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01Case study

Multi-Tenant AI Commerce Merchandising Platform

Role
Full-Stack / Platform Engineer

A distributed enterprise SaaS platform that gives retail teams one account-scoped workspace for product assortment, category sequencing, automation, analytics and AI-assisted merchandising across heterogeneous commerce engines.

  • React
  • TypeScript
  • AWS Lambda
  • Auth0
  • DynamoDB
  • SQS
  • Step Functions
  • OpenAI
  • Terraform
  • CloudFront
Enterprise commerce merchandising workspace with catalog sequencing and analytics
02Challenge

The problem

Retail merchandising teams operated across platform-specific admin tools, separate analytics vendors and manual category-ranking processes. The product needed to unify stores, locales, catalogs, behavioral signals and inventory while keeping large, long-running operations responsive and enforcing account-level roles and entitlements.

  • Present one merchandising experience across multiple commerce platforms
  • Support multiple accounts, stores, locales and catalog contexts
  • Combine product, behavioral, social and inventory signals
  • Automate category sorting and scheduled merchandising changes
  • Move long-running and high-volume operations off the interactive request path
  • Enforce authentication, account roles, feature flags and entitlements
  • Deploy safely across development, QA, UAT and production
03Solution

How it was solved

The product was delivered as a distributed platform rather than a monolith: two React applications, centralized identity, an API edge, normalized commerce adapters and focused domain services running primarily on AWS Lambda. Shared auth utilities, asynchronous request orchestration, caching and environment-specific infrastructure connected the system behind account-scoped permissions.

04Evolution

Delivery path

  1. 01 · Authenticate & select account

    Auth0 login and entitled-account selection established the user’s account-scoped roles, stores and feature access.

  2. 02 · Choose commerce context

    Merchandisers selected store, locale, catalog and category before loading normalized product data.

  3. 03 · Browse large catalogs

    Virtualized grids and progress-aware pagination kept category exploration responsive at enterprise catalog scale.

  4. 04 · Sequence products

    Drag-and-drop, bulk moves, clipboard actions, pinning, locking and explicit positioning controlled category presentation.

  5. 05 · Configure automation

    Business rules, linked categories and recurring schedules coordinated automated ranking and synchronization jobs.

  6. 06 · Analyze performance

    Traffic, conversion, revenue, geography, KPIs and executive summaries combined analytics from multiple providers.

  7. 07 · Apply AI intelligence

    Similarity clustering, substitutions, bestsellers, trending products, frequently-bought-together suggestions and narrative KPI reports supported decisions.

  8. 08 · Prewarm delivery

    Scheduled and manual crawler jobs populated caches before high-demand browsing and merchandising sessions.

05Architecture

Platform topology

API Gateway authorizers protected the edge; application hooks enforced account-scoped roles. Human users authenticated through Auth0 with retained OIDC options, while machine-to-machine credentials supported service calls. Terraform reproduced the platform across development, QA, UAT and production.

Merchandising SPA / widget     Admin onboarding SPA
             │                         │
             ├──────────┬──────────────┤
             ▼          ▼              ▼
        Auth0 identity     API Gateway + JWT authorizers
             │                         │
             ▼                         ▼
      Shared auth library       Lambda domain services
                                ├─ config + entitlements
                                ├─ rules + linked categories
                                ├─ analytics + AI pipelines
                                ├─ alerts + async requests
                                └─ cache-prewarming control
                                             │
                                             ▼
        Commerce adapters: SFCC / Magento / HCL / Shopify
                                             │
           ┌─────────────────┬───────────────┴──────────────┐
           ▼                 ▼                              ▼
    Commerce APIs     Analytics providers           AWS data plane
                                              DynamoDB / S3 / SQS
                                              Redis / Step Functions
                                              EventBridge / Fargate
06Product

Product surface

  • Catalog merchandising

    Virtualized grids, progress pagination, drag-and-drop ordering, bulk moves, pinning, locking and explicit placement

  • Product presentation

    Variants, colors, leading images, badges, product groups, attributes, facets and content slots

  • Catalog operations

    Add, copy, replace, remove, publish/unpublish, refresh products and synchronize linked categories

  • Rules & automation

    Sorting rules, recurring schedules, category jobs and Step Functions orchestration

  • Analytics

    Traffic, conversion, revenue, geographic, KPI and executive-summary dashboards with CSV export

  • AI merchandising

    Embeddings, hierarchical clustering, similar products, substitutions, bestsellers, trends and frequently bought together

  • Inventory alerts

    Product watchlists and low-stock, out-of-stock and back-in-stock transitions

  • Platform operations

    Feature flags, account entitlements, multilingual UI, cache warming and embedded documentation assistant

07Engineering

Technical highlights

  • Cross-platform normalization

    A shared adapter layer translated SFCC OCAPI/SCAPI, Magento REST, HCL SOAP/BOD and Shopify GraphQL into consistent product and category operations.

