Technology
Modern AI where it fits. Proven engineering underneath.
We use established application platforms where reliability matters and integrate modern AI models, retrieval systems and APIs where they provide a concrete advantage to the workflow.
Typical stack
Technologies we work with.
The exact stack varies by system. These are technologies from current and recent work, not a complete catalogue.
Applied AI
LLM APIsClaude APILocal LLMsRAGEmbeddingsSemantic searchQuantized models
Backend & application services
C#.NETASP.NET CoreWeb APIBackground jobsREST
Web & client applications
TypeScriptJavaScriptAngularHTML/CSS
Mobile
React NativeFlutterAndroidiOSOffline workflows
Data
SQL ServerMySQLMongoDBRelational modelingReporting
Integrations
Messaging APIsPush notificationsCloud storageAuthenticationWebhooks
Delivery & operations
GitCI/CDWindows ServerLinuxNginxIIS
Engineering principles
How technology decisions get made.
Fit the operating context
A field app with patchy connectivity needs different architecture from an internal office application on a controlled network.
Prefer proven reliability
Mature, well-understood technology is often the right foundation for systems expected to run for years.
Modernize with intent
Upgrade where it reduces risk, cost or friction—not simply because a newer framework exists.
AI when it is useful, not decorative. We are comfortable incorporating AI-assisted features where they materially improve a workflow, but we do not force an “AI” label onto systems that do not need one.