Python development for APIs, ERPNext & AI backends

Python is the one language that runs across our product, ERP and AI work. FastAPI gives us async, type-checked APIs for consumer-scale products; the Frappe framework — the Python foundation of ERPNext — is how we deliver every ERP project; and the AI practice lives in Python by default, from retrieval pipelines to model serving. That overlap means the engineers who build your API can wire it to an LLM or an ERP without a hand-off to another vendor.

We work in modern Python (3.11 and later), typed with Pydantic and mypy, packaged with uv or Poetry, tested with pytest, and deployed as containers on AWS, Azure or your own infrastructure. Async is the default for I/O-bound services; CPU-bound work goes to worker pools or a dedicated service.

ResqRoute is an emergency ambulance platform: a FastAPI backend handles dispatch, live GPS tracking and traffic-aware routing through the Google Maps API, with a React dispatch dashboard and Flutter apps for paramedics and patients. Pilot deployments cut dispatch response time by 35 % and replaced 80 % of radio-based coordination.

Zomee, a hyperlocal delivery platform, runs an async FastAPI backend that handled 5,000 concurrent orders with under 200 ms API response, syncing order state to customer, delivery-partner and merchant apps in under 800 ms — holding a ten-minute delivery promise at 92 % SLA compliance.

Both were built by one team across backend and mobile, which is usually the fastest route from idea to store listing for a product startup.

ERPNext is a Python application, and serious customisation means Python: custom doctypes, server-side scripts, scheduled jobs, REST endpoints and standalone Frappe apps. We have delivered a full distribution ERP with GST-compliant invoicing and multi-warehouse stock for Prem Plumbings, Frappe HRMS alongside a Frappe CRM on one database for Land Group, and HRMS for a site-based workforce at Zara Interiors. Our own HR, payroll and accounting run on the same platform, extended with a retrieval-grounded assistant written in the same language.

The ERP practice has its own page at ERPNext implementation & Frappe development; this is the engineering behind it.

Our AI practice builds in Python end to end: document ingestion with Docling, embeddings and retrieval on Qdrant or Chroma, orchestration in LangGraph, and inference through vLLM, Ollama or hosted models from OpenAI, Anthropic, Azure and Bedrock. Every system ships as a FastAPI service with evaluation harnesses, tracing and guardrails — the same operational standard as any other backend we deliver.

Because the AI and backend teams share a language and a toolchain, adding an LLM feature to an existing Python product rarely needs a new service boundary or a new vendor.

When a product is dominated by CRUD, permissions and content — internal tools, B2B portals, marketplaces with heavy admin needs — Django with Django REST Framework is faster to build and easier to staff than a hand-rolled stack. We use it with PostgreSQL, Celery workers and Redis, and pair it with React or Next.js front ends from our web engineering team. Flask stays in the toolbox for small internal services and model-serving shims.

Python is our pick when the backend must sit next to data, machine learning or ERP logic, or when a small team needs to move quickly in a typed, readable codebase. For long-lived transaction systems in regulated industries we usually recommend Spring Boot; for TypeScript-first teams and I/O-heavy BFF layers, Node.js. The backend practice overview explains how we decide.