Languages and development
Python: custom development, data and AI
When no boxed solution exists and the tool's core cannot be modified, business logic is written as a separate service.
We don't touch the tool's core: business logic moves into adjacent Python microservices linked via API and queues.
57,9%of developers actively code in Python — 4th place worldwide and +7 pp year over year
#1in the TIOBE index with a ~23.9% share — more than C++ and Java combined
600k+ready-made PyPI packages instead of custom-built solutions — "read before you write"
15k–20k RPSthroughput of an async FastAPI microservice on a single machine
Development
Development sells through the speed of changing a business process
For e-commerce, B2B, microservices and Python, what matters is not the stack but the locality of changes: a new channel, order, catalog or service must not break the whole system.
e-commerce
Omnichannel, accurate channel analytics, reservations, PIM and marketplaces.
B2B
Self-service portal, personal prices, stock, limits, documents and reorders.
Microservices/Python
Services around the core that can be tested, shipped and scaled independently.
ordercatalogserviceintegrationsales channel
Industry solutions
What you can build with Python
E-commerce and retail
Pricing and recommendation microservice alongside Pimcore/PIM
Assortment management and storefront personalization without modifying the PIM coreLearn more →Manufacturing and distribution
Data pipelines and demand forecasting on historical data
Procurement and inventory planning based on predictive analyticsLearn more →Finance and Insurance
ML scoring and transaction anomaly detection service
Risk scoring and anti-fraud without touching the core systemLearn more →Logistics and transportation
Route optimization services and telemetry integration
Delivery planning and real-time fleet controlLearn more →B2B services and SaaS
LLM assistants and RAG search over the internal knowledge base
Customer support and employee onboarding via an AI assistantLearn more →Media and content
Pipelines for processing and automatic content labeling
Moderation, tagging and preparing content for publicationLearn more →Healthcare and pharma
Medical data ETL and ML analytics for research
Collecting and analyzing anonymized data for decision-makingLearn more →
Capabilities
Python capabilities
Tool core (Pimcore / ERP / CRM) — left untouchedPython business-logic microservice (FastAPI, async)Message bus / queue (REST, gRPC, Kafka/RabbitMQ)ETL/ELT data pipeline (Airflow, pandas/Polars)Analytics layer and data warehouseAI/ML inference service (PyTorch, LLM, RAG)Clients and external systemsCI/CD, tests, observability (DORA/SRE practices)
Custom microservices alongside the core
Logic missing from the box lives in a separate service — Pimcore, ERP and CRM stay on an upgradable base, without forking or patching the core.
API and integration layer
FastAPI/async serves 15,000–20,000 requests/s on a single machine — external systems and the frontend get data with no bottlenecks.
Data engineering and ETL/ELT
Pipelines on Airflow, pandas and Polars gather data from disparate sources into a single layer for analytics and reporting.
ML and AI services
Recommendation, forecasting and classification models are deployed as services and embedded into existing business processes.
LLM and RAG wrapper layer
Python is the native environment for LangChain, vector databases and agents: internal assistants and document search reach production faster.
Automation and processing scripts
Routine exports, syncs and data checks move into code — less manual work and fewer operator errors.
Transferability and handover to teams
The widest developer market and mature standards: the project moves between teams and contractors without rewrites.
DORA/SRE engineering practices
Tests, CI/CD, typing and observability deliver predictable releases and fast recovery during incidents.
Approach
How we implement Python
Minimal core modification
We do not fork or patch the Python core. Python stays on a standard, upgradable version — business logic moves into separate microservices alongside, so platform updates do not break your customizations.
International Standards, Not Homegrown Hacks
Where a mature international solution exists, we use it instead of inventing our own protocol or platform. Before writing code, we study how the problem is already solved in the industry.
Transferability
The solution is loosely coupled and documented: it can be handed over between teams and contractors without rewriting. You are not tied to us.
AI compatibility
Python in the AI stack
Native stack for AI and ML
Python is the de facto standard for machine learning and data science: 41% of Python developers use it specifically for ML, and the PyTorch/scikit-learn/Transformers ecosystem covers the full cycle from prototype to production.
LLM agents and RAG out of the box
LangChain, LlamaIndex, Claude/OpenAI clients and vector databases are first-class Python libraries. Internal assistants and corporate data search are built on mature components, not in-house code.
AI-friendly codebase
Python leads in model training data and in coverage by AI coding assistants — code generation, review and refactoring are more accurate, speeding up delivery.
Serving models alongside business logic
FastAPI inference endpoints are deployed as separate microservices and embedded into existing processes without touching the ERP/CRM core.
Projects
Cases
Automated document package validation
- Python: implementation and integration.
Python platform for a hackathon with 10,000+ participants
- Python: implementation and integration.
Contacts
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