# The Future of Agentic Post-Purchase Starts Here Narvar is leading the next frontier of agentic commerce — bringing intelligent, policy-aware automation to post-purchase. Powered by deep integrations, behavioral signals, and brand-safe AI, we turn complexity into seamless resolution at scale. ## Agentic Solutions for Every Use Case ### Post-purchase is ready for agentic disruption Post-purchase is where loyalty is earned, yet friction still persists. For consumers, it feels like a chore. For retailers, it’s costly and difficult to manage across delivery, service, fraud, and returns. The work is manual, fragmented, and hard to scale — exactly where agentic systems are needed. ### Instant answer across all agentic solutions Narvar’s agent NAVI meets the consumer wherever they are, on a retailer’s website, in-app, or inside agentic assistants. Narvar instantly delivers clear tracking, delivery status, and next steps. The MCP (Modern Content Protocol) orchestrates every step, activating [IRIS™ intelligence](https://corp.narvar.com/iris) and retailer policies to resolve the inquiry automatically, without manual intervention. - **Reduces support costs by removing high-volume WISMO contacts** - **Strengthens loyalty by meeting consumers where they already are** ### Automated delivery issue resolution A consumer reports a missing package through the retailer’s support interface. Narvar, operating above the CSP layer, evaluates eligibility and instantly approves the claim, orchestrating replacement actions across Gladly, Kustomer, Salesforce, Zendesk, and Microsoft Dynamics. Narvar evaluates claim conditions and enforces retailer policies using IRIS™ intelligence to deliver consistent, instant outcomes. - **Reduces support costs and eliminates manual claim handling** - **Improves delivery experience by providing immediate resolution** ### Proactive, policy aligned exchanges A consumer indicates an item does not fit. Narvar checks inventory in real time, determines the correct size, and offers an exchange option instantly. The MCP Agent orchestrates identity, policies, and inventory signals through IRIS™ to convert a return into a retained sale. - **Post-purchase is where loyalty is earned; proactive solutions increase repeat engagement** - **Reduces refunds and protects margin by driving exchanges** ### Fraud and abuse prevention Unified Policy Control: our centralized governance layer unifies return, claim/appeasement, and service policies across channels (in-store, online, call center). Narvar’s agent, NAVI, applies risk and policy logic to enforce consistent decisions and communicates the trust score with NAVI, in real time. - **Retailers need governed automation to protect margins and manage growing fraud exposure.** - **Claims and returns are among the most expensive areas where automated decisions create immediate financial impact.** ### Intelligence loops in admin Signals from carriers, returns, and consumer interactions surface optimization opportunities. Narvar recommends policy adjustments such as enabling proactive exchanges, along with projected outcomes. IRIS™ evaluates your policies against the top global brands, then provides expert guidance on key adoptions or changes your brand should make along with the results they’ll deliver. - **Narvar continuously learns and recommends smarter policies that raise performance over time** - **Reduces manual oversight by turning insights into actionable guidance** ## The Agentic Post-Purchase Experience Narvar Agentic Post-Purchase from Narvar on Vimeo ### Agentic AI, designed for retailers Agentic post-purchase is an intelligence and autonomy layer powered by IRIS™. It connects identity, policy, and real-time system signals to automate actions across delivery, service, fraud, and returns. ### Frequently asked questions #### What is agentic post-purchase? Agentic post-purchase is an intelligence and autonomy layer that uses identity, retailer policies, and real-time signals to automate decisions across delivery, service, fraud, and returns. It delivers autonomous post-purchase decisioning that resolves issues instantly while preserving a seamless consumer experience. #### How is this different from traditional automation? Traditional automation follows static workflows. Agentic post-purchase applies IRIS™ intelligence and real-time policy logic to determine the correct action in context. This enables real-time post-purchase policy automation, dynamic delivery exception management, AI-driven returns policy execution, and automating post-purchase fraud checks without manual review. #### Does this replace my existing systems? No. Narvar’s agentic post-purchase model works across your current service and commerce stack. The agent embeds directly into platforms such as Salesforce, Zendesk, and Microsoft Dynamics and orchestrates decisions without altering your architecture. #### How does the agent interact with consumers? The agent meets consumers wherever they are, including agentic assistants, retailer sites, and support channels. It returns rich, actionable responses such as tracking cards, proactive exchanges, claim outcomes, and delivery service intelligence in real time. #### What actions can the agent take? The agent can answer delivery questions, surface real-time tracking, offer proactive exchanges, approve or deny claims, check inventory, apply retailer policies, and trigger downstream workflows inside existing systems. These actions enable intelligent post-purchase automation across delivery, service, fraud, and returns. #### How does IRIS™ support agentic decisioning? IRIS™ applies identity and policy logic to each interaction, evaluates available signals, and determines the correct action instantly. Over time, it learns from delivery, returns, claims, and consumer interactions, recommending policy adjustments that improve performance and reduce friction. #### Can retailers control the policy rules? Yes. Retailers set their policies and risk rules, and the agent enforces them consistently. Narvar’s admin experience also provides recommendations based on observed patterns and outcomes.