Architecture · United States

Development of the Micro-Service Architecture for the US Company

We designed and built a micro-service architecture for a US company, replacing a monolith with independent services that scale, deploy and fail without taking the whole platform down.

The brief
At a glance
Client
US technology company (under NDA)
Industry
Digital platform
Region
United States
Timeline
7 months
Services
Engineering · DevOps enablement
Engagement
B2B

Most businesses care about high scalability. Our client, a US company, needed a tailored solution to strengthen their digital ecosystem and scale further through modern technology.

The client's platform had grown the way most successful products do — one codebase, years of features, and a release process that got heavier every quarter. By the time they came to us, a change to the checkout flow meant rebuilding and redeploying the entire application, and release freezes were measured in weeks.

Scaling was just as blunt an instrument. When one hotspot — search, at peak season — needed more capacity, the whole monolith had to be scaled with it, multiplying infrastructure cost for no user-facing gain. And because everything ran in one process, a failure anywhere could cascade into an outage everywhere.

The mandate was clear: break the monolith into services that scale, deploy and fail independently — without pausing the business while it happened.

From one fragile deployment to a platform of independent services.
From one fragile deployment to a platform of independent services.

The solution

  1. 01

    Domain analysis

    Before touching infrastructure, our specialists applied domain-driven design to the client's actual business: mapping bounded contexts, finding the seams where the monolith naturally wanted to split, and aligning service boundaries with the teams that would own them. The architecture followed the domain — not the other way round.

  2. 02

    Technology stack

    We built the platform on Kubernetes, the open-source container-orchestration standard, with each service packaged in Docker and shipped through its own pipeline. Orchestration handles scheduling, scaling and recovery automatically, so capacity follows real demand — service by service, not platform by platform.

  3. 03

    Resilience and security

    Distributed systems fail in distributed ways, so we engineered for it deliberately: retries with backoff, circuit breakers, fallbacks, bulkheads and timeouts on every inter-service call. A struggling service now degrades gracefully instead of dragging the whole platform down with it.

  4. 04

    Quality and DevOps

    Comprehensive testing ran through every stage — unit, contract, integration and load — and we introduced DevOps practices alongside the code: CI/CD pipelines, observability dashboards and on-call runbooks. The collaboration habits stuck; the client's teams still ship with them today.

Stack
KubernetesDockerJava SE 17Node.js 17RedisAWSCI/CD

How we delivered it

  1. 01

    Domain mapping

    Workshops with the client's engineers and product owners produced a bounded-context map of the whole platform — the blueprint every later decision traced back to.

  2. 02

    Decomposition plan

    We sequenced the split by risk and value: which capabilities to extract first, which could wait, and where the data had to be untangled.

  3. 03

    Platform build

    The Kubernetes foundation went up next — clusters, pipelines, observability — so every extracted service landed on production-grade rails from day one.

  4. 04

    Strangler migration

    Services were carved out of the monolith in waves behind a routing layer, each one shadow-tested against the old path before taking live traffic.

  5. 05

    DevOps enablement

    We handed over more than code: the client's teams learned to own their services end to end, from deployment to incident response.

Containerised workloads, orchestrated by Kubernetes.
Containerised workloads, orchestrated by Kubernetes.
DevOps practices handed over to the client's own teams.
DevOps practices handed over to the client's own teams.

We went from dreading releases to shipping daily. The platform finally moves at the speed of the roadmap.

VP of Engineering, US technology company

The numbers

40+
Deploys per month, up from 2
99.95%
Platform uptime after migration
−35%
Infrastructure cost at peak load
14
Services extracted from the monolith

The outcome

The client received a production-ready solution that significantly improved their ecosystem, enhancing both scalability and overall project profitability — a reminder that cloud-based micro-services are the future of today's business.

Releases that once shipped twice a month now go out daily, because a change to one service no longer risks the rest of the platform. Peak-season capacity is bought where it is needed — search scales, checkout scales, and nothing else has to.

The migration finished without a single big-bang cutover: the strangler pattern retired the monolith wave by wave while the business kept running, and the client's teams now operate the platform entirely on their own.

Your project could be the next one here.

Start a project