01 Python
Python web development services for data-heavy products
Tailwebs offers Python web development services: web applications, APIs and data systems, from the backend of a mobile app to scraping pipelines and AI features. We are a Bengaluru product studio, founded in 2015, with 200+ projects delivered. Python suits products where data processing, automation or machine learning sit close to the core of what the product does.
02 When it fits
When custom Python web development fits your project
Python fits when the product's value is in processing data, scraping or reporting.
Project traits
- Data processing, analysis or reporting is central to the product. For example, a platform whose value is in the data it collects, cleans and presents.
- You need scraping, document processing or scheduled data jobs. For example, pipelines that turn public documents into a searchable database.
- You plan AI or machine-learning features, now or later. For example, search, summaries or an assistant added to an existing product.
- You need a strong admin interface quickly. For example, the API and the admin panel behind a mobile app.
- You are replacing manual, spreadsheet-based work. For example, a web tool in place of a shared spreadsheet, as in tool digitalisation.
- Your data or research team already works in Python. For example, analysts whose scripts the product will run, so both sides work in one language.
04 How we build
Python software development: how we build backends
Four choices decide how a Python backend holds up: the framework, the tests on its business rules, where slow work runs, and how close it sits to AI features.
- 01
A framework chosen by project trait
A product that needs accounts and admin screens from day one suits a full framework with a built-in admin; an API-only service or an AI endpoint suits a lightweight API framework. Each is picked for the job it does.Our Python work uses Django, at Metropolitan Anesthesia Consultants and in the backend of Homa's IoT ecosystem.
- 02
Type hints and automated tests
Business rules are where a silent break costs most, so they are the first part a test suite has to cover.
- 03
Background jobs for slow work
Imports, scraping and long calculations belong in background jobs, so the app stays responsive while they run.
- 04
Python for AI features
Most AI and LLM tooling is written for Python first, so an AI feature can sit close to the rest of the backend. The approach, evaluation and running cost are on AI and LLM development.
- 05
A codebase you can maintain
The codebase is handed over with its documentation and tests.
Python backends we have built: Metropolitan Anesthesia's web app, which moved their whole manual process for contractor credentialing, document compliance and timesheets into one system and cut manual effort by 80%; and the backend and admin in the full IoT ecosystem for Homa's smart atta chakki, with device connectivity over MQTT.
Need the whole web product, not only its backend? See web app development.
Want an AI feature scoped as a project of its own? See AI development.
05 Data-source audit
Before Python web development starts: a data-source audit
Data-heavy Python projects fail on their data, so the sources are checked before the build is scoped.
Building a public data portal? The same check comes first there: see public data portals and dashboards.
- Inventory
- Every source the product will draw on, such as databases, spreadsheets, partner APIs, public websites and PDFs, with who owns it and how often it changes.
- Sample review
- Real records from each source, checked for gaps, duplicates, formats and the fields people actually need.
- Access and permission
- API keys, data-sharing agreements and, for public websites, the site's terms and robots rules, checked before any scraper is written. Where permission is unclear, your legal team decides.
- Refresh and failure rules
- How often each source is fetched, what happens when a source changes format or goes down, and who is alerted.
- Output: a data map
- A short written map of the sources, which sets the scope, cost and timeline of the build.
06 Upgrades
Upgrading an ageing Python application
Yes: we upgrade ageing Python applications, including ones still on Python 2.
Code audit first
A review of the code, its dependencies and its tests, with tests added around the most-used features where they are missing.
Python 2 to 3
Where the app still needs it, one module at a time.
One release at a time
The framework moves up one supported release at a time, with the tests run at each step.
Dead dependencies replaced
Abandoned packages are swapped for maintained ones.
Running an ageing Python app? Tell us what it runs on.
07 FAQ