// Technology
What we build with, and why
A list of logos doesn’t tell you much on its own. Here’s what each category of technology actually enables, and where we use it.
Frontend
The layer users actually interact with. We pick a frontend stack based on the product's complexity and performance needs, not by default.
React
Component-based interfaces for web applications
Next.js
Server rendering, routing, and performance optimization for React apps
Angular
Structured framework for larger enterprise frontends
Blazor
.NET-based interfaces for teams standardized on the Microsoft stack
TypeScript
Typed JavaScript for safer, more maintainable frontend code
Backend
The systems that handle business logic, data, and integrations behind the interface.
C# / .NET
Enterprise-grade backend services and APIs
ASP.NET Core
Web APIs and services on the .NET platform
Node.js
JavaScript-based backend services, often paired with a React or Next.js frontend
Python
Backend services, data processing, and AI/ML workloads
Java
Backend services for existing Java-based enterprise environments
Mobile
Native and cross-platform tooling for Android and iOS applications.
React Native
Shared codebase across Android and iOS
Flutter
Cross-platform apps with a single, consistent UI toolkit
Android (Kotlin)
Native Android development
iOS (Swift)
Native iOS development
Cloud
Infrastructure platforms used for hosting, scaling, and managing applications.
Microsoft Azure
Cloud infrastructure, managed services, and Azure OpenAI
AWS
Cloud infrastructure, managed services, and Amazon Bedrock
Google Cloud
Cloud infrastructure for teams already standardized on GCP
AI
Tools used to build practical generative AI features: language models, orchestration, and retrieval over business data.
OpenAI
Language models for generative AI applications
Azure OpenAI
Language models deployed within an Azure environment
Amazon Bedrock
Managed access to foundation models within AWS
LangChain
Framework for building applications on top of language models
LangGraph
Building structured, multi-step AI agent workflows
Python
Primary language for AI and data workloads
Vector databases
Storing and retrieving data for RAG applications
Databases
Where application and business data actually lives.
SQL Server
Relational data for .NET-centric applications
PostgreSQL
Relational data for general-purpose application backends
MySQL
Relational data for web applications and CMS-driven sites
MongoDB
Document-based storage for flexible or evolving data models
Redis
In-memory caching and session storage for performance-sensitive systems
DevOps
How code gets built, tested, deployed, and kept running reliably.
Azure DevOps
CI/CD pipelines and work tracking for Azure-based projects
GitHub
Source control and collaboration
GitHub Actions
Automated build, test, and deployment workflows
Docker
Packaging applications consistently across environments
Kubernetes
Orchestrating containers at scale, when the workload actually needs it
Have a stack in mind already?
If you already know what you're standardized on, we'll work within it. If you're deciding, we'll help you weigh the tradeoffs directly.