Human-centered design systems and product experiences
Core services
01UI/UX Design02Web Development03Software Development042D/3D Experiences05App Development06AI Agents & Automation07Website DesignTechnical services
Frontend Engineering
React DevelopmentNext.js DevelopmentWeb ApplicationsEnterprise DashboardsSaaS PlatformsBackend & Infrastructure
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Technology & Software
Backend Systems Engineering
The systems behindthe screen
We engineer the systems behind your product, from data models and background workers to event streams and caches, for platforms where load, consistency and failure handling decide whether the product holds up.
We design a backend for the day something fails, so a slow dependency or a duplicate message never becomes corrupted data.
Backends built for load, consistency and failure
- 01Event-driven services
- 02Data modeling
- 03Load-tested systems
Backend systems engineering covers the core of a platform, meaning the domain model, the database, background jobs, message queues, caches and the services that tie them together. It is the right choice when your product processes money, orders, sensor data or other workloads where volume is high, steps run asynchronously and a lost or repeated operation has real consequences.
We start with the data model and the failure cases, because both are expensive to change later. A modular monolith is our default, and we split out services only where scaling or team boundaries require it. Operations are idempotent, queues have dead-letter handling, and we load test against realistic traffic before launch so limits are known rather than discovered.
Talk to us about your projectCapabilities, End to End
What It Looks Like When It Ships
From Kickoff to Launch
We work through the business rules, data volumes and consistency needs with your team, then write down the entities, the events and the operations that must never be lost or repeated.
Service boundaries, storage, messaging and hosting are chosen and recorded as short decision records. Each one states the alternatives considered and the trade-off accepted, so later engineers understand the reasoning.
We deliver one complete flow at a time, from request to database to event, with tests and tracing included. Each slice is deployed to staging and reviewed before the next begins.
Before launch we run load and failure tests, including a database failover and a stalled queue, and publish the measured limits. The results guide capacity settings and the alerts we configure.
Built for Teams Like Yours
What buyers ask before they start
If yours is not here, ask us directly. The first conversation is free and useful whether or not we work together.
Book a callMost products are better served by a well-structured monolith for longer than people expect. It is simpler to deploy, test and reason about, and clear module boundaries keep the option to split later. Microservices make sense when parts of the system scale very differently, when separate teams need to release independently, or when one component needs a different technology. We recommend the smallest number of services that solves the actual problem.
We choose by workload and by who will maintain the code. Node.js with TypeScript suits products that share types and engineers with a React front end. Python suits data processing and anything close to machine learning. Go suits services with high concurrency and tight latency needs. If your team already has depth in one of them, that usually outweighs the technical differences.
Yes. We add tracing and query logging first, because guesses about performance are usually wrong. With measurements in hand we address the largest costs, which tend to be missing indexes, repeated queries in loops, work done inside a request that belongs in a queue, and absent caching. Each fix is verified against the baseline. We report what we found and what remains before proposing any larger restructuring.
We keep strongly related data in one database and one transaction wherever possible. When work must cross a service or queue boundary, we use the outbox pattern so a message is published only if the database change commits, and we make every consumer idempotent so a redelivered message has no extra effect. Reconciliation jobs compare systems on a schedule and raise an alert on any mismatch.
Both depend on the size of the domain, the integrations involved and the load you need to support, so we do not quote before understanding them. After the free scoping consultation we prepare a proposal that states what will be built, the milestones, the assumptions and a clear timeline. Work starts only once you have approved that document, and changes to scope are discussed openly as they arise.
