Ecommerce development and AI pod teams for retail brands
Senior engineering teams that build Shopify, Adobe Commerce and headless storefronts, wire up OMS, PIM and ERP, and ship search and support AI to production.
By the Ryz Labs team · Updated October 2026
Ryz is an ecommerce development company that staffs senior Latin American engineering teams for retailers and brands: Shopify and Shopify Plus builds, Magento and Adobe Commerce work, headless storefronts, and the OMS, PIM and ERP integrations behind them. Our AI pod teams build product search, catalog enrichment and support agents in your cloud and ship them to production. Every team works on US business hours, so checkout bugs found at 10 a.m. Eastern get fixed the same day.
Retail engineering has a calendar most software doesn't. Code freezes start in October, Black Friday and Cyber Monday traffic can be many times a normal day, and a broken promo rule costs real margin by the hour. Trusted by Fortune 500 engineering teams, Ryz brings in engineers from the top 1% of the tens of thousands we interview, and they work on your team, in your repos and standups.
What we build for retail and ecommerce teams
- Shopify and Shopify Plus storefronts. Online Store 2.0 themes, custom apps on the Admin GraphQL API, Checkout UI Extensions and Shopify Functions for discounts, delivery and payment rules, and Hydrogen storefronts on Oxygen.
- Magento and Adobe Commerce. Module development in PHP, upgrades across 2.4.x releases, Adobe Commerce Cloud deployments, extension conflict cleanup, and replatforming plans for stores that have outgrown a heavily customized install.
- Headless and composable commerce. Next.js or Hydrogen front ends on commercetools, BigCommerce, Salesforce Commerce Cloud or Shopify's Storefront API, with a headless CMS such as Contentful or Sanity for merchandising pages.
- Order management integrations. Order capture, split shipments, ship-from-store and buy online pick up in store flows against Manhattan Active Omni, IBM Sterling, Fluent Commerce or Kibo, with inventory reservations that hold under peak load.
- Product information pipelines. Akeneo, Salsify, inRiver or Pimcore as the source of truth, syndicated to storefronts, marketplaces and Google Merchant Center without hand-edited spreadsheets.
- ERP and fulfillment. NetSuite, SAP S/4HANA and Microsoft Dynamics 365 connections, 3PL and WMS feeds, and EDI 850 purchase orders and 856 advance ship notices for wholesale partners.
- Search and merchandising. Algolia, Constructor or Elasticsearch/OpenSearch with synonyms, facets, boosting rules and query analytics merchandisers can tune themselves.
- Peak readiness. Load tests with k6 or Gatling against realistic carts, CDN and cache rules, queue-based order intake, and Core Web Vitals work on product and category pages.
Choosing a platform comes before any of that. Here is how our teams usually frame it for a client:
| Approach | Best when | Where the engineering goes |
|---|
| Shopify / Shopify Plus | You want the platform to own hosting, checkout and PCI scope | Themes, apps, Functions, integrations to ERP and OMS |
| Magento / Adobe Commerce | You need deep catalog, B2B or pricing customization in code you control | PHP modules, upgrades, performance, hosting and patching |
| Headless / composable | Several brands, regions or channels share one commerce back end | Front end, API orchestration, CMS, caching, more services to run |
Where AI pods help in retail and ecommerce
Retail AI fails in predictable places: bad catalog data, hallucinated product claims, and models that ignore inventory. Our AI pod teams build for those failure modes first.
- Semantic and conversational product search. Embeddings over titles, attributes and reviews, combined with keyword search so SKU and brand queries still match exactly. The hard part is ranking that respects stock, margin and merchandising rules, plus offline evaluation sets built from real query logs.
- Catalog enrichment. LLM pipelines that fill missing attributes, normalize sizes and colors, and draft product copy into the PIM as drafts. Every output is checked against the source spec sheet, and claims about materials or safety stay with a human reviewer.
- Customer service agents. Agents that answer "where is my order", start returns and change addresses through your OMS APIs, with hard limits on refunds and a clean handoff to a person in Zendesk, Gorgias or Salesforce Service Cloud. Our pods have shipped production voice AI: an AI voice platform with more than 1M outbound calls, and a driver-support agent that works in three languages.
- Returns and promo abuse detection. Scoring orders and returns for patterns like wardrobing, coupon stacking and reseller bots. One of our pods built fraud detection for a global fleet company that has confirmed $5.94M in fraud, validated by the client's fraud team. The same pattern applies: features from your own history, explainable scores and a review queue. See our case studies.
