Technology
Python development.
The language AI, data and automation already speak.
MeetOnChai Studio uses Python where it is clearly the best tool: AI and LLM features, data processing, automation scripts and APIs that sit next to that work. For most product backends we use Node.js, and we pick Python when the problem lives in its ecosystem.
Why we use it
Why Python, and when not.
Nearly every serious AI, machine learning and data library is Python first. Embedding models, document parsers, PDF and OCR tools, pandas, scikit-learn, and the reference SDKs for OpenAI and Anthropic all land in Python before anywhere else. When a feature depends on that ecosystem, writing it in Python means fewer workarounds and fewer surprises.
Python is also hard to beat for automation. Scraping a supplier portal, cleaning a messy spreadsheet export, reconciling payments every night or generating reports: these are a few dozen readable lines in Python. With FastAPI and Pydantic, the same code can be exposed as a typed, documented API that your web or mobile app calls.
When it's the wrong call: If you need a standard product backend with auth, CRUD and payments, and your frontend is React, Node.js with TypeScript usually means one language and fewer moving parts. Python is also not our pick for real-time features with many open connections, where Node.js or Go fit better. We do not add Python to a stack just to say we use AI.
What we build
What we build with Python.
- 01
AI and LLM services
Retrieval, summarisation, classification and extraction services that your app calls over a simple API.
- 02
Document processing
Pulling structured data out of PDFs, invoices, forms and scanned documents, with OCR where needed.
- 03
Data pipelines
Scheduled jobs that collect, clean and load data from APIs, spreadsheets and databases into one place.
- 04
Business automation
Scripts that replace repetitive manual work: report generation, reconciliation, notifications and syncs.
- 05
FastAPI backends
Typed, auto-documented APIs for products where the core logic is data or ML heavy.
- 06
Scrapers and integrations
Respectful, maintainable data collection and connections to systems that only offer exports or old APIs.
The stack around it
What it ships with.
- Language and tooling
- Python 3uvRuffPydantic
- APIs
- FastAPIDjango when an admin is needed
- AI and data
- OpenAI SDKLangChainpandaspgvector
- Running it
- DockerAWSCelery or scheduled jobsPostgreSQL
Honest comparison
Python vs the alternatives.
vs Node.js
Our default for product APIs and real-time features. Python wins when the work depends on AI, ML or data libraries that are Python first.
vs No-code automation (Zapier, Make, n8n)
Great for simple glue between apps, and we will recommend it when it is enough. Once logic, volume or cost grows, a small Python service is cheaper and easier to debug.
Services
Services that fit.
- AI Integration & AutomationChatbots, AI features and automations added to the product you already have.
- Web App DevelopmentNext.js web apps and SaaS products that hold up under real traffic.
- DevOps & DeploymentCI/CD, hosting, monitoring and rollbacks, so launch day is boring.
- Maintenance & SupportBug fixes, updates and small improvements after launch, on a plan that fits.
FAQ
Python questions.
Do we need Python to add AI to our app?
Not always. Calling a hosted model like OpenAI or Claude works perfectly well from Node.js. Python earns its place when you need document parsing, custom embeddings, data science libraries or heavier processing that is Python first.
Can a Python service work alongside our existing Node.js or PHP app?
Yes. We usually run it as a separate small service with a clean API, so your main app calls it like any other integration. Your existing system does not need to change languages.
FastAPI or Django?
FastAPI for lean APIs and AI services, where typed request models and automatic docs matter. Django when you need a full admin panel, auth and ORM out of the box and the app is mostly CRUD.
Can you automate our spreadsheet and reporting work?
Very likely. If a person exports data, cleans it and pastes it somewhere every week, that is a good candidate. We start by watching how it is done today, then automate the boring parts and keep a human check where mistakes would be costly.
How do you deploy Python services?
In Docker containers on AWS or a similar host, with logs, health checks and scheduled jobs set up properly. Dependencies are pinned so the service builds the same way next year as it does today.
Every project starts with a conversation
Got an idea? Let's talk over chai.
A free 15-minute call. No pitch deck, no obligation. You leave with an honest read on what to build first.
- We reply within 24 hours, usually much sooner.
- NDA signed before you share the details.
- Based in Kathmandu, working with Nepal, India and worldwide.