Repetition
CSV imports, reports, reconciliations. At night, error-prone, in one person's head.
Backend & AI - Python
Python for data, automation and AI.
Scripts, pipelines and services that take load off your core systems - from imports to RAG. No notebook that nobody ever puts into production.
Automation
Pipelines
AI
APIs
Data
When Python is the right lever
When the PHP applications should stay as they are - and there is work next to them that fits Python more honestly.
Knowledge & documents
The manuals and tickets are there - nobody finds the right passage. This is where AI and RAG start.
Data pipelines
Several sources, messy fields, no owner. We build jobs you can see.
Model integration
A pilot chat with no systems behind it. We orchestrate models behind your processes.
What we deliver in Python
From automation to the AI backend.
Automations
Imports, reconciliations, notifications. Logging and retries included.
Pipelines
Cleaning, features, delivery - as a job, not as a laptop script.
AI building blocks
RAG, classification, summarisation. See AI adoption.
Services
Often behind FastAPI. Connected to Bitrix24, Shopware, Symfony.
Approach: value before notebook
We start with the task and the data permissions. The model or the library is the second decision.
- Task, data class, success criterion
- Job or API - deliberately small
- Staging, evaluation, fallback
- Operations: logs, costs, owner
How Python reaches everyday work
No research project without acceptance.
Task
Which hour do we want to buy back?
Data
Sources, permissions, PII. What may the script see?
Pilot
One job or one endpoint in staging, against real examples.
Operations
Schedule, alerts, runbook. Maintenance optional.
What business teams rely on
Python as craft, not as a show.
Integration first
We write into your systems, not into a demo UI.
Visible costs
Model and compute costs are part of the plan.
No team replacement
People stay in charge of decisions - see the AI FAQ.
Frequently asked questions
Between a script and a platform.
Why not PHP?
Often it works. Python sits better with data, AI and mixed libraries. The domain logic can stay in Symfony.
Jupyter in production?
No. Notebooks for understanding, jobs for running.
Do we need data scientists?
For many automations and RAG pilots: no. For your own models: sometimes. We tell you when.
GDPR?
Data classes and storage locations before the pilot. Details on the AI page.
Packages, venv, Docker?
Yes - reproducible. No “works on my machine”.
How do we start?
One job or one RAG slice. A fixed scope after a call.
More stacks
Related technologies we often use alongside.
Python that runs every day?
Tell us which repetitive task or which knowledge you want to offload - we propose the smallest job.