Python Development Agency — APIs, AI & Data

Python excels for AI and ML integrations, data processing pipelines, scientific computing, and rapid API development. Its ecosystem (NumPy, Pandas, LangChain, Scikit-learn) is unmatched for data-heavy work. For pure web APIs without data needs, Node.js or Go may be faster.

We default to FastAPI for new APIs — it is async-native, fast, and generates OpenAPI docs automatically. Django is our choice when you need its built-in admin, ORM, or are integrating with a Django REST Framework ecosystem.

FastAPI is built on asyncio and ASGI (via Uvicorn/Gunicorn). We write async route handlers for I/O-bound operations, offload CPU-bound tasks to Celery workers, and use async SQLAlchemy for non-blocking database access.

We use Pydantic v2 for request/response validation — it integrates natively with FastAPI and provides TypeScript-like model definition with runtime enforcement. This eliminates entire classes of data shape bugs.

Yes. We design and implement ETL pipelines using Pandas, Polars, or custom async pipelines. For orchestration we use Prefect or Airflow depending on scale. We have built pipelines processing millions of records per day.

We integrate OpenAI and Anthropic APIs, build LangChain RAG pipelines, serve custom ML models with FastAPI, and implement vector search with Pinecone or pgvector. Kultrix handles the full AI integration stack from prompt engineering to production monitoring.

We use pytest with fixtures and factory_boy for test data. We test at three levels: unit tests for pure functions, integration tests for database and external service interactions, and end-to-end tests for critical API flows. We target 80%+ coverage on business logic.

We containerize with Docker (multi-stage builds for small images), deploy on Railway, AWS ECS, or Fly.io, run Uvicorn behind an Nginx reverse proxy, and configure Gunicorn workers for production throughput.

Yes. Common improvements include adding type hints, converting to async, upgrading to Pydantic v2, adding comprehensive tests, fixing N+1 queries, and containerizing for consistent deployments. Kultrix starts with a code audit to prioritize the highest-impact work.

Yes. We build production web scrapers with Playwright and Scrapy, handle JavaScript-rendered pages, implement polite crawling with rate limiting and retries, and store structured data in PostgreSQL or cloud object storage.

We use Poetry or uv for dependency management — both provide lockfiles, virtual environment isolation, and reproducible builds. We pin dependencies in production to avoid unexpected breaking changes from upstream packages.

A FastAPI backend with auth and database takes 4–8 weeks. A full data platform with pipelines and dashboards takes 2–4 months. Kultrix scopes Python projects carefully to match your budget and delivery timeline.