The stack we bet on

Tools get picked for what they'll cost you in three years, not for what's trending this one. Here's what we reach for by default, what each one is actually for, and where we'll tell you the boring option is the right one.

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Backend

Boring where it counts

C# and .NET Core are where we're most at home, especially inside legacy systems other firms won't touch. Decade-old enterprise services get carved into maintainable .NET Core one seam at a time, and new backends we build there tend to still be running, unmodified, years later.

Node.js and TypeScript cover most product APIs and backends built from scratch, since the same types run on the client and the server. Python shows up for data pipelines and the glue around AI systems, and Go gets the call when a service needs to be small, fast, and not much else.

C# / .NET Core
Node.js + TypeScript
Python
Go

Frontend

Built to load fast

React handles the product UIs and dashboards, the screens people spend all day in. Component reuse and a mature ecosystem mean less time reinventing form validation and more time on the parts of your product that are actually different.

Astro is what this site runs on, and it's our default for content and marketing sites: static by default, fast without effort, with just enough JavaScript where it's earned. TypeScript sits under both, because untyped JavaScript in production is a bet we'd rather not make.

React
Astro
TypeScript

AI & LLMs

Where it earns its keep

Claude and OpenAI's APIs cover most of the model layer, chosen per task for quality, latency, and cost rather than brand loyalty. Around them we build RAG pipelines that retrieve from your actual documents and cite what they used, so an answer can be checked instead of just trusted.

Every feature ships with an evaluation harness and guardrails before it reaches a real user, because a demo that works once tells you nothing. And we'll say it plainly: roughly a third of the AI projects that reach us ship better and cheaper as ordinary software. We'd rather tell you that on the call than bill you for finding out.

Claude & OpenAI APIs
RAG pipelines
Evaluation & guardrails

Mobile & desktop

Native feel, shared code

iOS and Android apps that feel native because the moments that matter, like scrolling, gestures, and offline states, get built for the platform rather than around a lowest common denominator. Windows and macOS builds follow the same rule: shared code where it saves time, native where it saves face.

Offline-first sync is close to a default requirement now, not an add-on. People use these apps on gym Wi-Fi, on the subway, and mid-flight, and the app needs to keep working and reconcile cleanly the moment a connection comes back.

iOS & Android
Windows & macOS
Offline-first sync

Cloud, data & DevOps

Whichever cloud you trust

AWS, Azure, and Google Cloud all get used, deployed on whichever one your business already runs on rather than whichever one we'd prefer. Docker and CI/CD pipelines mean deployments are routine events, not incidents, and Postgres is the default datastore until a specific workload proves it needs something else.

Big data pipelines get built for the volume you actually have, not the volume a vendor's sales deck imagines. A good chunk of this practice is quieter than migrations: monitoring that catches problems before customers do, and cost reviews that regularly find a bill can shrink instead of grow.

AWS
Azure
Google Cloud
Docker & CI/CD
PostgreSQL

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