Launch-Scale Commerce: Architecture as Revenue Capacity

A global athletic apparel and footwear brand engaged Taller to staff backend engineering capacity for its launch-scale commerce infrastructure.

Sneaker drops created demand spikes that compressed an entire region’s commerce traffic into a matter of minutes. Any delay at that moment cost sales directly: even a fraction of a second of lag at peak load (the p99, the slowest one percent of requests) translated into measurable lost revenue. Before modernizing, the client’s systems ran on aging databases (Cassandra and Couchbase), pushed search updates only once every two months, and gave the team little visibility into the short-lived software services that spun up and vanished during a launch. The client came to Taller as part of a larger effort to turn the commerce architecture itself into revenue capacity: fast, reusable, and resilient enough to hold up during the brand’s biggest launch moments.

Under the client’s GraphQL commerce initiative, lightweight GraphQL gateways (a single efficient layer that sits above hundreds of separate service APIs and coordinates them) powered checkout, cart, wishlist, content, the main consumer app, and the product-customization platform. Behind the scenes, workloads moved off those aging databases onto DynamoDB (Amazon’s high-speed managed database); search ran on Amazon Elasticsearch, the social graph for tens of millions of users on AWS Neptune, and an event-stream database fired notifications to millions of users. A new observability layer stitched together metrics, traces, and logs across those short-lived services during launch events, so the team could see what was happening in real time.

Taller’s backend and Java API engineers worked directly inside this environment, building REST APIs, running DynamoDB under heavy simultaneous load, and operating the streaming (Kafka and Kinesis), deployment automation (Jenkins), content delivery (CloudFront), infrastructure-as-code (CloudFormation), and container tooling (Kubernetes and Docker) that kept it moving. Taller’s QA engineers were also embedded in the client’s production release pipeline, supporting launch-event testing alongside the commerce work.