Retail technology built for the decisions that drive margin.
Most retail technology environments evolved channel by channel — an e-commerce platform that does not know what happens in store, a loyalty system that cannot see the warehouse, a POS environment carrying years of accumulated integrations that were never fully documented. The result is a customer experience built on systems that were never designed to work together. Shelorve connects them — and builds the intelligence layer that turns connected data into decisions.
Most retail architectures treat in-store, online, and app customers as separate people — because the underlying systems were built at different times by different teams. The result is inconsistent pricing, broken promotions, loyalty points that do not transfer, and inventory that the website shows as available when the warehouse shows as zero. The customer experiences a single brand. The technology does not reflect that reality — and the customer notices.
POS environments running the business
Enterprise POS environments carry years of accumulated integrations, customizations, and dependencies that were never fully documented. Payment gateway connections, loyalty platform links, inventory feeds, and back-office interfaces built incrementally over time create an integration landscape that is genuinely complex to change without risk. The challenge is not the platform — it is everything that has been built around it, on top of it, and between it and the rest of the enterprise over the years.
Demand forecasting running on intuition
Most retail organizations have the data to forecast demand accurately — transaction history, seasonal patterns, promotional calendars, external market signals. Most do not have the data platform infrastructure to connect and use it. Buying decisions are made on spreadsheets when they could be made with models trained on years of actual sales data. The gap is not data. It is the absence of a platform that makes that data usable — and models that the buying team trusts enough to act on.
Customer data fragmented across systems
Loyalty data in one system. E-commerce purchase history in another. In-store transaction data in a third. Customer service records in a fourth. Personalization is impossible when the systems do not share a common customer identity. The unified customer data platform is not a nice-to-have — it is the foundation every AI model, every marketing decision, and every service interaction depends on. Without it, every downstream investment underperforms.
The gap between retail data and retail decisions
Most retailers have more data than they have ever had — transaction history, browse behavior, loyalty interactions, returns data, competitor pricing signals. Most are still making buying, pricing, and marketing decisions the same way they did a decade ago. The gap is not the absence of data. It is the absence of models trained on what is actually happening in the business, connected to the systems where decisions are made, and trusted by the people who need to act on them.
Legacy modernization methodology applied to POS and retail systems
0
Big-bang cutovers — phased delivery protects store operations throughout
Real-time
Omnichannel data platform connecting POS, warehouse, and e-commerce
Retail · Common Questions
What retail leaders ask us before they engage
Every POS modernization engagement begins with a complete dependency assessment of the environment — mapping every integration the system touches, including payment gateways, inventory systems, loyalty platforms, back-office accounting, and any custom interfaces that have accumulated over years of incremental change. This applies whether the estate runs Oracle Retail Xstore, a legacy custom platform, or a combination of both. The migration is phased by region or banner so no single cutover affects the entire estate. The existing system runs in parallel until each phase is confirmed stable in production — not just in testing.
The foundation is a unified customer identity that resolves the same customer across in-store, online, app, and loyalty touchpoints. On top of that: a real-time event stream via AWS Kinesis that captures every transaction and interaction as it happens; a data lake on S3 and Redshift for historical analysis and model training; PCI DSS-compliant network segmentation that keeps cardholder data isolated from the broader data environment; and the API layer that makes this unified data available to Salesforce, the e-commerce platform, and the merchandising systems that need to consume it. The result is a single operational picture of the business — not five separate views that never agree.
Shelorve builds production ML systems across the full range of retail decision types.
Demand forecasting — Models trained on transaction history, promotional calendars, seasonal patterns, and external signals. Typical improvement of 30–40% versus spreadsheet or rules-based forecasting, connected directly to inventory planning and replenishment systems.
Dynamic pricing — Real-time pricing models that respond to demand signals, competitor pricing data, inventory levels, and margin targets. Built to operate at the speed of e-commerce without requiring manual intervention on every SKU.
Personalization — Product recommendation engines and next-best-offer models trained on unified customer data across in-store, online, and loyalty touchpoints. Served via API to e-commerce platforms, email platforms, and Salesforce Marketing Cloud.
Customer lifetime value and churn prediction — Models that score customers by long-term value and likelihood to lapse, enabling marketing investment decisions based onpredicted return rather than last-click attribution
Customer lifetime value and churn prediction — Models that score customers by long-term value and likelihood to lapse, enabling marketing investment decisions based on predicted return rather than last-click attribution.
Inventory optimization — Models that recommend optimal stock levels by SKU, location, and season — reducing both overstock and out-of-stock events simultaneously.
Visual search and product discovery — Image-based search and similarity models for e-commerce, enabling customers to find products from photos rather than keyword queries.
Fraud and returns abuse detection — Anomaly detection models that identify returns fraud, promotion abuse, and account takeover patterns before they affect margin.
All built with explainability so the teams who act on these models understand why each recommendation is being made — and trust it enough to change how they work.
Yes. Shelorve integrates Salesforce with Shopify, Salesforce Commerce Cloud, Magento, SAP Commerce, and custom-built e-commerce platforms using MuleSoft. The integration brings order history, browse behaviour, and customer service interactions into a unified Salesforce record — enabling personalisation and service that reflects the customer's full relationship with the brand.
Customer data unification requires a master data management approach — defining a canonical customer identity and the resolution logic that matches the same person across systems with different identifiers. Shelorve designs the identity resolution rules, builds the matching pipeline on AWS, and integrates the unified record into Salesforce as the system of truth for customer interactions.
Retail
Working in retail?
Retail transformation starts with understanding what your systems are actually doing.
Tell us what you are trying to solve. We understand omnichannel complexity, the operational risk of POS modernization, and the customer data and AI challenges that every modern retailer is navigating — and we have delivered in production environments where getting it wrong was never an option.