Bespoke software,
built to last.
Greenfield applications, internal tools, integrations, and the API and data layers underneath them. Typed languages by default. Tests on the behaviors that actually break. A deployable artifact within weeks, and a codebase your team can still read a year later.
Why custom software degrades.
Most custom software gets built once, under deadline pressure. Tests are skipped, observability is an afterthought, deployment is manual, configuration lives in tribal knowledge. The original developers leave or rotate off. Six months later nobody can confidently change anything, the internal team that inherited it is spending most of its time on workarounds, and the rewrite is already being discussed.
The way through is to build custom software with the same rigor as product development. Typed code, tested behaviors, structured logs from the start, a deployment that works on every commit. The handoff includes the code, the tests, the runbooks, and enough context for the next team to keep it running once we are gone.
Apollo builds custom software like we will have to maintain it. TypeScript or similarly typed languages by default, tests on the behaviors that matter, CI/CD wired up before the first feature ships, observability in place from day one. Your team inherits something they can read, extend, and operate without a permanent dependency on us.
The handoff. The consulting team finishes, the internal team inherits, nobody documented the deployment story or the environment config. Three weeks later something breaks at 11pm and there is no one to call.
Observability. Custom apps without structured logs or metrics become black boxes. By the time anyone notices a problem in production, the trail to the cause has already gone cold.
Custom software in regulated industries needs the same audit story as everything else. Access logs, change records, evidence of who did what when. These belong in the system from the start; retrofitting them later is always more expensive than it looks.
Manual workarounds. When custom software is hard to change, departments build spreadsheets and side processes around it. The cost shows up in onboarding time, in reporting that does not reconcile, and in the vendor calls nobody wanted to make.
Five shapes of custom work.
Most custom-software engagements land in one of these patterns. Each has a different architectural shape, a different testing posture, and a different operational profile once the internal team takes it over.
Greenfield Applications
Full-stack web applications built from zero. Architecture chosen for the team that will run it, not the trend on the conference circuit. Auth, multi-tenancy, observability, and a deployment pipeline working before the first feature ships, so the first release does not turn into the first emergency.
Internal Tools & Admin Systems
Back-office applications, ops dashboards, admin panels, batch-job consoles. The workhorse software your team uses every day. Built well enough that new hires can be productive on it in a week, and that you stop apologizing for it in demos.
Integrations & Middleware
The software that connects systems that were never built to talk to each other. Event-driven, retry-aware, idempotent, with structured logs for every message and a replay path for when something inevitably goes wrong. Designed to sit alongside legacy systems we cannot rip out, not against them.
APIs & Data Layers
Backend services with proper API design, typed schemas, versioning that does not break clients, and a database layer that does not turn into a shared dumping ground over time. REST, GraphQL, or gRPC, picked for the consumers, not for fashion.
Web & Mobile Clients
Customer-facing applications built on top of the APIs above. Component libraries that the team can keep extending, accessibility that is not an afterthought, state management that survives the second feature request, and performance budgets enforced in CI. We default to web with responsive design and progressive enhancement, and reach for React Native or native code when the use case actually requires it.
A custom application, end to end.
Simplified, and never the whole picture. Real systems coexist with legacy databases, vendor platforms, and SSO that predates the project. The shape below is what we aim for over time, with the Core Domain Services as the centerpiece and the rest as platform plumbing chosen so those services stay readable, testable, and replaceable.
Code that ships and keeps shipping.
An API handler, the way we'd write it
Below is a simplified version of an API handler we'd ship. Typed input validation at the boundary, a transactional write, structured logs on both the request and the result, explicit error handling for the cases that matter. Nothing exotic.
The shape is what you'd expect from a team that has been on call for software it wrote. The point is that this rigor is the default, not the exception.
import { db, log, metrics } from "@apollo/platform" import { z } from "zod" // Typed at the boundary. Anything that gets past this is well-shaped. const CreateOrder = z.object({ customerId: z.string().uuid(), items: z.array(z.object({ sku: z.string(), quantity: z.number().int().positive(), })).min(1), shippingAddressId: z.string().uuid(), }); export async function createOrder( input: unknown, ctx: RequestContext, ) { const parsed = CreateOrder.safeParse(input); if (!parsed.success) { log.warn({ event: "order.create.invalid", errors: parsed.error.issues, ctx, }); throw new ValidationError(parsed.error); } const { customerId, items, shippingAddressId } = parsed.data; // One transaction. Either the order and its line items both land, or neither does. const order = await db.transaction(async (tx) => { const created = await tx.order.create({ customerId, shippingAddressId, status: "pending", }); await tx.orderItem.bulkInsert( items.map((item) => ({ orderId: created.id, ...item })), ); return created; }); log.info({event: "order.create.success", orderId: order.id, customerId, ctx,}); metrics.increment("orders.created", { customerId }); return order; }
Four phases. Production from week one.
Apollo's standard methodology, applied to the specific failure modes of custom-software programs. Each phase produces working software. The CI/CD pipeline is running before the first feature ships, and the internal team that will own the system is in the room from discovery onward.
Domain. Constraints. Architecture sketch.
The business problem, the constraints (compliance, integrations, performance, team capacity), the system boundaries, and an honest read on the existing environment we have to live with. You leave with a written proposal that names the trade-offs and the risk profile of each one.
API contracts. Data model. System boundaries.
Domain model agreed. API contracts written before either side starts coding against them. Data model reviewed for the queries it will actually serve. The CI/CD pipeline, deployment target, and observability story decided before sprint one — not negotiated in the middle of it.
Typed code. Tested behaviors. Deployable.
Two-week iterations. Working software every time. Each feature lands with its tests, its docs, and a deploy that already went through staging. Your team sees the codebase grow in a way they can follow, and gets pulled in early on the parts they will own.
Runbooks. Observability. Knowledge transfer.
Documentation written for the people who will run this after we leave. Observability dashboards walked through. On-call runbooks for the failure modes we already know about. Pair-programming sessions with your team where it helps, and a defined point at which we step back.
The shortlist we work from.
What we deliver on, day in and day out. We pick for the domain, the team that will operate it, and the existing environment it has to live inside. We'll write the reasoning into any proposal.
Languages & frameworks
Data & storage
Infrastructure & ops
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Tell us what you need built.
A paragraph is enough. The application or system in scope, what's already there, the team that will own it after we leave, and any compliance constraints. We'll reply within one business day — either with a 30-minute call, or with an honest "this is not the right fit; here's who you should call instead."