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Cloud & Backend Engineering

Backend systems built for reliability, scale and change.

We design and improve APIs, databases, queues, workers and cloud architecture with an emphasis on production behavior—not architecture diagrams for their own sake.

Discuss This ProblemHow We Work

When this service fits

Recognize the bottleneck?

Start with the work that is slow, fragile or difficult to change.

  • API latency or error rates are increasing as usage grows.
  • Background jobs fail silently or require manual restart.
  • Database access patterns no longer match the product's scale.
  • Cloud infrastructure is difficult to reason about or expensive to operate.
  • A monolithic request flow needs asynchronous processing.
  • Teams need better observability, deployment confidence or failure isolation.

What we build

Practical systems, ready for production.

API design

REST, GraphQL and service boundaries that support product evolution.

Queues & workers

Reliable asynchronous processing with retries, idempotency and backpressure.

Database engineering

Schema, query, caching and access-pattern improvements.

AWS architecture

Lambda, ECS/EKS, S3, SQS, SNS, RDS, DynamoDB and surrounding operational patterns.

Observability

Logging, metrics, tracing and actionable production signals.

CI/CD & delivery

Safer releases, environment consistency and infrastructure automation.

Reference architecture

A backend designed for production

The final architecture follows your workflow, scale, risk and existing systems. This is one way the pieces can fit together.

A backend designed for productionSYSTEM / FLOW
Example inputapi-event
  1. Clients / frontend
  2. API layer
  3. Database & cache accessQueued background jobs
  4. Asynchronous branch
    Queue → Worker A / Worker B
  5. Storage / external APIs
  6. Across the system
    Monitoring & observability
The API uses databases and caching for immediate work; a queue isolates background jobs with independent workers. Monitoring covers every layer.

Expected outcomes

What success should look like.

  • Fewer production bottlenecks and clearer failure modes.
  • Systems that can absorb growth without emergency rewrites.
  • Lower operational risk around background processing.
  • Cloud infrastructure that engineering teams can understand and change.
  • A technical foundation aligned to product needs rather than fashionable complexity.

Case studies

All case studies

Backend Engineering · Cloud Architecture · Document Automation

Scaling Enterprise Reporting with Asynchronous Processing

Problem: Large PDF and Excel exports outgrow the web request that starts them.

An asynchronous architecture for generating complex PDF and Excel reports through queues, background processing, and controlled document delivery.

  • AWS
  • Backend Engineering
  • PDF / Excel
  • Background Processing
Anonymized Engineering Experience

SaaS Engineering · Browser Extensions · Platform Development

Building Safentry: A Real-Time Risk Awareness Platform

Problem: Position size, leverage and exposure are easy to misjudge in the moments before an order is placed.

A browser extension and SaaS platform providing contextual pre-trade risk warnings, account management, and subscription infrastructure.

  • Chrome Extension
  • SaaS
  • Next.js
  • Integrations
Product Engineering

Let’s find the right path

Bring us the workflow, bottleneck or failure mode.

We will help you define the problem, the expected result and a sensible technical path before turning it into a project.

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