Artificial intelligence & ML
Frontier commercial models where capability matters, open-weight models where control and cost matter, and classical machine learning where it is simply the right tool.
The most advanced tools,
chosen deliberately
We work at the frontier, but we are not led by it. Every technology below earns its place against the same test: does it make the system more capable, more maintainable and more secure over its whole life — not just at launch?
Mobile
Native where the experience demands it, cross-platform where speed of delivery wins. Both routes ship through automated pipelines with the same quality gates.
Cloud & infrastructure
Provider-appropriate rather than provider-loyal. Infrastructure is defined in code, reviewed like application code and reproducible from an empty account.
Data
Modern, testable data stacks — with vector and search infrastructure treated as first-class citizens alongside the relational and analytical layers.
Application engineering
Typed languages, tested boundaries and framework choices that a new engineer can read in a week rather than reverse-engineer over a quarter.
Delivery & security
Everything ships through the same path: automated tests, security scanning, policy checks, then a reviewed release. Manual deployment is treated as an incident.
Engineering standards
What we hold constant
Technology choices change with every project. These do not.
- Everything in version control — application code, infrastructure, prompts, evaluation sets and documentation
- Automated quality gates — no route to production that skips tests, review and scanning
- Observability from the first commit — structured logs, metrics and traces, not added after the first outage
- Reversibility — every release can be rolled back, every model version can be pinned
- Documented decisions — architecture decision records so future teams know why, not just what
- Exit-ready — you can take the whole system in-house at any point, and we will help you do it
How we evaluate a new technology
Does it remove real complexity?
Can we operate it at 3 a.m.?
Is the security model clear?
Can the client hire for it?
What is the exit cost?
If a technology fails more than one of these, it does not go into a client system — however interesting it is.
Want the detail behind any of this?
We are happy to walk your technical team through architecture, trade-offs and the reasoning behind our recommendations.