AI DecisionLayer

For retail promotions & personalization

AI-based decisioning platform for retail that optimizes marketing, loyalty, and promotional decisions at the individual customer level.

1:1 Decisioning, Not Segmentation

RetailEngine analyzes real customer behavior and recommends the best action for each individual - aligned with revenue, margin, or growth goals.

Central Decision Layer

A single AI decision layer connects POS and loyalty systems with AI models - delivering real-time, customer-level decisions across channels.

Transparent, GDPR-Compliant Control

AI forecasts the expected impact, but control stays with the retailer: decisions are adjustable, explainable, and protected by built-in data privacy.

Full-Assortment Hyperpersonalization

AI DecisionLayer activates the entire assortment - tens or hundreds of thousands of SKUs across hundreds of categories. Each customer receives a unique, dynamically weighted product set aligned with real-time business priorities. This is not rule-based targeting; it is continuous optimization at scale. Retail field research shows that broader personalization significantly improves conversion, revenue per visitor, and lifetime value. Expanding the personalized assortment increases match probability - and commercial uplift grows accordingly.

Proven Impact Through Automated A/B Testing

AI DecisionLayer includes a built-in automated A/B testing platform to verify and prove the effectiveness of every optimization. Retailers can design, manage, and analyze experiments at customer or store level using balanced audience splitting and clearly defined KPIs. The system measures true incremental uplift - revenue, margin, and behavioral change - while controlling for cannibalization and external effects. Sample sizes and minimum detectable effects are calculated in advance to ensure statistical validity. Every result is measurable, transparent, and backed by proven evidence - not assumptions.

Explainable & Fully Traceable Decisions

AI DecisionLayer does not operate as a black box. Every decision and recommendation is explainable. You can clearly understand why a specific offer was selected for a particular customer - including the business objective, constraints, and expected impact. You can also view the exact promotion mechanics used, the composition of the recommended products and categories, and the reasoning behind their selection.

Low Entry Barriers and Easy Adoption with retail platforms

Cash Register / POS Software

Supports online and offline scenarios (store, self-checkout, mobile POS).

Loyalty Platforms

Reduces blanket discounting while increasing retention and customer lifetime value.

CDP (Customer Data Platform)

Leverages unified cross-channel customer profiles for consistent hyper-personalization across store, e-commerce, and mobile.

ERP Platforms

Aligns marketing and promotion decisions with operational realities: margin targets, inventory levels, supply constraints, and supplier agreements.

About us

We are a team of experienced data scientists and AI engineers specializing in retail recommendation systems and uplift modeling.

For many years, we have built AI solutions that help retailers understand customer behavior at a deeper level and make better, measurable decisions. Our work combines advanced machine learning with real-world retail complexity.

We are united by shared values.

We believe AI should be responsible, explainable, and economically meaningful. It should strengthen human decision-making - not replace it.

Our vision is clear:

to create intelligent systems that help retailers grow sustainably, understand their customers more precisely, and make everyday commerce smarter and more human.

Frequently asked questions

Traditional marketing automation relies on predefined segments and rules. AI Decisioning works differently: it selects the optimal action here and now for a specific customer, taking context and expected incremental impact into account. It is a shift from campaigns for segments to a controlled, scalable decision-making production process.

In broad-assortment retail, there are too many moving parts: thousands of SKUs, complex promotional mechanics, thin margins, cannibalization effects, supplier constraints, and regulatory requirements. The problem is not a lack of data, retailers have plenty of it. The problem is that manual decision-making no longer scales and leads to systematic value loss: discounts are given where they are not needed, and not given where they would generate real incremental growth.

The final decision remains with the retailer. RetailEngine acts as a central Decision Layer that controls rules, constraints, and accountability. External ML/AI components provide forecasts (uplift, rankings, impact estimates), but they do not make autonomous final decisions.

The architecture is designed so that the Decision Layer acts as a PII firewall: external models receive only what they need (pseudonymized IDs, purchase and interaction events, contextual signals), without names, contact details, or payment data. Decisions remain controlled, explainable, and auditable, which is critical for GDPR/DSGVO compliance and business governance.

Yes, this is a core function of the Decision Layer. The AI Decision Layer accounts for margin constraints, budgets, frequency caps, product availability, and retailer business rules, ensuring personalization does not turn into uncontrolled discounting.

Prove uplift on your data

Run a controlled pilot with transparent methodology.