LLM orchestration, RAG pipelines, data engineering, and ML inference. FastAPI-first, async-native, with observability built in from the start.
Where it fits in our builds
When a product has real ML in it, the Python service is where that lives: FastAPI in front, embedding and inference pipelines behind, deployed in containers next to the product API.
FastAPI
PyTorch
Docker
AWS
Supabase
GitHub
Our stance
Python is the lingua franca of AI/ML: every major LLM provider, vector DB, and embedding model ships best-in-class Python SDKs. FastAPI gives us TypeScript-like DX (type hints, OpenAPI auto-generation) without leaving Python, and the ecosystem of PyTorch, Hugging Face, and scientific computing is unmatched for ML work. Async support means Python no longer means slow: FastAPI on Uvicorn handles real production load. We reach for Python when the AI/ML work is heavy, and pair it with NestJS for the product API.
Capabilities
Async-native APIs with type hints and auto-generated OpenAPI. The front door for AI services.
LangChain and LlamaIndex where they earn their place, custom orchestration for production.
Embedding pipelines, vector stores, and retrieval orchestration with grounding signals.
PyTorch and Hugging Face Transformers served behind monitored, cost-aware endpoints.
Pandas, Polars, and DuckDB for transformation, enrichment, and analytical workloads.
Celery and RQ for distributed task queues, long-running jobs, and async workloads.
Infrastructure that holds up when traffic arrives
Tooling
Hard lines
Saying no to these is part of the service. Each one is a production incident we would rather not relive.
When there is no AI/ML work, NestJS does standard product APIs better, and our team is denser there.
FastAPI is async-native. Writing blocking code throws away the framework’s biggest advantage.
Notebooks are for exploration. Production code lives in modules with tests and types.
Pydantic plus mypy in CI is the standard. Untyped Python rots fast.
We write custom orchestration where it matters, not where a maintained library already solves it.
Content engines, enrichment pipelines, and ML-driven ad tooling.
Risk models, fraud pipelines, and document-intelligence workloads.
Clinical assistants, triage models, and medical-document workflows.
We built a minimum viable product to connect disparate advertising APIs into a single, intuitive user interface.
We built the technical foundation for a SaaS platform that automates complex legal document creation with usage-based pricing system
We engineered a voice-first AI companion with a therapeutic framework to validate a new model for mental health support.
Adjacent stacks
Need to extend your team with engineers who have shipped production Python for AI workloads? We embed senior FastAPI and ML engineers into your team: async-native, type-strict, and observability-minded from day one.
Production engineering
We pick tools because they ship, not because they trend. Every technology we list is one we have deployed to production, debugged under load, and would choose again for the right problem.
Straight answers on how we run Python in production, from frameworks to deploys.
Python for AI/ML and data work; NestJS for product APIs. We pick per workload, not per dogma.
No account managers. No discovery theatre. A direct conversation about your Python stack and what it takes to ship it to production.