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
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.
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.
Python is where the AI-heavy work happens. That is only an advantage if it stays there, so the boundary matters more than either language does.
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.
A custom checkout and payment platform for Shopify merchants: gateway integration, Apple Pay and Google Pay, multi-store installs, disputes and settlement reporting.
9 stalled pull requests and a 3-week bug backlog cleared; 3 held features then shipped.
A Shopify app that inverts order entry for live sellers: scan the product, and the draft order builds itself.
30+ SKUs scanned into one Shopify draft order; the old version choked on large ones.
A 60-second skin scan on a pharmacy shelf that resolves into one of more than a thousand personalised formulas, handed to the customer the same visit.
1,000+ skincare formulas resolved from a 60-second phone scan in 55+ San Pablo pharmacies, before the customer leaves the store.
A senior team that owns Python workstreams alongside yours: async-native, type-strict and observability-minded, built for real AI workloads.
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.