AI that turns retail complexity into connected customer and operational intelligence

Yes. The platform is built with enterprise-grade security, including embedded guardrails, observability, access controls, and encryption. Audit-ready reporting demonstrates model transparency and fairness, giving retail teams and executives confidence in every AI-driven decision.

Our platform supports industry and regional compliance standards relevant to retailers, including PCI DSS for payment data, GDPR, CCPA, and other consumer privacy frameworks. You retain full ownership and control over your data and AI models, helping ensure alignment with regulatory expectations across every market you operate in.

Designed for composability and openness, the platform integrates with core retail systems including e-commerce platforms, order management, POS, loyalty platforms, ERP, CRM, data warehouses, and LLMs. This allows retailers to leverage new AI innovations across marketing, service, merchandising, and loss prevention without being locked into legacy investments.

Yes. The platform runs across hyperscaler public clouds, private clouds, and on-premises environments. Flexible deployment options, consumption-based or license-based packaging, and SLAs enable retailers to scale AI initiatives across brands, regions, and channels while maintaining operational control and governance.

AI improves marketing performance by replacing broad, batch-based segmentation with precise microsegments built from behavior, lifecycle stage, channel engagement, and purchase history — ensuring offers reach shoppers who are actually likely to respond. Generative AI personalizes creative across email, SMS, push, and web, while real-time decision engines deliver next-best offers during live site and app sessions. This shifts marketing from periodic campaign pushes to continuous, personalized engagement tuned to each shopper.

AI reduces contact center costs by automating the high-volume, low-complexity inquiries that drive most call and chat volume — order status, returns, delivery issues, and loyalty questions — using NLP-powered virtual assistants that resolve common requests without human intervention. When a shopper does need an agent, AI passes full order and customer context to reduce handle time, and real-time guidance surfaces in-call recommendations that improve first-contact resolution and CSAT.

AI improves forecasting by continuously sensing demand signals across sales, inventory, weather, promotions, and competitive pricing, then generating forecasts at the SKU, store, and channel level — rather than relying on static historical models that can’t adapt to fast-moving conditions. Recommendation engines surface assortment, replenishment, and pricing actions tied to margin and sell-through goals, and AI agents flag anomalies so merchandising teams can focus on decisions that actually move the business.

AI detects fraud by continuously analyzing transaction, return, loyalty, and session data to identify patterns that rules-based systems miss — including return abuse, promotion fraud, account takeovers, and first-party fraud. Rather than flagging issues after losses have occurred, AI models score risk in real time across returns, orders, and accounts, and AI agents automate case triage while keeping human investigators in the loop for final decisions. This shifts loss prevention from reactive investigation to continuous, proactive control.

Most AI platforms for retail address specific functions in isolation — a personalization engine here, a forecasting tool there, a fraud rules system elsewhere — without connecting those workflows into a governed, unified execution layer. Uniphore operates across the full range of retail operations on a single composable platform, applying policies and controls during execution rather than after the fact. Combined with sovereign deployment options that allow retailers to run AI on any cloud or on-premises environment, and fine-tuned models built for retail-specific workflows, Uniphore gives retailers the control that point solutions and general-purpose AI platforms weren’t designed to deliver.