Backend systems that survive real traffic
Event-driven services, APIs and async workers in Go, Python and TypeScript, built with the failure cases handled instead of discovered later.
Talk to an engineerWhen teams call us
- The API works fine until traffic doubles, and nobody is sure which part gives out first.
- A queue backs up overnight and the same job runs twice, so a customer gets charged twice.
- A third-party integration times out and takes down a flow that had nothing to do with it.
- Adding a feature means touching six services, and each release is a small act of faith.
- A monolith needs splitting, and the last attempt made things slower and harder to debug.
What we build
Event-driven services
Producers and consumers over Kafka, RabbitMQ or SQS, with explicit contracts, ordering guarantees where they matter, and backpressure handling when a consumer cannot keep up.
APIs worth integrating against
REST and gRPC interfaces with versioning and a deprecation policy, so your clients and your other teams are not broken by your next release.
Async workers and pipelines
Background processing with retries, exponential backoff, dead-letter queues and idempotency keys, so a repeated message is harmless instead of expensive.
Data access that holds up
PostgreSQL, MySQL and MongoDB schemas with the indexes the query planner actually uses, plus Redis caching with an invalidation strategy rather than hopeful TTLs.
Boundaries that survive change
Hexagonal architecture and DDD where the domain justifies it, keeping business logic separate from the frameworks and vendors around it.
Typical projects
Split a monolith without a rewrite
Carve out the highest-risk domain into its own service, define the contract between old and new, and migrate traffic gradually behind a flag. No big-bang cutover.
Make a payment or order flow exactly-once
Introduce idempotency keys, a reconciliation job and a dead-letter path, so retries stop creating duplicate charges and every discrepancy is visible.
Rebuild an ingestion pipeline for volume
Replace synchronous processing with queued workers, add batching and backpressure, and instrument each stage so throughput problems have a location.
Take an internal API to public quality
Add versioning, authentication, rate limiting, pagination, consistent errors and documentation, so external partners can integrate without a support ticket.
What you get
- Running services in your infrastructure, in your repository, under your CI
- Architecture decisions written down, with the trade-offs and the rejected alternatives
- Tests at the levels that matter: unit for logic, integration for boundaries, contract for APIs
- Instrumentation from day one, not bolted on after the first incident
- A walkthrough with your engineers so ownership actually transfers
Questions we get asked
Which language will you use?
Go for latency-sensitive or high-concurrency services, Python where the ecosystem wins such as data and AI work, TypeScript when it keeps you close to an existing Node codebase. If your team already has a language and maintains it well, we use that. Introducing a new language your team cannot maintain is a liability, not a service.
Can you work inside our existing codebase?
Yes, and that is most of what we do. We work in your repository, follow your review process and ship through your pipeline. Greenfield is the easier case, not the common one.
How do you handle a system you have never seen?
We start with an architecture review: read the code, trace the critical paths, look at the incidents and the metrics, then produce a prioritised list of what breaks first. It is fixed price and it stands on its own, so you can stop there if you want.
Start a conversation
Tell us what you are building, or what is currently breaking. You will get a straight answer from an engineer, not a sales script.