The reference language for AI
Model providers’ SDKs and libraries for agents, document search (RAG) and machine learning are available in Python first.
Expertise · Python
Custom Python Development: services, data and AI in production
Etixio delivers custom Python development for your services, data processing and AI features: APIs, automations, data pipelines, agents and document search. We build, take over existing prototypes and scripts, and bring them to monitored execution in production.
Understanding the technology
Python in your project.
Python is used for web services, data processing and automation. Its ecosystem makes it possible to connect several processing steps, but a useful script still needs to be organized to run as a monitored, maintainable service. We handle this transition to controlled execution.
The strengths for your product
Why choose Python?
Python is one of the most versatile languages. Its readable syntax and ecosystem cover web applications and APIs as well as data and artificial intelligence, making it possible to bring these topics together in a single codebase.
Versatile
Web applications, APIs, automation scripts, data processing and learning models share the same language and tools.
Quick to implement
Simple syntax and short prototyping cycles make it possible to validate an idea quickly, provided it is industrialized afterwards.
A rich ecosystem
Django, Flask and FastAPI for the web; Pandas, NumPy and Scikit-learn for data; PyTorch for models.
Able to scale
With a suitable architecture — asynchronous processing, caching, job queues, load balancing — Python powers high-traffic platforms.
Our scope of work
The work we take on.
Services
Expose processing through APIs and explicit contracts.
Data
Organize the collection, transformation, validation and delivery of information.
Industrialization
Isolate dependencies, test processing and prepare it for production execution.
Python and AI
AI features in Python, all the way to production.
Most AI projects start in Python, often as a notebook or prototype. Our role is to turn it into a reliable service, integrated with your tools and monitored over time.
Agents and assistants
Assistants connected to your documents and software, able to search, summarize or trigger an action, with guardrails and human oversight. See our AI agents and chatbots.
Document search (RAG)
Preparing and chunking documents, indexing in a vector database, search and generation of sourced answers, as for the medical AI agent that summarizes the patient record.
Extraction, classification and summarization
Automated reading of incoming documents, field extraction, request classification and summaries, integrated into your existing processing. In commercial software, these processes become AI features of the product.
Taking over AI POCs
We take over a prototype’s code, data and model choices, then add authentication, monitoring, evaluation datasets, error handling and cost control. See our AI POC to production service.
Classic machine learning
Classification, forecasting or anomaly detection with Scikit-learn or PyTorch when the data lends itself to it and a language model is not the right tool.
For an overview of our AI use cases, see our enterprise AI solutions.
What we build
The Python projects we deliver.
Web applications and business tools
Back offices, internal tools and platforms built with Django or FastAPI, tailored to your processes.
APIs and microservices
REST or GraphQL APIs, gRPC services and third-party integrations to connect your systems.
Automations
Scripts, bots and internal workflows that replace repetitive manual tasks, with logging and error recovery.
Data and pipelines
Data collection, transformation and validation, ETL pipelines and dashboard feeds. See our BI & data offering.
Cloud deployments
Containerized, automated services on AWS, GCP or Azure, with monitoring and recovery procedures.
Our Python expertise
The Python ecosystem we master.
Frameworks & APIs
Django for full-featured applications, Flask for lightweight services, FastAPI and Pydantic for typed asynchronous APIs, REST, GraphQL and gRPC.
AI & machine learning
OpenAI, Anthropic and Google SDKs, agent and RAG libraries, vector databases (pgvector, Elasticsearch), Scikit-learn, PyTorch and TensorFlow.
Data
Pandas and NumPy, Airflow for pipeline orchestration, Spark for large volumes, Kafka for streams, PostgreSQL, MongoDB, Elasticsearch and Redis.
Industrialization
Reproducible environments, testing with pytest, Docker and Kubernetes, Terraform, continuous integration and deployment.
In the field
From technology to real-world use.
Collecting supplier files may require recognizing formats, checking fields and feeding an application. We isolate transformations, keep the information needed for diagnosis and plan for retries. Tests use incomplete, malformed or already processed files to verify the flow’s rules.
The choices that matter
The choices to examine before building.
Environments, dependencies and memory consumption must be reproducible. We choose between a scheduled task, an API service and a job queue based on the expected latency and volumes. Costly operations are measured before deciding on parallelism or a change of tool.
From work to deliverables
Delivery your team can take over.
The scope of the engagement specifies the components to build or take over and the acceptance criteria. We prepare what is needed to understand the changes, verify them and continue the work.
- Services and API contracts
- Business rule tests
- Deployment procedures
Frequently asked questions
Python development: your questions.
Why choose Python over another language?
For its readability, speed of implementation and an ecosystem that covers the web, data and AI. It is particularly relevant when your product combines an application, data processing and AI features.
Django, Flask or FastAPI, how do you choose?
Django suits full-featured, structured applications, with built-in administration and user management. Flask is lightweight and flexible for simple services. FastAPI is suited to modern, asynchronous, typed APIs, particularly for exposing AI features. The choice depends on the scope, the expected performance and the team that will maintain the code.
Is Python performant enough for large-scale applications?
Yes, with a suitable architecture — asynchronous processing, caching, job queues, load balancing. Intensive computation relies on optimized libraries (NumPy, PyTorch) or is isolated in dedicated services.
Is Python suited to artificial intelligence?
It is the reference language for AI and machine learning. Model providers’ SDKs, agent and RAG libraries, as well as PyTorch and Scikit-learn, are available in Python first.
Can you take over an AI prototype written in Python?
Yes. We audit the code, data and model choices, then add what is missing for production — authentication, data access control, monitoring, evaluations, error handling and cost tracking — before integrating it with your tools.
What are the timelines for a Python project?
A first usable scope, such as an MVP, is generally delivered within a few months depending on complexity. Scoping makes it possible to estimate the schedule precisely.
How do you ensure quality?
Unit and integration tests with pytest, checked typing, continuous integration, systematic code reviews and production monitoring. For AI features, we add evaluation datasets that are rerun on every change.
How much does a Python project cost?
It depends on the scope, the complexity and the collaboration model: a fixed-price project for a defined scope or a dedicated team based in Madagascar and Mauritius to evolve the product over time.
Let’s talk about your technical context.
Tell us about the application, the issue to address and the known constraints. We will review the dependencies and the first scope of work with your team.