🌐 Microservices • Cloud • Scalability • Resilience • Security

System Architecture:
Building Systems That Scale

We design robust, scalable, and secure system architectures that power your applications and support your business growth.

Horizontal Scalability

Architectures designed to scale out seamlessly as demand grows

Built-In Resilience

Systems that self-heal and maintain availability during failures

Performance Optimized

Architectures tuned for low latency and high throughput

System Architecture Solutions

From monolith to microservices — we design architectures that scale.

Microservices Architecture

Break down monolithic applications into independent, scalable services that can be developed, deployed, and scaled independently.

  • Domain-driven design approach
  • API gateway implementation
  • Service discovery & registry
  • Distributed tracing & monitoring

Cloud-Native Architecture

Design systems that fully leverage cloud capabilities for scalability, resilience, and cost efficiency.

  • Serverless architecture design
  • Containerization strategy
  • Managed service integration
  • Cost optimization strategies

Event-Driven Architecture

Build responsive, loosely coupled systems that react to events in real-time for better scalability and resilience.

  • Message broker implementation
  • Event sourcing & CQRS patterns
  • Real-time data processing
  • Stream processing pipelines

API-First Architecture

Design systems around well-defined, versioned APIs that enable seamless integration and future-proof your architecture.

  • OpenAPI/Swagger specification
  • API versioning strategy
  • API security & rate limiting
  • Developer portal implementation

Serverless Architecture

Build highly scalable applications without managing infrastructure, paying only for what you use.

  • Function-as-a-Service design
  • Event-driven workflows
  • State management strategies
  • Cost monitoring & optimization

Legacy Modernization

Strategically modernize legacy systems while minimizing risk and maintaining business continuity.

  • Strangler fig pattern implementation
  • Database modernization strategy
  • Incremental migration approach
  • Parallel run & validation

Our System Architecture Process

A proven methodology for designing robust, scalable systems.

1

Current State Assessment

We analyze your existing systems, traffic patterns, pain points, and business requirements to understand your architecture needs.

  • System inventory & dependency mapping
  • Traffic pattern analysis
  • Performance & reliability assessment
  • Cost structure analysis
2

Architecture Design

We design a target architecture that meets your scalability, reliability, and security requirements with clear migration paths.

  • High-level architecture blueprint
  • Technology selection & justification
  • Scalability & failure mode planning
  • Security & compliance considerations
3

Proof of Concept

We build and test critical components to validate the architecture before full implementation.

  • Key component implementation
  • Performance & stress testing
  • Failure scenario simulation
  • Cost & performance validation
4

Implementation & Migration

We implement the new architecture with minimal disruption using phased migration strategies.

  • Incremental implementation plan
  • Blue/green or canary deployments
  • Comprehensive monitoring setup
  • Documentation & knowledge transfer

Architecture Patterns We Implement

Modern patterns for building scalable, resilient systems.

Microservices Pattern

Decompose applications into small, independent services that run in their own processes and communicate via lightweight mechanisms.

When to use:Complex applications with multiple teams
Key benefits:Independent scaling & deployment
Challenges:Distributed transactions
Example:E-commerce platform

Event Sourcing

Store state changes as a sequence of events rather than just the current state, enabling powerful temporal queries and audit trails.

When to use:Audit requirements or complex workflows
Key benefits:Complete history & temporal queries
Challenges:Event schema evolution
Example:Financial transaction system

CQRS

Separate read and write operations into different models to optimize performance, scalability, and security.

When to use:High read/write ratio applications
Key benefits:Optimized data models for each operation
Challenges:Eventual consistency
Example:Social media platform

Circuit Breaker

Prevent cascading failures by temporarily cutting off calls to a failing service and allowing it to recover.

When to use:Distributed systems with dependencies
Key benefits:System resilience & graceful degradation
Challenges:State management across instances
Example:Payment gateway integration

Saga Pattern

Manage transactions across multiple services using a sequence of local transactions with compensating actions for rollback.

When to use:Distributed transactions across services
Key benefits:Maintain data consistency without 2PC
Challenges:Compensating action design
Example:Order processing system

Strangler Fig Pattern

Gradually migrate functionality from a legacy system to a new system by routing requests through a facade.

When to use:Legacy system modernization
Key benefits:Risk mitigation through incremental change
Challenges:Facade complexity over time
Example:Banking system modernization

Architecture Impact

How our system architecture designs transform business outcomes.

99.99%
System Uptime

Average uptime achieved through resilient architecture patterns

Industry average:99.5%
Downtime:52 mins/year vs 43.8 hrs/year
68%
Cost Reduction

Cloud cost savings through optimized architecture and resource usage

Before optimization:$14,200/mo
After optimization:$4,500/mo
5.2x
Throughput Increase

Requests per second handled after architecture optimization

Before:850 RPS
After:4,420 RPS
73%
Faster Scaling

Time reduction for scaling operations during traffic spikes

Manual scaling:45-60 mins
Automated scaling:12-18 mins
Success Story

E-Commerce Platform: 10x Traffic Handling

A major e-commerce platform was experiencing frequent outages during peak shopping periods.

  • Redesigned monolith as microservices
  • Implemented event-driven architecture
  • Added circuit breakers & retries
  • Migrated to AWS with auto-scaling
10x
Traffic Handling
99.99%
Uptime Achieved
"ArchiTech transformed our architecture from a fragile monolith to a resilient, scalable system. Black Friday traffic no longer keeps us up at night."
— David Chen, CTO

Our Architecture Principles

The guiding principles behind every system we design.

Right-Sized Complexity

We avoid both over-engineering and under-engineering by matching complexity to actual business needs and growth projections.

Our approach: Start simple, add complexity only when validated by metrics

Automated Resilience

Build self-healing systems that automatically recover from failures without human intervention.

Our approach: Implement circuit breakers, retries, and fallbacks at every layer

Observability-Driven

Design systems with comprehensive metrics, logging, and tracing from day one to enable rapid debugging and optimization.

Our approach: Implement distributed tracing and business-relevant metrics

Performance by Design

Optimize for performance at the architectural level, not just through code optimization.

Our approach: Design data flows and access patterns for low latency

Ready to Build a System That Scales With Your Business?

Let's design an architecture that supports your growth, ensures reliability, and optimizes costs.

Architecture assessment report Target architecture blueprint No-obligation consultation
Architecture Decision Record Sample
// ADR: Microservices vs Monolith
date: 2023-08-15
status: accepted
// Context
Our e-commerce platform is experiencing scaling
issues during peak traffic. The monolith architecture
is becoming difficult to scale and deploy.
// Decision
We will adopt a microservices architecture with
bounded contexts based on business capabilities.
Initial services: Catalog, Cart, Orders, Payments.
// Consequences
✅ Independent scaling & deployment
✅ Technology diversity per service
✅ Improved developer productivity
⚠️ Increased operational complexity
⚠️ Need for distributed tracing
⚠️ Eventual consistency challenges