account-takeover signatures such as impossible-travel logins and sudden changes to saved details, and defends logins, checkout and stock drops against bots. It steps up verification or blocks at the point of attack rather than after the order ships.

Question 6

How does estimated delivery date prediction work at checkout?

Accepted Answer

Sellarix predicts a realistic delivery date per order from origin node, carrier history, destination and network conditions, and shows it before checkout. Because it shares the order graph with the Serve suite, a WISMO query gets the same date the shopper already saw.

Question 7

What is the difference between chargeback management and chargeback liability?

Accepted Answer

Sellarix focuses on chargeback prediction, evidence assembly and recovery, not underwriting guarantees. It predicts likely disputes, helps you decide whether to contest, and builds the case to win, so you recover winnable revenue without paying a liability premium.

Question 8

How does multi-warehouse allocation and supplier-risk monitoring work?

Accepted Answer

Sellarix allocates stock across warehouses from the same product, customer and order graph, while supplier-risk monitoring tracks the lead-time and reliability signals that threaten replenishment. On one spine, a supplier delay reshapes both the PO plan and the fulfilment decision together.

Question 9

Is the AI automation explainable and EU AI Act compliant?

Accepted Answer

Yes. Every model logs its decisions and exposes the reason behind a forecast, block or flag, and recommend-versus-auto-apply controls mean automation never runs unsupervised. AI interactions are disclosed under EU AI Act Article 50, whose transparency duties apply from 2 August 2026.

Question 10

How is Operate and Protect different from separate forecasting, logistics and fraud tools?

Accepted Answer

It runs every capability on the same product, customer and order graph as the rest of Sellarix, under one contract and one bill, instead of stitching point tools that each keep a partial copy. That unified spine is why each decision sees the full picture.

	Sellarix	Traditional / point tools
Data foundation	Forecasting, returns and fraud read the same product, customer and order graph as search and merchandising.	Point tools keep their own partial copy via separate syncs, so they may not share context.
Forecast to purchase order	Demand forecast flows straight into reorder points, allocation and drafted purchase orders in one auditable loop.	A forecasting app outputs a number a planner re-keys into a separate purchasing system, where the loop breaks.
Fraud decisioning	Risk is scored on hundreds of signals plus the shopper's own history, cutting false declines while catching abuse rings.	A standalone screen judges each transaction in isolation, so rules over-block good customers or miss linked abuse.
Returns	Returns are predicted before dispatch and acted on upstream through fit guidance, PDP fixes or holds.	A returns portal processes the return after it has already happened, with less it can do.
Governance and disclosure	Every decision is logged and explainable, with recommend-versus-auto-apply controls and Article 50 disclosure built in.	Compliance, audit logging and AI disclosure are left to the merchant to stitch across several vendors.
Commercial model	One contract, one data model and one bill across all operations and protection capabilities.	Separate subscriptions, integrations and renewals, with the integration cost and tool sprawl on you.

AI forecasting, returns and fraud, on any platform

Overview

How it works

One shared data spine

The right model per problem

Decisions surfaced and acted on

Logged, explained and disclosed

Funktionen

Use cases

Stop the over-order, mark-down cycle

Cut returns at the source

Block fraud without false-declining good customers

How Sellarix compares

Operate & Protect FAQ

Part of one platform

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