Python as part of your operational system – not as an isolated script.
Connecting Python Services – Custom and Seamless
Python is the most widely used programming language for data processing, machine learning, automation, and scientific computing. Companies utilize Python-based services to analyze data, run AI models, generate reports, connect external APIs, or execute complex calculation logic. For operationally complex companies, Python is relevant because many specialized functions – from forecast calculation to document processing to machine learning – exist as Python services that must be embedded in an operational system to realize their impact. We integrate Python-based services and scripts into custom enterprise software. No loose collection of scripts, no Jupyter Notebook as a production solution – but a tailored connection that precisely fits your processes and your system.
What We Connect
| Integration Options | |
|---|---|
| 🔄 | Embed Python microservices as backend services into your operating system |
| 📊 | Run data analyses, forecasting models, and machine learning pipelines productively within the system |
| 📄 | Document processing – parse PDFs, extract data, analyze texts – automated via Python services |
| ⚡ | Event-driven calculations – e.g., automatically update forecasts with a new data record, check data quality on import |
| 🔗 | Seamless connection to databases, APIs, AI frameworks, ETL pipelines, and other system components |
How Integration Works
We develop Python services as containerized microservices or serverless functions and integrate them into your operating system via REST APIs, message queues, or direct database connections. The integration is developed as a fixed component of your system architecture – no loose scripts, no manual execution. What this means in concrete terms:
| 🏗️ | Custom integration – built for your processes, not as a generic framework |
| 🔄 | Automatic data flow – Python services run in the background, triggered by events in the system |
| 🗄️ | A data foundation – results flow directly back into your central system |
| 🛡️ | Secure and GDPR-compliant – containerized, versioned, monitored, and documented |
Typical Use Case
A logistics company with 60 employees has a data analyst who calculates capacity forecasts in Python, analyzes tour data, and prepares monthly reports for management. The scripts run on his laptop, he manually pulls the data from the ERP via CSV export, and the results end up as PowerPoint slides in the management meeting. If the analyst is sick or on vacation, the analyses are missed. The forecasting models are good – but they exist as isolated Jupyter Notebooks, not as part of the operational system. Decisions based on these analyses are made without them. Through integration, the Python models become productive services within the operating system. The capacity forecast runs automatically – daily, based on current data from the ERP, without manual export. Results flow directly into the scheduling dashboard: Where are bottlenecks expected next week, where are there available capacities? Tour analyses run as a background service and provide optimization suggestions. New data automatically triggers an update of the models. Management sees forecasts and analyses in the central dashboard – not as a monthly retrospective, but as real-time control information. The analyst's knowledge is no longer stuck on his laptop – it is embedded in the system.
Part of Your Operating System
Python is the lingua franca of data processing and AI – powerful, flexible, and with a vast ecosystem of libraries. But as a loose collection of scripts on individual computers, this potential remains isolated: dependent on individuals, disconnected from the operational system, without automatic execution. Only as an integrated part of an operating system do Python services realize their full benefit – when models are running productively, results are available in real-time, and data processing occurs not alongside the company but within it. We develop AI-powered operating systems for operationally complex companies. Python-based services and AI models are a critical building block.