Python for AI and backend services.
FastAPI services, AI/ML pipelines, voice agents, and data processing. When the work is computation-heavy, Python is where we go.
python.org ↗Python powers our AI and backend service work. We build FastAPI services for high-performance APIs that need async I/O and automatic OpenAPI documentation. For AI projects, Python gives us direct access to the machine learning ecosystem: model inference, embeddings, vector search, and audio processing. We've built voice AI agents with LiveKit, data processing pipelines with SQLAlchemy, and integration services that connect AI models to business workflows. Python handles the work that doesn't fit naturally into a JavaScript runtime.
Python owns the AI/ML ecosystem. When a project needs model inference, audio processing, or integration with AI APIs, Python has the most mature libraries and the most production-tested patterns. FastAPI gives us the performance of Go with the developer experience of Express. Type hints and Pydantic provide the same compile-time safety philosophy we apply to TypeScript. For backend services that are computation-heavy rather than I/O-heavy, Python is the pragmatic choice.
Python development for AI features and backends
When a project needs real intelligence (model inference, embeddings, vector search, audio processing, or a voice agent) Python is where we build it, because that is where the mature, production-tested libraries live. We use Python for the parts of a system that do not fit naturally into a JavaScript runtime: connecting to AI models, running data pipelines, and handling computation-heavy work that benefits from the scientific and machine-learning ecosystem. The pattern we most often ship is a Python service that owns the AI or data logic sitting behind a TypeScript front end, communicating over a clean API so each side works in the language it is best at. That separation keeps the AI work testable and swappable without touching the interface, and it lets us adopt new models and techniques as the field moves without rearchitecting the app around them. Python is the language we reach for when the work is intelligence rather than presentation.
FastAPI services in production
For the API layer of our Python work we build on FastAPI, because it gives us high-performance async I/O with type safety that mirrors how we already work in TypeScript. Type hints and Pydantic models validate requests at runtime and generate OpenAPI documentation automatically, so the contract a front end codes against stays accurate instead of rotting in a separate document. These services usually deploy as persistent containers on Railway, where a Python process can hold a model connection or a database pool open and run continuously rather than cold-starting on every request. That matters for AI workloads, where reloading a model per invocation would be slow and expensive. We keep the surface honest with the same discipline we apply elsewhere: typed boundaries, small testable modules, and integrations tucked behind clean interfaces, so a FastAPI backend built for one AI feature can grow into a system without becoming a tangle.
How we build a Python service
-
Scope the intelligence
We separate the AI or data work from the rest of the app and define exactly what the Python service owns: the model calls, the pipeline, the processing. Drawing that boundary first keeps the intelligence testable and swappable without touching the front end.
-
Typed FastAPI surface
We expose the service through FastAPI with type hints and Pydantic models, so requests are validated at runtime and the API documents itself. The front end codes against a contract that stays accurate because it is generated from the code, not maintained by hand.
-
Model and data integration
We wire in model inference, embeddings, vector search, or audio processing behind clean interfaces, so a model or provider can be swapped without rewriting the service. Connections that are expensive to open are pooled and reused rather than recreated per request.
-
Deploy as a persistent service
We deploy to a long-running container on Railway so the process can hold model and database connections open and respond fast. AI workloads in particular need to stay warm, which a persistent service delivers and a cold-starting function cannot.
Voice AI Agents
Real-time voice agents built with LiveKit for automated customer interactions in rental and service businesses.
FastAPI Backend Services
High-performance async APIs with automatic documentation, validation, and SQLAlchemy for database access.
AI/ML Pipelines
Model inference, text generation, embedding creation, and intelligent document processing services.
Systems we pair Python with
Railway
Where our FastAPI services and processing pipelines run. A persistent Python container can hold model connections open and run continuously, which suits AI workloads far better than a function that cold-starts each call.
AWS
For AI and data work we reach into AWS for durable object storage and managed model inference, while the Python compute stays on Railway. Each platform handles the part it does best.
Node.js
Most of our Python services sit behind a TypeScript front end and Node API. Python owns the intelligence, Node owns the app logic, and the two talk over a clean, typed HTTP boundary.
TypeScript
The language on the other side of most Python services. We apply the same type-safety philosophy in both, using Python type hints and Pydantic to mirror the typed contracts our TypeScript front ends expect.
