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Start a projectServices / Software Development
We develop Python solutions for applications, intelligent systems, modernization projects, and content platforms.
Some products need Python close to the interface. We build these layers with Streamlit, Dash, or NiceGUI for data-driven interfaces, and use Django templates with HTMX for fast, server-rendered web UIs. We apply PyScript only when its download size and startup time fit the product’s needs.
Your backend should handle APIs, data work, integrations, and background jobs without slowing you down. We build Python backends with Django, Flask, and FastAPI for web apps, SaaS platforms, enterprise systems, and API-first products that need to scale cleanly.
AI features need clean data and solid application logic, not just a model. We build ML models and data pipelines with scikit-learn, PyTorch, TensorFlow, Pandas, and NumPy, and implement LLM features using Hugging Face Transformers, LangChain or LlamaIndex, and provider SDKs, served through FastAPI for production use.
Django works well when you need authentication, an admin interface, and a structured data layer out of the box. We use Django, Django REST Framework, and Django’s ORM to build maintainable applications with the APIs, data models, and backend features your workflows require.
Older stacks can slow you down when they no longer support how you need to build. We migrate applications from PHP, Java, or .NET to Python in stages, testing each stage against existing behavior to protect business logic, data, and integrations, and upgrade legacy Python apps to supported releases.
Content teams should be able to publish without waiting on developers for routine changes. We build Python CMS solutions on Wagtail or Django CMS, and provide maintenance, upgrades, troubleshooting, security updates, and performance tuning as your platform grows.
A well-planned Python solution changes what your application can handle and how easily your teams can work with it.
Use established Python frameworks, libraries, and development patterns to move new functionality from development into production more efficiently.
Process and transform sizable datasets with Pandas, NumPy, and PyArrow, and move to Polars, Dask, or PySpark when data outgrows a single machine's memory.
Integrate machine learning models, AI services, data pipelines, and application logic so intelligent features work within the wider software environment.
Clarify the business problem, users, existing systems, technical constraints, and performance requirements the application must address.
Choose the Python framework, application structure, data layer, APIs, integrations, and deployment approach based on the workload and future needs.
Prioritise core functionality and integrations first, proving the architecture early while keeping the application ready to scale.
Test functionality, performance, security, integrations, and deployment behaviour against the demands of the real environment.
Use production feedback, performance data, support insights, and changing requirements to guide optimisation and future development.
Tell us what your application needs to handle, where your current technology creates limitations, or what you want to build next. We’ll help define the right Python approach.
Plan your projectWe build new Python applications, extend existing software, and modernize older systems. Our work covers frontend and backend development, Django, AI and machine learning, migration, CMS development, and support. Many projects combine two or three of these, such as a Django backend with an AI feature, or a migration followed by ongoing support.
Yes. We can take over an existing Python project and continue development without requiring a complete rebuild. We first review the codebase, architecture, dependencies, documentation, development practices, and outstanding issues to understand the current state. From there, we establish priorities and continue feature development, fixes, optimization, or modernization within the existing environment.
We start by examining the codebase, application architecture, dependencies, data flows, APIs, integrations, and deployment setup to understand how the system works. We document the critical components and technical dependencies as we go, giving the development team a clearer foundation for maintenance, troubleshooting, and future development.
Yes. We work within established development environments instead of forcing a separate toolchain onto the project. We align with your coding standards, version control practices, testing frameworks, CI/CD pipelines, deployment processes, and collaboration tools so our Python development fits into the way your team already works.
We address security at every stage of Python development. We review dependencies, authentication and authorization, API endpoints, input validation, data handling, secrets management, and application configurations, while following secure development practices. We also fix the vulnerabilities that testing or ongoing maintenance uncovers.