Why we choose what we choose, not a logo wall
Every technology decision is a trade-off made on purpose. Here's the reasoning, category by category, not a list of names to look credible.
Read this ifyour team will be the one maintaining this long after we've gone.
What we build with, and why
TypeScript Everywhere It Can Be
A shared type system across frontend, backend, and infrastructure code catches a class of bugs before they reach production, and makes a codebase easier for the next engineer to navigate.
Python for Data & AI
The ecosystem for data pipelines, model orchestration, and ML tooling is deepest in Python: using anything else there means reinventing tooling that already exists and is well-maintained.
React & Next.js for Product Surfaces
Server-rendered React gives fast, SEO-capable interfaces without sacrificing the interactivity modern products need: one framework covers marketing sites, dashboards, and portals alike.
Boring Where Boring Is Correct
A new framework gets adopted when it solves a real problem in front of us, not because it's new. Every dependency we add is a dependency we're responsible for maintaining.
How we reason about model choice
Model-Agnostic by Default
Foundation models change quickly; the systems we build route around a specific provider so a model upgrade or provider switch doesn't mean a rewrite.
Retrieval Before Fine-Tuning
Most business problems are better served by giving a model the right context at query time than by retraining it, cheaper, faster to iterate, and easier to keep current.
Evaluation Is Part of the Build
A model integration ships with an evaluation set built from realistic inputs, not a spot-check before launch, the same standard we hold any production code to.
Open and Closed Models, Chosen Per Task
Some workloads are better served by a smaller, self-hosted model for cost or latency reasons; others need a frontier model's reasoning. We choose per workload, not by default preference.
Where the data actually lives
PostgreSQL as the Default System of Record
A mature, well-understood relational database is the right foundation for most transactional systems: reached for first, not replaced by something more novel without a specific reason.
Purpose-Built Stores Where They Earn Their Keep
Redis for cache and ephemeral state, vector databases for retrieval, message queues for asynchronous work: each addition solves a specific access pattern the default store handles poorly.
Schema and Migrations as Code
Data structure changes are reviewed, versioned, and reversible, the same discipline applied to application code applies to how the data underneath it evolves.
How systems get deployed and run
Containers as the Deployment Unit
Packaging an application the same way for every environment (local, staging, production) removes an entire class of ‘works on my machine’ failures.
Infrastructure as Code, Reviewed Like Code
Environments are defined in version-controlled configuration, not clicked together in a console, so an infrastructure change goes through the same review as an application change.
Cloud-Provider Fit Over Default Preference
AWS, Google Cloud, or another provider: the choice follows your existing footprint, compliance needs, and cost profile, not a standing preference on our side.
Built in, not bolted on
Secure by Design
Identity, access control, and encryption are part of the architecture from day one, not a hardening pass before launch.
Access Control
Least-privilege access and audit logging on every system we build, so who touched what is never a mystery.
Encryption Everywhere
Data encrypted in transit and at rest as a default, not an optional configuration.
Responsible Disclosure
A clear, monitored channel for reporting vulnerabilities, see our Security policy for details.
How change ships safely
CI/CD as Standard
Every change ships through the same automated pipeline (build, test, deploy) not manual copy to production.
Staged Rollouts
Changes move through environments deliberately, with the ability to catch a problem before it reaches every user.
Observability from Day One
Logging, monitoring, and alerting are part of the initial build, not a project added after the first incident.
Rollback Ready
Every deployment is designed to be reversible: a bad release is a delay, not a crisis.