Case Study
Case Study
Unified Commerce Intelligence Ecosystem
Commerce intelligence platform governing decisions in real time.Enables precise, scalable, self-optimizing personalization.
Year:
2026
Industry:
e-commerce
Team:
45 people
Approach
We audited the e-commerce ecosystem to assess how customer signals, product data, and pricing logic drive decisions. This exposed fragmentation between intent detection and execution, limiting personalization and responsiveness. Targeted research revealed gaps across key shopper journeys. These insights led to a Controlled Autonomy Architecture, aligning real-time signals, governed decisioning, and dynamic execution.
We audited the e-commerce ecosystem to assess how customer signals, product data, and pricing logic drive decisions. This exposed fragmentation between intent detection and execution, limiting personalization and responsiveness. Targeted research revealed gaps across key shopper journeys. These insights led to a Controlled Autonomy Architecture, aligning real-time signals, governed decisioning, and dynamic execution.
We audited the e-commerce ecosystem to assess how customer signals, product data, and pricing logic drive decisions. This exposed fragmentation between intent detection and execution, limiting personalization and responsiveness. Targeted research revealed gaps across key shopper journeys. These insights led to a Controlled Autonomy Architecture, aligning real-time signals, governed decisioning, and dynamic execution.
Architecture
The platform is built as a layered system combining signal intelligence, decision governance, and execution orchestration. A unified data layer feeds AI models generating real-time insights on intent and demand. A decision layer applies business constraints, while modular agents activate recommendations, pricing, and promotions across channels.
The platform is built as a layered system combining signal intelligence, decision governance, and execution orchestration. A unified data layer feeds AI models generating real-time insights on intent and demand. A decision layer applies business constraints, while modular agents activate recommendations, pricing, and promotions across channels.
The platform is built as a layered system combining signal intelligence, decision governance, and execution orchestration. A unified data layer feeds AI models generating real-time insights on intent and demand. A decision layer applies business constraints, while modular agents activate recommendations, pricing, and promotions across channels.
Outcome
The system enables continuous optimization of commerce performance. It improves conversion, AOV, and customer lifetime value, while increasing promotion efficiency and reducing manual operations. At the same time, it ensures all decisions remain controlled, auditable, and aligned with business goals.
The system enables continuous optimization of commerce performance. It improves conversion, AOV, and customer lifetime value, while increasing promotion efficiency and reducing manual operations. At the same time, it ensures all decisions remain controlled, auditable, and aligned with business goals.
The system enables continuous optimization of commerce performance. It improves conversion, AOV, and customer lifetime value, while increasing promotion efficiency and reducing manual operations. At the same time, it ensures all decisions remain controlled, auditable, and aligned with business goals.
Conversion Rate
+25%
CPA
-35%
Automation of routine decisions
70%
Human intervention
-60%
Increase in CLV
+35%
DECISION LATENCY
< 100 ms
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Get in touch
Whether you have questions or just want to explore options, I'm here.