The stack we build on

Current versions, in production. Nothing trend-chasing, nothing end-of-life.

Every item below is a current stable release that our teams ship with in production today. Exact patch versions move as we upgrade; if you're migrating and need to know the precise build we run, ask us and we'll tell you.

Our working stack

Application, data and infrastructure we actually run.

Application

.NET 10 / C# 14 Angular 22 React 19 Flutter 3.47 Blazor Python 3.14 / Django 6 NestJS 12 / Node 22 TypeScript 5

Data, AI & messaging

PostgreSQL 18 SQL Server 2025 MongoDB 8 MySQL · Redis 8 Elasticsearch 9 RabbitMQ · Apache Kafka Azure OpenAI · GPT & Gemini pgvector · RAG pipelines

Infrastructure & QA

Azure AWS Docker Kubernetes 1.37 Terraform GitHub Actions Selenium 4 · Cypress · Playwright
The migration path

Where each legacy stack lands.

This is the map we use on every modernization. Same business logic, no rip-and-replace risk, and a target your engineers can actually support for the next decade.

What you have todayWhere we move it
VB6 / VB.NET desktop apps.NET 10 with clean APIs. The logic carries over, the user interface gets a modern look
WinForms / WPF / WCFAngular 22 or React 19 frontends, backed by REST / GraphQL APIs on .NET 10
Legacy ESB & message queuesApache Kafka or RabbitMQ with defined contracts and a service gateway
Older Java / C++ server codeRe-platformed onto .NET 10 or Node/NestJS, depending on what the logic needs
Old SQL Server / MySQL databasesPostgreSQL 18 or SQL Server 2025, as managed, monitored services
Monolithic servers on virtual machinesContainers on Kubernetes 1.37, deployed via Terraform and GitHub Actions
AI that earns its place

How we build AI into your systems.

We don't bolt a chatbot onto the side of your website and call it innovation. AI earns a spot when it removes a step someone does hundreds of times a week, like reading an invoice, matching a payment, triaging a support ticket, drafting a report.

On the platforms side, that means Azure OpenAI and current GPT and Gemini models, wired through Semantic Kernel and RAG pipelines over your own documents (via pgvector), with the whole thing monitored and versioned like any other production feature.

  • Document processing Extract data from invoices, contracts and PDFs into your systems automatically.
  • Data reconciliation AI flags mismatches in payments, orders and ledgers that a human would miss.
  • Support triage Route tickets, draft replies, summarize threads. Your agents stay in control.
  • Reporting Plain-English answers over the numbers your business already tracks.

Want to know if your stack is on this page?

Ask an engineer