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E-commerce AI Example

E-commerce is a strong AxonFlow use case because the workflow mixes personalization, catalog access, pricing logic, and customer-data governance. Teams want helpful AI behavior, but they also need guardrails around data access, promotions, and customer-facing responses.

What Community Lets You Validate​

With the public/community product you can already validate:

  • governed recommendation or shopping-assistant requests
  • MCP-backed access to product, inventory, or order systems
  • tenant policies around discounts, inventory, or output behavior
  • audit logging for customer-facing AI interactions

That is enough to build serious internal or customer-facing pilots before moving to broader organizational rollout.

Product Capabilities This Example Exercises​

An e-commerce assistant is useful because it combines several AxonFlow capabilities in one workflow:

  • governed customer-facing LLM requests
  • governed access to catalog, order, and inventory data through MCP
  • policy-backed controls around discounts, membership tiers, or promotional behavior
  • audit trails for customer-affecting AI interactions

That makes this a good fit for teams assessing AxonFlow as both an AI governance layer and a practical retail application platform.

Example: Governed Shopping Assistant Request​

import { AxonFlow } from '@axonflow/sdk';

const client = new AxonFlow({
  endpoint: process.env.AXONFLOW_ENDPOINT!,
  clientId: process.env.AXONFLOW_CLIENT_ID!,
  clientSecret: process.env.AXONFLOW_CLIENT_SECRET,
});

const response = await client.proxyLLMCall({
  userToken: 'shopper-98765',
  query: 'Recommend running shoes for a customer who recently bought hiking boots.',
  requestType: 'chat',
  context: {
    provider: 'openai',
    model: 'gpt-4o',
    membership_tier: 'gold',
    region: 'us-west',
  },
});

Example: Governed Catalog Query​

from axonflow import AxonFlow

with AxonFlow.sync(
    endpoint="http://localhost:8080",
    client_id="ecommerce-app",
    client_secret="secret",
) as client:
    result = client.mcp_query(
        "postgres",
        "SELECT sku, name, price, inventory_count FROM products WHERE category = 'running' LIMIT 20",
    )

    print(result.policy_info)
    print(result.data)

Community Pattern​

The most common e-commerce pattern is:

  1. the storefront or backend sends a recommendation request through the Agent
  2. policies evaluate the request and customer context
  3. the app retrieves governed inventory or catalog data through MCP
  4. the final response is returned with policy metadata and audit coverage

This pattern works well for product recommendation, shopping copilots, and internal merchandising assistants.

Real-World Variations​

Teams usually adapt this pattern in one of three ways:

  • shopping assistant: draft product suggestions and answer buyer questions
  • merchandising copilot: help internal teams analyze assortment, inventory, and pricing
  • service assistant: combine order lookups with governed response drafting for support flows

The underlying AxonFlow pattern is the same, but the policies, prompts, and MCP connector mix change based on the business workflow.

When Evaluation or Enterprise Helps​

As the rollout expands, teams often want:

  • richer approval and review workflows for pricing or promotional actions
  • broader operations visibility through protected dashboards
  • enterprise identity and policy-management workflows
  • deeper organizational governance across many teams and lines of business

For many retailers, that moment comes when the AI layer is no longer an isolated feature and starts influencing customer communication, merchandising operations, or revenue-sensitive automation at scale.

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