Expertise
Python expertise: APIs, data processing, automation and AI
Python is the reference language for data processing, automation and artificial intelligence, and an excellent choice for APIs thanks to FastAPI and Django. Recent versions have significantly improved performance and typing, making it a serious production language.
Agencei uses Python for FastAPI services, data import and transformation pipelines, migration scripts, business automation and AI agents built on language models. We write typed Python, tested with pytest and deployed in containers.
This expertise is for companies that need to connect systems, make manual processes reliable or bring AI into their workflows, without depending on a closed platform or on a tool nobody on the team can maintain.
Use cases
FastAPI services
Asynchronous REST services, Pydantic validation, automatic OpenAPI documentation, ideal for exposing models or data processing.
Data pipelines
Importing, cleaning and transforming files, APIs and databases, orchestrated with scheduled jobs, Airflow or Prefect.
Data migrations
Idempotent scripts to transfer and transform data between databases, CMSs or ERPs, with checks and discrepancy reports.
AI agents and assistants
Agents that query your data, call your tools and carry out tasks, built with libraries such as LangChain, LangGraph or the model provider's SDK.
Business automation
Document generation, reconciliation, notifications, information extraction from emails or PDFs, replacing repetitive manual work.
Django applications
Data-rich back offices and portals with the Django admin, its ORM and a proven permission system.
Project types
- FastAPI or Django REST API
- ETL or ELT pipeline
- Data migration script
- AI agent connected to business tools
- Process automation
- Django back office
- Scoring or classification service
Integration with the rest of the stack
Python + PostgreSQL
SQLAlchemy or the Django ORM, Alembic migrations, and pgvector for semantic search in AI projects.
Python + Docker + AWS
Containers on ECS or Kubernetes for services, AWS Lambda for event-driven functions, S3 for files and SQS for queues.
Python + React or Next.js frontend
The FastAPI service exposes an OpenAPI schema used to generate the frontend's TypeScript types.
Python + AI models
Calls to language model APIs, local execution of open-source models, vector databases and result evaluation.
Python + Node.js or Java
Python handles data and AI alongside an existing main backend, through an internal API or a message queue.
Examples of problems solved
Daily processing that takes hours
Profiling, vectorisation with pandas or Polars, pushing SQL queries down to the database and parallelising independent steps.
Data import failing halfway through
Rewriting as idempotent batches with checkpoints, upfront validation and a log of rejected rows.
AI agent giving off-topic answers
Improving context retrieval (RAG), document chunking, precise tool definitions and automated evaluation.
Inconsistent Python dependencies across machines
Moving to uv or Poetry with a lock file, a reproducible Docker image and explicit Python versions.
Slow Python API under load
Adopting asynchronous FastAPI, connection pooling, caching and offloading heavy computation to Celery workers or background tasks.
Frequently asked questions
Is Python fast enough for a production API?
Yes for the vast majority of business APIs. FastAPI on an ASGI server handles high concurrency, and heavy processing is offloaded to workers or compiled libraries such as NumPy or Polars.
FastAPI or Django?
FastAPI for lightweight, asynchronous, data- or AI-oriented APIs. Django when you need a ready-made admin, a complete ORM and a highly structured framework.
Can you integrate AI into our existing tools?
Yes. Python is the natural language for this: we build a service that queries your data and APIs, and expose it to your current tools without rewriting them.
How do you ensure Python code quality?
Type checking with mypy or pyright, formatting and linting with Ruff, pytest tests, locked dependency management and all of it running in CI.
How do you deploy Python applications?
As Docker containers on Kubernetes or ECS for services, as Lambda functions for event-driven processing, with environment variables and secrets managed outside the code.
Related services
Business automation
Workflows, API integrations, scripts and scheduled jobs, with n8n, Make or custom code.
See this serviceArtificial intelligence
LLM-based solutions, RAG, document processing, classification and assistants, secured and evaluated.
See this serviceAI agents
Autonomous or semi-autonomous agents connected to your CRM, ticketing or ERP, with guardrails and oversight.
See this serviceAPI and backend
REST and GraphQL APIs, Node.js, Spring Boot and Python backend services, documented and secured.
See this serviceData migration
Migration between databases, engines, versions or systems, with ETL, validation and zero-downtime cutover.
See this serviceDocker Containerization
Containerising applications with Docker: optimised Dockerfiles, Compose, registries and CI/CD integration.
See this serviceRelated expertise
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