Us

Applying AI to disorganized or inaccessible data leads to errors, poor model quality, and a huge waste of resources.

We help you prepare, govern, and make your data accessible with clear structures and automated processes.

What we do:

• Design and implementation of scalable ETL/ELT data pipelines.
• Building cloud or hybrid architectures for efficient data storage, processing, and access (BigQuery, Databricks…).
• Modeling and organizing structured and unstructured data for advanced analytics and ML.
• Implementation of data quality, cataloging, and lineage systems (DataOps).
• Process orchestration and automation with tools such as Airflow, dbt, etc.
• Definition of data governance and access models.

This service adapts general-purpose Artificial Intelligence models to your organization’s specific context: your language, your data, your way of working… using your own data and structures.

Our goal is to train the models so they deliver value from concrete use cases.

What we do:

• Fine-tuning, RAG… of LLMs and other pretrained models.
• Creation of training-specific datasets.
• Application of techniques such as prompt engineering or custom embeddings.
• Evaluation of results with metrics tailored to the use case.
• Design of retraining and continuous-improvement workflows.
• Generation of proprietary embeddings for vector systems.

This service is designed to support organizations that identify processes they could automate or improve with AI, but don’t know how to move from the idea or business need to real, functional AI solutions.

What we do:

• Identification and prioritization of use cases with clear ROI.
• Development of custom solutions: assistants, agents, recommenders, classifiers, etc.
• Technical and functional validation of the solution in real-world environments.

This service is designed to help organizations deploy Artificial Intelligence solutions in secure, scalable environments connected to their existing technology ecosystem.

Models often work well in testing, but fail when integrated or scaled in real-world environments.

Our goal is to help you build a solid, well-connected, and protected infrastructure where AI can operate without friction or risk.

What we do:

• Auditing and designing cloud-native or hybrid architectures for model deployment.
• Deployment of APIs and containers (Docker, Kubernetes).
• Developing and implementing MCP Servers so models can execute the necessary operations.
• Integration with corporate systems (CRM, ERP, CMS, etc.).
• Implementation of security, authentication, encryption, and monitoring policies (IAM, Zero Trust).
• Infrastructure automation (IaC with Terraform, CI/CD).
• Security policies and regulatory compliance.

We make sure models run in production with efficiency, speed, and stability. We move from the lab to the real environment with a focus on performance and scalability.

Many organizations manage to train models but run into difficulties when trying to integrate them into their systems or get them up and running.

Our goal is to help those models move beyond testing and function in the day-to-day of your operation.

What we do:

• Converting models into optimized formats (ONNX, quantization).
• Deployment as services (API, batch, edge computing).
• Integration with inference pipelines and databases.
• Automatic scaling based on load and demand.
• Version management, rollback, and maintenance.
• Evaluation of cloud costs and efficiency.

This service is designed to provide continuous tracking and real visibility into how Artificial Intelligence models behave once they’re in production.

Many AI projects work well at first, but over time lose accuracy, introduce bias, or fail without anyone noticing.

Our goal is to help you keep control of what your AI is doing at all times, measure its impact, and be able to react when something changes or stops working as it should.

What we do:

• Monitoring of model performance and drift.
• Automatic alerts for anomalies, errors, or loss of accuracy.
• Visualization through dashboards for business and technology teams, with metrics tailored to each.
• Logging and full traceability of every inference or prediction.
• Integration with existing observability platforms (Grafana, Kibana, etc.).
• Visual auditing of automated decisions.

This service is designed so organizations can explore the potential of Artificial Intelligence without major upfront investment, validating ideas quickly and with controlled risk.

There’s often a clear intuition about where AI can add value, but the technical or business evidence needed to justify implementation is missing.

Our goal is to help you experiment with purpose: turning hypotheses into functional prototypes, evaluating their impact, and deciding — with real data — whether it’s worth scaling.

What we do:

• Identification and prioritization of use cases with AI potential.
• Definition of the experiment: scope, success metrics, and limitations.
• Agile development of functional prototypes or proofs of concept.
• Evaluation of technical performance and business value.
• Co-creation workshops and knowledge transfer.
• Support in the transition from PoC to a production solution (when applicable).
• Support in building the business case.

This service enables the generation of high-quality synthetic data when real data is scarce, sensitive, or simply inaccessible.
Many AI models can’t be trained properly due to a lack of data or legal restrictions related to privacy.

Our goal is to help you create artificial, yet realistic, datasets that allow you to train, validate, or test models without compromising confidentiality.

What we do:

• Suitability assessment for synthetic data use.
• Generation of artificial datasets (tabular, text, image) with validated fidelity.
• Statistical and functional validation of the generated data.
• Training and testing of models using synthetic data.
• Ensuring regulatory compliance (anonymization, differential privacy, etc.).
• Integration into existing AI or MLOps pipelines.

This service, delivered in partnership with a specialized partner, is designed to adapt companies’ digital positioning strategy to a new scenario in which search engines no longer rely solely on keywords, but interpret content using techniques similar to language models.

Our goal is to help you create and structure content that can be understood by both search engines and generative models, so your brand stays relevant.

What we do:

• AI-focused SEO audit: technical, semantic, and content-structure analysis.
• Restructuring web content to make it easier for LLMs to interpret.
• Content strategies optimized for featured snippets and intent-based search.
• Integration of AI-driven content generation under editorial control.
• Ongoing monitoring of rankings, performance, and adjustments.