For teams whose app depends on consistent records, customer permissions and integrations that fail in a predictable way. We build or extend the backend around those rules: clear module boundaries, typed API contracts and tests for the failure paths that matter. The data model, service boundaries and where each rule lives are the decisions that cost years to undo, so we make them on purpose and write down why. Your team receives the code and operating instructions with each increment.
The backend was built by whoever was available. Rules are scattered across screens and services, the same operation gives different results, and changing one place breaks another. New engineers need months to learn where things live.
An external system times out or returns incomplete data. You need an agreed outcome for the operation and a way to investigate it.
The backend must enforce who can read or change each record, whatever screen or API client sends the request.
Concurrent requests and repeated jobs behave differently from a single-user test. Before usage grows we check each critical path: indexes that match the filters, no N+1 queries, short transactions, jobs that are safe to run twice and connection pools budgeted across replicas.
Who owns each record, which operations are allowed and how other systems call them, defined in OpenAPI or a shared schema with generated types. Existing clients, versioning and compatibility are part of the plan.
Each business area gets its own module with a public interface, built as ports and adapters: the rule lives in a use case, the database and external APIs sit behind adapters, and import checks keep other modules out. Your engineers, and their coding agents, can change one area without reading the whole repository.
Idempotency keys for requests that get retried, an outbox where a database write and a message must both happen, and an agreed outcome for every external call that times out. Tests cover the permissions, invalid inputs and failure paths agreed in scope.
Architecture decision records next to the code: what we chose, what we rejected and what would justify a second look. Microservices, CQRS or event-driven messaging only where the problem calls for them, with the reason on file for the next maintainer.
The implementation, API documentation and tests, plus setup and deployment instructions. Handover covers the decisions a maintainer needs before changing the behavior.
Define the rules before the endpoints
Some backend decisions are cheap to change later. The data model, module boundaries and how services talk to each other are not, so we make those with the people who understand the operations and record the reasons. The scope names the first complete operation your team will be able to use and the dependencies that affect its date. You get a written status daily at first, then weekly once trust is there.
You want to build it stable enough so you can remove any piece without everything else falling apart. It’s way harder than it sounds.
Start with the user action, the data it changes and the external systems it touches. We group the rules into domain boundaries (bounded contexts, if your team speaks DDD) and mark the decisions that would be expensive to reverse. You get an agreed first slice and the open questions before we estimate.
We write the data model and API contracts with the teams that will consume them and record each architecture decision with its trade-offs. Direct calls or events, one service or several: the choice follows the problem, not the trend. You receive an estimate per module and a date for the first usable increment.
One vertical slice: the API adapter, the use case with its rule and the ports to storage and integrations. Tests cover the domain rule, the contract, the database mapping and the agreed failure paths, like a retried request or a timeout after the write. You get a deployable increment with code, tests and documentation for review.
We plan the rollout and how to roll it back, add trace IDs and alerts for the failures that matter, and document how to inspect them. Your team takes over, or we agree continued work.
How module boundaries, typed contracts and tests keep an agent's changes inside one part of your backend, with a Java/Spring example.
Read the briefWhat our own backend needed after go-live: 51 data-consistency fixes and 26 on APIs and integrations, from a 587-commit log.
Read the briefWhy data model and integration choices left open for months cost more than the ones made on purpose.
Read the articleBring one operation, the systems it depends on and the date you need it working. We will use that to scope the first usable part of the backend.
Mike KacprowiczBook a callChief Growth Officer
