What we build with, and why
What gets used depends on your environment and on who looks after the application after handover.
Frontend
Tools
Angular with RxJS, HTML5, CSS3, Tailwind CSS, high-performance grids for data-heavy screens
Clerical work means the same screen a hundred times a day, and lists with five-figure result counts have to filter in the browser as fast as they did in the old fat client.
Backend
Tools
Node.js with TypeScript or ASP.NET, plus Python and Go for throughput and special tasks
If you already run a .NET landscape we build in ASP.NET, otherwise it stays with Node.js and TypeScript, and Python or Go only come in where throughput or a special task calls for them.
Databases and legacy data
Tools
Microsoft SQL Server, PostgreSQL, MongoDB where needed, plus DBF, DBC and Access
We connect the legacy formats read-only, so data can be reported on or migrated while the legacy system keeps running unchanged.
Mobile in the field
Tools
Ionic with Capacitor for Android and iOS, capture by EAN and QR scan
Out there what counts is that a delivery or a return is recorded at the moment of the scan; the predecessors of such apps still ran on portable MS-DOS mini PCs at our clients.
Data and reporting
Tools
HydraData as the data pump, Power BI, Microsoft Fabric with PySpark, InfluxDB as a time-series source
HydraData gets data out of systems that will not release it, and Power BI then sits on the resulting SQL database rather than on the intermediate step.
How we use AI
AI in our workshop and AI in your application are two decisions we take separately.
In our own craft since 2022
Before that we ran machine learning with TensorFlow for stochastic analysis. Today AI is part of the toolkit when reviewing code, running security checks, understanding unfamiliar processes and for part of the implementation.
No vibe coding
Every line a tool contributes passes through a developer before delivery. We are liable for the result exactly as if we had written it by hand. We tested that on a tool of our own that assesses security findings and checks the impact of changes. The review applies there too, where no client asks for it.
Which model gets to see what
Before a model goes near your code or your data, we ask you. For unproblematic material we use Claude or GPT on business plans where the provider may not use your content for training; for sensitive data an open-source model runs locally at our end, and in client environments we have integrated smaller models on site, which keeps the data in house and cuts ongoing API costs.
Rarely the answer inside your product
As soon as a transaction has to be evidenced, a posting or an anti-money-laundering check for instance, a result that reads differently on the next run is no use, and where a fixed rule does the same job we tell you so. The exception is reading reports that only arrive as PDF; there Azure AI Document Intelligence sits in the data flow, and because the raw output is kept, every figure can be traced back to its source.
We have used AI in our own work since 2022, and machine learning with TensorFlow for stochastic analysis before that: code review, security checks, process analysis and parts of the implementation. No vibe coding: whatever a tool writes, a human reviews, and we stand behind it like any other line. Which model is allowed near your code is agreed with you beforehand, over accounts where your content does not feed the providers’ training; for sensitive data we run open-source models locally instead.
First the conversation, then the analysis, then the quote.
Tell us what you run and what gets in the way. If it fits, we look at your system more closely. Whether that analysis runs as a separate service or feeds into a quote, we tell you before we start. After that you get an assessment: effort, timeframe, price range and the risks we see.
Discuss your project