Python AI systems built for production, not notebooks
We build production Python AI applications, LLM integrations, agentic pipelines, and FastAPI services that ship with the typing, testing, and observability that notebooks skip but production requires.
From prototype notebook to production Python service
The ecosystem is Python. The gap between a working demo and a production service is where most AI projects stall.
Python AI application development
Building production Python applications that integrate LLMs, agents, and AI pipelines, with the software engineering discipline that makes them maintainable beyond the first release.
LLM integration with Python
Integrating Claude, GPT-4, and open-source models into Python services using the Anthropic SDK, OpenAI SDK, and LiteLLM, with prompt versioning, retry logic, and structured output parsing.
Python agent frameworks
Building agentic systems in Python with LangGraph and CrewAI, the two frameworks with the deepest Python ecosystems and the strongest production track records.
FastAPI AI service architecture
Wrapping AI pipelines in production FastAPI services, async endpoints, background tasks, streaming responses, and the API design that makes AI capabilities accessible to the rest of your stack.
The richest AI ecosystem, if you engineer it properly
Python is the AI language
Every major AI framework, LangGraph, CrewAI, LlamaIndex, Transformers, PyTorch, has Python as its primary interface. Building AI systems in Python is choosing the path with the richest ecosystem and the most current tooling.
Production Python is a different skill
Python notebooks that demonstrate a concept and Python services that run under production load are not the same engineering problem. We build for the latter, async, typed, tested, and observable.
Deep Python AI experience
AppUnik has shipped Python AI systems in production across multiple client industries. We know the dependency management challenges, the async pitfalls, and the testing approaches that actually work for AI-integrated Python services.
Have a Python AI project that needs to leave the notebook?
Tell us what you have and what production looks like for you. We will map the gap and tell you what it takes to close it.
