Use Case: Shopentum

High data volume. Low decision clarity.ShopentumDecision Intelligence System

E-commerce teams process fragmented data across systems without clear inference. Shopentum introduces a deterministic decision layer.

Shopentum connects data and turns it into clear decisions.

By connecting marketing and technical signals, we identify the most critical growth barriers in e-commerce operations.

Data Inputs

GA4 Data

Google Ads

Meta Ads

Search Console

SHOPENTUM

Decision Intelligence Engine

Stores context, evaluates audit data, GA4, and Google/Meta Ads signals in real time, then drives execution based on market reality and business goals.

Execution Modules

AI Marketing

Task Manager

Growth Engine

Ads Engine

SHOPENTUM PREVIEW

Ukážky dashboardu

Problem / Reality

The store operated, but decision logic was weak.

Decisions were manual and feeling-based

Marketing and data were not connected (data silos)

Optimization was slow, reactive, and expensive

The system did not scale without constant team growth

Architecture / Approach

Next-generation architecture: Decision Intelligence System

I designed a custom decision layer architecture built on AI WORKS principles.

Runtime vs Snapshot

Strict separation between real-time processing and immutable historical states for fully auditable decisions.

Pipeline-based Processing

No ad-hoc scripts. Chained data pipeline from raw collection to strategic insight.

Delta Engine

Continuously compares current state against benchmarks and explains cause and business impact over time.

Findings Engine

Multi-layer analytics that detects critical barriers and growth opportunities in data noise.

shopentum_os_core.log

[SYSTEM] Initializing Decision Layer...

[DATA] Fetching snapshots from GA4 & Meta API

[ENGINE] Delta analysis complete: +14% deviation in ROAS

[FINDINGS] Critical issue identified: Checkout drop-off

[ACTION] Generating recommendations...

Solution / Logic

Shopentum was built

A system that tracks marketing state over time

Automatically identifies issues and shifts

Generates recommendations from real data

Prepares evidence for strategic decisions

Speed

Faster decisions

Data is interpreted immediately.

Efficiency

Less manual work

Automation of routine analysis.

Control

Clearer performance visibility

No blind spots in marketing.

Scaling

Growth readiness

No need to constantly increase team size.

Need a technical architect for your AI initiative?

Define the challenge. I will design the logic layer and deploy a system that turns data into decisions.