01
Build fast, well-documented APIs with FastAPI's automatic OpenAPI schema generation and type validation.
Preparing experience
Core stack — backend, automation and AI systems
Python Development
Python, typically served through FastAPI, is part of WLC's core stack for backend systems, automation workflows, data pipelines and AI-enabled applications—including our production work on Claude and OpenAI.
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Capabilities included
What this type of engagement typically covers.
- Python & FastAPI
- AI/LLM integrations
- Automation
- Data workflows
Why WLC
Built for buyers who need clarity, speed and accountability.
Three principles that shape how we scope, build and deliver every engagement.
02
Power AI applications—LLM integrations, RAG pipelines, agent orchestration—on the language with the deepest AI/ML ecosystem support.
03
Automate manual operational workflows—data processing, reporting, integrations—that currently consume hours of staff time.
04
Build data pipelines and ETL processes with mature, well-tested libraries rather than ad-hoc scripts.
05
Use async FastAPI endpoints for I/O-heavy workloads like calling external LLM APIs without blocking the server.
06
Maintain a codebase that's approachable for both backend engineers and data/ML specialists to collaborate on.
Delivery model
A clean path from idea to launch.
Discovery
Architecture
Sprint build
QA
Launch support
Questions buyers ask
Common questions before starting a project.
Yes—Python with FastAPI is one of our six core technologies, and it's the backend of choice for our AI application work on Claude and OpenAI.
FastAPI gives us async support, automatic request validation and OpenAPI documentation out of the box, which fits API-first and AI-integration workloads better than Django's more opinionated full-stack approach for our use cases.
Yes—Python is our default language for LLM integrations, RAG pipelines and agent orchestration, given its ecosystem of AI/ML libraries and SDKs for both Claude and OpenAI.
Yes, with the right architecture—async FastAPI, appropriate worker/process configuration, caching and horizontal scaling handle production traffic reliably for both standard APIs and AI-serving workloads.
Yes—we start with a technical audit of architecture, dependencies and test coverage regardless of which Python framework it's built on, then propose a prioritised roadmap.
Related pages
Continue exploring WLC capabilities.
Ready to move
Tell us what you are building — we will map a practical route.
Share the outcome, timeline and constraints you are working with. We will respond with useful next questions and a clear way to start.