Multi-brand commerce migration ahead of a platform shutdown

Rescuing 10+ storefronts and modernising onto a headless commerce stack, with four agents running the migration in production.

Outcome

All 10+ brands live before the shutdown with zero revenue-loss downtime, manual migration effort down 70%, and about 20% added revenue in year two.

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At a glance

Client & Industry

Multi-brand direct-to-consumer wellness company, 10+ storefronts, air and water purification

Engagement

Managed service, fixed-scope phases, from platform rescue to commerce modernisation

Scale

768K+ customer profiles, 610K+ orders, 31,500 warranty records, TV and media traffic spikes

Maps to (service pillars)

Agentic AI & AgentOps, AI & ML Engineering, Cloud & Platform Engineering, AI-Ready Data Engineering

Agivant’s Role

Migration and data engineering across platform move, headless architecture, and production rollout

Where it started

A hard deadline against the whole storefront estate, on a stack that could not carry peak-sales traffic.

Business problem

A three-month platform shutdown put all storefront revenue at risk, while 8 to 10 second loads and checkout failures cost peak-sales conversions.

No decision framework

Incompatible schemas across the legacy platform and WooCommerce, a zero-loss move of 1.38M records, 31,500 warranty records in custom EAV structures, and 30+ performance-degrading plugins. Traditional ETL and manual QA could not meet the date.

Agivant’s Role

Migration and data engineering across platform move, headless architecture, and production rollout

What Agivant built

Forward Deployed Engineers embedded across order management, marketing, brand, and support. Discovery two weeks, prototype and schema mapping three weeks, then production sprints and a brand-by-brand rollout with automated go-live gates. Value in days and weeks.

Data Migration agent

Schema-aware ETL from the legacy platform and WooCommerce, with retry and audit trails.

Data Validation agent

Source-to-destination integrity checks on products, customers, orders, and warranty metafields.

SEO and URL Mapping agent

Automated 301 redirect rules and drift monitoring after cutover.

QA and Regression agent

Automated journey tests across browse, cart, checkout, and account, plus visual regression per brand.

How it is built

Platforms	Data and reporting	Marketing and CX	Support app	Production agents	Controls
Legacy commerce platform, WooCommerce, Shopify Plus	Azure SQL, Salsify	Klaviyo, Loop, Yotpo, Signifyd, Statsig, Triple Whale, DataGrail	React and Next.js	Data Migration, Data Validation, SEO and URL Mapping, QA and Regression	Human approval at go-live, role-scoped access, staged per brand

Before

A monolith with manual scripts, siloed brand databases, and no automated QA.

After

An agent layer feeding unified pipelines into Shopify Plus, headless APIs, Next.js and Hydrogen frontends, and automated go-live gates.

Proof in production

At the first brand cutover, the Data Migration agent moved 768K profiles overnight, Validation flagged 0.3% metafield mismatches, SEO generated 4,200 redirects, and QA ran 847 checkout scenarios, all before sign-off. A three-week data sprint plus a one-week QA cycle became a 48-hour pipeline.

Measured results

All 10+ brands live before the shutdown, with zero revenue-loss downtime.

+1.5%

Storefront conversion

+15%

Support-channel conversion, unified app

70% down

Manual migration effort

80% down

Campaign deployment time

91 to 100

Mobile PageSpeed, lead brand

~20% up

Year-two revenue

The repeatable pattern

Reusable asset

The Commerce Migration Blueprint reuses 80%+ of agent configs on later moves, and a new storefront joins the support cockpit in under two hours. Every build amplifies the next.

Challenges overcome

Parallel workstreams across 10+ brands on one deadline, a shift to scoped admin access, and 31,500 warranty records restructured into Shopify metafields with no customer-facing disruption.

“We needed a partner who could rescue our storefronts fast and set us up for the long term. The AI-accelerated approach did both.”

Client leadership

Enterprise movement

Foundational

Operational

Path to autonomous