- Demand forecasting and replenishment. Store- and SKU-level forecasts in Databricks or Snowflake that handle promotions, stockouts and new items with no history, feeding buyers' allocation tools rather than replacing them.
Regulations and constraints our engineers work within
- PCI DSS v4.0.1. Our engineers have experience keeping card data out of scope with hosted fields and tokenization, and with requirements 6.4.3 and 11.6.1 on inventorying and monitoring scripts on payment pages, which hit retailers with heavy tag-manager use.
- Privacy laws. CCPA/CPRA and the growing list of state privacy laws, GDPR for EU shoppers, Global Privacy Control signals, and consent management with OneTrust or similar tools wired into analytics and ad pixels.
- Marketing rules. TCPA consent for SMS programs, CAN-SPAM for email, and the FTC's rule on fake reviews and testimonials, which matters when AI generates review summaries.
- Accessibility. WCAG 2.1 and 2.2 AA work on storefronts, since ADA website lawsuits against retailers are common in US courts.
- Sales tax. Post-Wayfair economic nexus handled through Avalara, Vertex or TaxJar integrations rather than hard-coded rates.
Our engineers work within your security program and your auditors' controls. Ryz does not claim PCI or SOC 2 certification for its teams.
Integrations and data
Most retail engineering time goes into the seams between systems. Our teams regularly work with:
- Payments and fraud: Stripe, Adyen, Braintree, PayPal, Klarna and Affirm, plus Signifyd, Riskified or Forter.
- Marketplaces and feeds: Amazon Selling Partner API, Walmart Marketplace, Google Merchant Center and Meta catalogs.
- Customer data: Klaviyo, Braze, Segment and loyalty platforms, with event schemas that survive a platform migration.
- Data: Fivetran or Airbyte into Snowflake, BigQuery or Databricks, dbt models for orders, returns and contribution margin. Our data engineering teams build these pipelines.
- Stores: POS systems, endless-aisle tablets, and inventory sync between stores and distribution centers.
How teams engage Ryz
Retailers usually come to us in one of three ways:
- Staff augmentation. You add a few senior Shopify, Adobe Commerce or React engineers ahead of peak season, and they join your existing team. See hire ecommerce developers and hire Shopify developers.
- Dedicated development team. A Ryz team owns a scoped system, such as a headless rebuild, an OMS cutover or a Magento-to-Shopify migration, and ships it with your product owner.
- AI pod. A pod of senior engineers, including a tech lead and an ML engineer, builds search, enrichment or support agents in your AWS or Azure account and runs them to production.
The steps are Talk, Match, Join and Grow. Cost is team size times duration times monthly rate. Typical rates are $7,000–$15,000 per engineer per month: mid-level engineers at $7,000–$10,000 and seniors at $10,000–$15,000, with leads at $15,000+. Four senior engineers at $12,000 a month for six months comes to $288,000. You get a plan, a price and the names of the people before you start.
When Ryz isn't the right fit
- You want a freelancer for a two-week theme tweak, booked self-serve with no conversation. A marketplace fits that better.
- You need follow-the-sun coverage from engineers in Europe or Asia time zones.
- You want a strategy consultancy to run a board-level digital transformation, or you're shopping for a packaged AI platform rather than a team that builds.
Related
FAQ
Do you build on Shopify or on Adobe Commerce?
Both. Our engineers build Shopify and Shopify Plus themes, apps, Functions and Hydrogen storefronts, and they write and upgrade Magento and Adobe Commerce modules in PHP. If you're deciding between them, we'll walk through catalog complexity, B2B needs, PCI scope and who runs hosting before recommending one.
Can a Ryz team handle a replatforming or headless migration?
Yes. A dedicated team plans product, customer and order data migration, keeps SEO with 301 redirect maps, runs both platforms in parallel where needed, and schedules cutover well away from your peak season.
How do your engineers handle payment and customer data?
They work in your environment, under your access controls, logging and change management. We design so card data never touches your servers, using hosted payment fields and tokens, and keep customer data in your accounts. Ryz does not claim PCI DSS or SOC 2 certification; our engineers have experience working within those standards and your auditors' requirements.
What does an ecommerce development team cost?
Typical rates are $7,000–$15,000 per engineer per month, depending on seniority. Multiply team size by months by rate: three senior engineers at $11,000 a month for four months is $132,000. We quote per team after a scoping call.
Will your engineers be available during Black Friday and Cyber Monday?
Our teams work on US business hours, including New York hours, and plan peak coverage with you in advance: code freeze dates, on-call rotation and rollback plans are agreed before November, not during it.
Questions we didn't answer? Email info@ryzlabs.com.