What we build on - and why.
Every tool below is in production use across client systems. Stack choices reflect proven scalability, operational maturity, and the specific constraints of enterprise architecture - not recency or trend.
AI / Machine Learning
Foundation models, autonomous agents, retrieval systems, and vision pipelines built for production enterprise environments.
Stack decisions are always validated against your existing infrastructure. We do not recommend replacing proven systems without a documented migration case - most problems are solved with better architecture around existing tools.
How we make architectural decisions.
These principles apply regardless of the technology category. They are the criteria we use when evaluating every stack decision.
Decouple at the right boundary
Microservices only make sense when teams need to deploy independently. A modular monolith with clean internal interfaces is often a better starting point - and a far easier system to operate.
Prefer boring infrastructure
The newest database is rarely the right database. We select storage systems based on workload characteristics - read/write ratios, query patterns, consistency requirements - not technology trend cycles.
Observability is not optional
Every system we deploy ships with structured logging, distributed tracing, and alerting from day one. You cannot operate a production system you cannot see.
Security is architecture, not a checklist
Least-privilege IAM, encrypted secrets, zero-trust networking, and automated vulnerability scanning are integrated into every deployment pipeline - not bolted on after the fact.
Services that use this stack
Have a specific architectural question?
We are available to review your existing system, identify risk areas, and propose an architecture that fits your team's operational realities.