Django and Flask web applications, automation scripts, data pipelines, ML model integrations, and REST APIs - Python used purposefully for the problems it solves best.
We use Python for the things it's actually best at: web applications with Django or Flask, automation scripts that replace hours of manual work, ETL pipelines, and production-ready integrations with ML models and AI APIs.
DynamicUnit's Python work spans internal tooling, customer-facing web portals, scheduled data pipelines feeding ERP and analytics platforms, and lightweight microservices. We write idiomatic, well-tested Python - with type hints, linting, and CI pipelines - not just scripts that work once and break when someone looks at them.
Python pairs naturally with our BigQuery and data warehousing practices for pipeline-heavy workloads. For enterprise Microsoft stack projects, our .NET development team is the better fit. Need Python services to talk to Dynamics 365? We've built that integration across multiple engagements. And our API development team delivers FastAPI and Django REST Framework endpoints alongside the application build.
ETL pipelines pulling data from TMS and WMS platforms, demand forecasting models, automated reporting scripts, and Python connectors to ERP systems.
Risk scoring models, automated compliance reporting, transaction anomaly detection, and data pipelines feeding data warehouses from banking platforms.
Product recommendation engines, pricing optimisation scripts, inventory sync pipelines, and automated data feeds between marketplaces and back-office systems.
Clinical data pipelines, ML model integration for diagnostic support, automated lab report processing, and research data transformation using Pandas and NumPy.
Wherever Python is the right tool for your problem - here's what we can build for you.
Full-stack web apps using Django's ORM, admin framework, authentication, and class-based views - production-ready with PostgreSQL and Redis.
High-performance REST APIs using FastAPI with async support and automatic OpenAPI docs, or Flask for lighter-weight microservices.
Scheduled scripts, RPA-style automation, file processing, and system integration tasks that replace manual repetitive work reliably.
Extract, transform, and load pipelines using Pandas, SQLAlchemy, and Celery - feeding data warehouses, ERPs, and reporting platforms on schedule.
Wrap trained models (scikit-learn, PyTorch, Hugging Face) in production inference APIs with input validation, versioning, and monitoring.
Automated analysis scripts using Pandas and NumPy that output structured reports, dashboards, or data exports for business stakeholders.
Python adapters and connectors for ERP systems, Dynamics 365, third-party APIs, and databases - with proper retry logic and error handling.
Dockerised Python services deployed to Azure Container Apps, AWS Lambda, or Azure Functions - with CI/CD pipelines and secrets management.
Python is easy to write and easy to write badly. We bring engineering discipline to Python projects so they're maintainable, testable, and don't quietly fail in production.
We use type hints, Pydantic models, and linting (Ruff/mypy) so the codebase is self-documenting and catches errors before runtime.
pytest suites with coverage targets, mocked external dependencies, and CI pipeline gating - not a manual "I tested it locally" sign-off.
We've built pipelines that process millions of records against ERP systems - with proper batching, transaction management, and failure recovery.
Python sits alongside our .NET and ERP practice - so integrations between Python services and Dynamics 365 or Azure are a natural fit, not a challenge.
Structured logging, application telemetry via Azure Application Insights or Datadog, and alerting configured before handover - not after an incident.
Code is documented, architecture decisions are recorded, and we walk your team through the codebase on handover - no mystery boxes.
We analyse your requirements and confirm Python is the right tool. If .NET is a better fit for your ecosystem, we'll tell you. You get a scope document with architecture decisions and a delivery timeline.
We build with type hints, pytest suites, and CI pipelines from sprint one. For API projects, OpenAPI docs are generated alongside the code. Demos at each milestone keep you in the loop.
We connect to your data sources, ERP systems, and third-party APIs. For ML projects, model performance is validated against real data before deployment. End-to-end testing ensures nothing breaks silently.
Containerised deployment to Azure or AWS, monitoring and alerting setup, code documentation, and a walkthrough session so your team can maintain and extend the solution independently.
Tell us what you're trying to automate, build, or integrate - we'll assess whether Python is the right tool and give you a clear delivery plan.