  • Responsive enterprise catalogs

    React Window virtualization, progressive pagination, Redis-backed caching and prewarming workflows kept large category operations usable.

  • Asynchronous command model

    A 202-and-polling façade, SQS, Step Functions and EventBridge moved long-running updates and scheduled automation away from synchronous UI requests.

  • Account-scoped tenancy

    Store configuration, Auth0 role claims, application hooks, feature flags and entitlements controlled both UI surfaces and API capabilities.

  • AI product intelligence

    Embedding and hierarchical-clustering services generated similarity groups and substitutions, while asynchronous OpenAI analysis produced narrative KPI reports.

  • Embeddable delivery

    The primary React product could run as a standalone SPA or custom-element widget using Shadow DOM isolation and extracted styles.

08Integrations

Connected systems

  • Commerce engines

    Adapters supported Salesforce Commerce Cloud, Magento, HCL Commerce and Shopify through their respective REST, SOAP/BOD and GraphQL interfaces.

  • Analytics providers

    A gateway normalized Google Analytics 4, Adobe/Omniture and retained legacy analytics sources behind one cached application contract.

  • Auth0 & OIDC

    Auth0 SPAs, APIs, M2M clients, roles, claims, enterprise SSO and attack protection formed the primary identity layer, with Keycloak and generic OIDC compatibility retained in the UI.

  • OpenAI

    Asynchronous KPI analysis produced narrative summaries from normalized performance data.

  • AWS data plane

    DynamoDB, S3, SQS, Redis, Step Functions, EventBridge and ECS Fargate supported storage, queues, caching, orchestration and crawler workloads.

  • Delivery platform

    Bitbucket Pipelines used AWS OIDC and Terraform to deploy private S3/CloudFront applications, Lambda services and environment-specific infrastructure.

09Scope

What the team delivered

  1. Helped design and deliver a multi-tenant merchandising SPA and embeddable widget

  2. Built platform capabilities across frontend, domain services and AWS infrastructure

  3. Supported normalized integrations across SFCC, Magento, HCL and Shopify

  4. Implemented asynchronous orchestration for long-running category and catalog operations

  5. Contributed to analytics, AI clustering, product alerts and cache-prewarming workflows

  6. Established account-scoped authentication, roles, entitlements and multi-environment delivery

10Services

Repos & services

Anonymized service family across the platform.

ServiceResponsibilityStack
Merchandising ExperiencePrimary catalog, category, analytics and AI workflows as a SPA or embedded widgetReact 17, TypeScript, Redux Saga, Material UI
Operator OnboardingInternal account selection and Auth0-backed merchandiser registrationReact, Auth0
Identity FoundationTenants, applications, APIs, SSO, roles, claims, M2M tokens and shared auth helpersAuth0, OIDC, Terraform
Commerce Integration LayerNormalized product, category and badge operations across commerce enginesSFCC, Magento, HCL, Shopify adapters
Configuration & RulesAccount/store settings, entitlements, sorting, automation and linked categoriesAWS Lambda, DynamoDB, Step Functions
Analytics & IntelligenceProvider normalization, caching, KPI narratives, social signals and AI clusteringLambda, Redis, OpenAI, embeddings
Async OperationsLong-running request façade, jobs, queues, alerts and stock-transition processingSQS, EventBridge, Step Functions
Cache PrewarmingScheduled/manual crawling and operational monitoring for warm catalog cachesECS Fargate, EventBridge
11Outcomes

Results that matter

  • Unified advanced merchandising workflows across four heterogeneous commerce engines

  • Supported multi-account, multi-store and multi-locale operation behind role and entitlement boundaries

  • Moved long-running catalog updates and automation into asynchronous, observable workflows

  • Combined product, inventory, behavioral and AI-derived signals in one merchandising workspace

  • Delivered repeatable development, QA, UAT and production environments through infrastructure-as-code and CI/CD

The represented product spanned 14 repositories across applications, domain services, identity, adapters and AI pipelines on a multi-environment AWS serverless platform.

Enterprise merchandising, multi-tenant SaaS architecture, commerce adapters, asynchronous workflows, AI product intelligence, identity and serverless cloud delivery.

12Boundaries

Scope & caveats

These implementation boundaries keep the public description technically honest.

  • This was a team-built platform across 14 repositories; the role should not imply sole ownership of every subsystem.
  • The social-analytics path included a demonstration provider and should not be described as a complete production integration.
  • Some retained libraries and identity adapters reflected compatibility requirements across an evolving platform.
  • No conversion, revenue or merchandising-lift metrics were supplied, so outcomes are limited to delivered platform capabilities.
13Visuals

Product visuals

Architecture connecting merchandising applications, domain services and commerce adapters
AI clustering, product performance and inventory signals for retail merchandising
14More work

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