Technologies · Python
Python development for back ends, data and AI
We build Django and FastAPI applications, data pipelines, automation scripts and AI features in Python, with tests, type hints and documentation your own team can work from.
Python is a general-purpose programming language known for readable code and a very large library ecosystem. It is the default language for data analysis, machine learning and AI work, and it is a proven choice for web back ends through frameworks such as Django and FastAPI. Meritbyte Technologies, a Nepal-based software and AI development company, builds Python applications for businesses in Nepal and, remotely, in the USA, Australia, Canada and the UK.
What do businesses use Python for?
Python earns its place in four kinds of work:
- Web back ends and internal tools. Django ships with an ORM, authentication, permissions and an admin interface, so a back-office tool for staff can be usable in weeks rather than months.
- APIs. FastAPI builds fast, typed APIs and generates interactive documentation from the code itself.
- Data. Pipelines that pull from spreadsheets, databases and APIs, clean the data with pandas or Polars, and feed dashboards and reports.
- AI and automation. Document search, assistants, classification and extraction built on large language models, plus scripts that take over repetitive office work.
Django or FastAPI?
Both are mature. The choice depends on what you are building:
| Choose | When |
|---|---|
| Django | A full application with users, permissions and an admin panel for staff |
| FastAPI | A focused API or service, often feeding a separate React or mobile front end |
| Plain Python scripts or jobs | Scheduled automation, data syncs and reports with no web interface |
When a project needs an API next to a JavaScript front end with lots of real-time features, Node.js may be the simpler choice. We decide per component and explain the reasoning in the scope.
Python for AI features
Most useful business AI is not a chatbot bolted to a homepage. It is search across your own documents, drafting replies for staff to check, or pulling fields out of invoices and forms. Python is where the tooling for this lives: official SDKs for the major model providers, embedding and vector search libraries, and evaluation tools.
Our approach, set out in our AI development service:
- Start with the task and a test set. Fifty real questions with known good answers, agreed before any build.
- Retrieve before generating. Retrieval-augmented generation grounds answers in your own content; our guide to RAG for business explains how.
- Measure, then ship. Accuracy against the test set is reported at every demo, and the assistant says "I don't know" rather than guessing.
- Keep data where it should be. Models hosted through Amazon Bedrock, Azure OpenAI or Google Vertex AI when data residency or privacy rules require it.
If you are weighing an assistant for your website, read AI chatbots for business websites first; sometimes a good search box is the cheaper answer.
How we write Python that lasts
- Type hints and checks with mypy or Pyright, so the next developer knows what each function expects.
- pytest for unit and integration tests, run in the pipeline on every change.
- Dependencies pinned and scanned for known vulnerabilities.
- Background jobs in Celery or a managed queue, never inside a web request.
- PostgreSQL as the default database, with migrations under version control.
- Containers for deployment, so development, staging and production run the same thing.
How a Python project runs with us
- Free scoping call; a written scope, timeline and number follow.
- Fixed-price first milestone, usually the data model, one complete workflow and, for AI work, the evaluation set and a first accuracy score.
- Two-week build blocks with a demo at the end of each; stop after any block.
- Handover with documentation and the repository in your organisation, or a support retainer.
Need Python engineers inside your own team? See hire developers. For budgets, our custom software development cost guide explains what drives the number.
Frequently asked questions
What is Python used for in business?
Python is used for web back ends and internal tools (often with Django), APIs (often with FastAPI), data pipelines and reporting, automation of repetitive tasks, and AI features such as document search, extraction and assistants. Its large library ecosystem means most common problems already have well-tested building blocks.
Is Django still a good choice?
Yes. Django is mature, well documented and actively maintained, and its built-in admin, authentication and permissions save significant time on internal tools and content-heavy applications. For a small, focused API without an admin, FastAPI is often lighter. Both are sound choices.
Can you build an AI assistant that uses our own documents?
Yes. We build retrieval-augmented generation (RAG) systems in Python that search your documents and answer from them, citing sources. Before building, we agree a test set of real questions with correct answers, and report accuracy against it at every demo so you can judge it on evidence.
Is Python fast enough for a web application?
For most business applications, yes. Response times are usually limited by database queries and external APIs rather than the language. Where raw speed matters, the heavy part can be moved to a background job, cached, or written as a separate service in a faster language.
How do I hire Python developers from Meritbyte?
For a defined project, we scope it and fix the price of the first milestone. To add capacity to your own team, our developers can join under the Embedded model and work in your repositories and standups. In both cases the code is in your organisation from the first commit.