Vaikora for Retail and E-commerce

When an AI customer-service agent has access to order data, what prevents it from issuing refunds outside policy? When a dynamic-pricing agent adjusts product prices, what proves the adjustment was within authorized bounds? When a fraud-screening agent processes payment data, how do you keep PCI compliance with an AI in the loop?

Vaikora is the deterministic policy layer for retail AI agents handling customer data, payment information, pricing decisions, and fulfillment workflows.

The retail AI agent problem

Retail moves fast and uses AI in three areas where the cost of unbounded agent behavior shows up immediately: customer-service automation (refunds, account changes, promotional credits), pricing and merchandising (dynamic price adjustments, inventory routing), and fraud/payment processing (transaction screening, payment data handling).

Specific challenges:

• Refund authority creep when AI customer-service agents have unbounded discretion
• Price discrimination risk from dynamic-pricing agents (consumer-protection rules in EU and CA)
• PCI DSS compliance when AI agents touch payment data
• Promotion abuse when agents authorize discounts outside policy

Compliance frameworks Vaikora addresses

• PCI DSS 4.0: scope reduction by enforcing rules at the agent action layer, audit trail for any AI-handled payment data
• GDPR Article 22 (automated decision-making): documented human-review pathways for material AI decisions
• California ADMT regulations: notice and opt-out enforcement for AI-driven decisions
• FTC consumer protection: provable consistency in customer-facing decisions to avoid unfair-and-deceptive-practices claims

Sample policy rules

- name: refund_limit_per_agent_session
  match: { tool: payment.refund, context.session.refunds_issued: "> 5" }
  decision: require_approval

- name: refund_amount_cap
  match: { tool: payment.refund, arg.amount: "> 200" }
  decision: require_approval

- name: dynamic_pricing_bounded
  match: { tool: catalog.update_price, arg.new_price.delta_pct: "abs > 30" }
  decision: deny

- name: payment_data_does_not_leave_pci_zone
  match: { tool: ["external_api.*", "email.send", "log.write"], payload.contains_pan: true }
  decision: deny

- name: promotional_credit_cap
  match: { tool: promotion.issue, arg.value: "> 50" }
  decision: require_approval

Resources

• Vaikora flagship product page
• State of AI runtime control 2026
• Book a demo

FAQs for Retail

• Can Vaikora keep AI agents outside PCI DSS scope? Yes, with the right deployment pattern. Rules can enforce that no agent ever directly handles primary account numbers (PANs), and that payment data flows are restricted to PCI-scoped systems. This is a scoping decision specific to each customer’s environment.
• Does Vaikora support GDPR Article 22 human-review requirements? Yes. The require_approval decision routes specific AI-initiated decisions to human reviewers, with full evidence of the routing and approval captured in the audit log.
• Can rules cap refunds per agent or per session? Yes. Rules can reference per-session state, per-agent state, per-customer state, or any combination.
• Is there a starter ruleset for retail? Yes. Vaikora ships with a starter ruleset covering refunds, promotions, dynamic pricing, account changes, and payment handling.