THE ROCENTO PLATFORM
Commerce.
Context.
Intelligence.
One connected architecture, from the first interaction to the next informed action.
Explore the layers ↓01 / Commerce core
A dependable transactional foundation.
Catalog, customers, carts, checkout, orders, inventory and promotions form the operational center. AI assistance depends on authoritative commerce facts; it does not replace transactional rules.
Core capability02 / Composable storefront
Your brand. Your customers. Their shopping experience.
Create a storefront that reflects your brand and the customers you serve. Connect product discovery, search and purchasing in one consistent journey, with experiences tailored to customer segments and delivery areas. Adapt as your business grows, while keeping commerce operations connected.
Core capability03 / Merchant administration
A workspace for the people behind the store.
Give your team one workspace to manage products, serve customers, fulfill orders and understand performance. Connected catalog, inventory, promotions and reporting help people make informed decisions throughout the working day.
Core capability04 / APIs & documentation
Connect commerce to the way you work.
Build integrations around your business with clearly defined commerce APIs. Connect customer experiences and operational workflows through a shared foundation, with clear contracts between your systems.
Integration foundation05 / Behavioral data
Signals with a purpose.
Searches, product views, cart changes, checkout and conversion events add context to transactions. Useful signals need clear event definitions, identity boundaries and retention decisions. Collection alone is not a prediction model.
Event collection06 / Commerce analytics
Operational activity, made legible.
Revenue, product, customer, inventory and cohort reporting create a basis for operational understanding. Analytical context complements current prices, stock and order state; current availability and pricing remain governed by commerce services.
Operational reporting07 / AI & machine learning
A foundation for learning.
Predictive recommendations, forecasting, intent, conversion and abandonment prediction, and AI-based segmentation are areas for continued development. These predictive models are not presented as validated production capabilities. Rule-based customer segmentation is available today using customer, completed-order and saved-cart data. Current shopping assistance uses bounded filtering and ranking grounded in catalog facts.
Research & development08 / Controlled AI agents
Useful assistance. Explicit boundaries.
The assistant MVP retrieves commerce facts through tenant- and persona-scoped read-only tools and approved knowledge retrieval. Product discovery, order status and support flows have documented scope. Broader autonomous actions remain in development. Assistant quality, provider configuration and deployment readiness require evaluation for each use case.
Assistant preview09 / Security boundaries
Access follows the application.
Agents use approved application capabilities rather than unrestricted database access. Authorization, scoped tools and authoritative commerce responses establish boundaries. Production deployment requires security evaluation and operational controls. See the security overview for current assurance information.
Design & ongoing validation10 / Extensibility
Room to evolve without losing the foundation.
Extend your storefront, connect new services and introduce new capabilities as your business evolves. A modular platform gives your teams room to improve customer experiences while keeping core commerce operations connected.
Architectural approach11 / ARCHITECTURE
From interaction to informed action.
Customer experience commerce platform behavioral events commerce context AI intelligence controlled assistance. Prediction and autonomous actions are future extensions of this foundation.
Read the architectural philosophyREADY FOR A CLOSER LOOK?