Python where it does the heavy lifting.

Python when the workload is data-heavy, AI/ML, or needs the scientific ecosystem (NumPy, Pandas, PyTorch, LangChain). Node.js for high-concurrency API servers and real-time products. Most of our production AI systems use Python for the AI/ML layer and Node.js for the API layer — both in the same architecture.

FastAPI or Django REST for APIs, Celery or AWS Lambda for async workloads, PostgreSQL for persistence, containerized deployments with proper logging and monitoring. We treat Python as a production system — not a notebook. Rollback, alerting, and auto-scaling are part of every production Python deployment.

Both. New services on modern Python with FastAPI and modernization of existing Python codebases — framework upgrades, dependency updates, architecture refactoring, and test coverage improvement.

Yes — that's one of Python's strengths. LLM integration, model inference endpoints, RAG pipelines, and data pipelines can all be added to existing Python services through the six-gate governance pipeline.

The same pod maintains the service — dependency updates, security patches, Python version upgrades, performance work, and AI model updates. Python's AI ecosystem moves fast; we stay current.