Five service lines
Services
Architecture, development and production deployment of data platforms, web and mobile applications and artificial intelligence systems. Systems integration, cloud architecture and infrastructure operations.
- Data
- Data warehouses and pipelines to support your analytics, applications and AI models.
- Applications and integration
- Custom software, web and mobile apps, APIs and integration with your systems.
- Azure and Google Cloud
- Cloud architecture, infrastructure as code and platform deployment on Azure and Google Cloud.
- Servers and hosting
- Servers, virtualisation, storage and enterprise services, from design to operations.
- Artificial intelligence
- Assistants, agents connected to your tools, data extraction and production AI infrastructure.
Data
Architecture and production deployment of data warehouses, ETL/ELT pipelines and SQL models for BI, applications and AI. Source integration, storage sizing and query optimisation.
Technical services
Relational database and warehouse schemas for SQL Server, PostgreSQL, BigQuery or Snowflake
Dimensional models, historical data and semantic layers
Batch and streaming pipelines for ingestion, transformation and publication
dbt models and Airflow or Azure Data Factory workflows
Power BI, Tableau and Looker dashboards or custom analytical interfaces
Data APIs, application connectors and deployment configuration
Related projects
Explore reference architectures
A Google Cloud platform brings market feeds and information extracted from financial documents into a BigQuery warehouse. One application is building a history of financial instruments and portfolios for analysis in Power BI and use by applications through APIs.
Apache Airflow
BigQuery
Cloud Run
Cloud Storage
dbt
GitHub
Power BI
Terraform
Vertex AI
Components and flows
- Source files retained in Cloud Storage and loaded into raw BigQuery tables
- dbt transformations and data quality checks before publishing fact and dimension tables
- Document extraction through Vertex AI, analysis in Power BI and application access through Cloud Run APIs
An Azure platform brings equipment measurements together with business data, retains their history and runs forecasting models. One application is monitoring a group of solar installations, comparing measurements across sites and publishing generation forecasts in dashboards or applications.
Azure Storage
Business Central
Azure Data Factory
Docker
.NET
Microsoft Entra ID
Microsoft Graph- Power BI
Python
Apache Spark
SQL Server- Terraform
Components and flows
- Collection of time series, Business Central data and files available through Microsoft Graph
- Retention in Azure Storage, Spark normalisation and loading into SQL Server models
- Python forecasting and publication of historical data and results in Power BI
A compute platform runs Python and R analyses of genomic sample batches in containers. Each run retains references to input files, the Git commit, container image and parameters so results can be compared and the pipeline rerun on another dataset.
- Apache Airflow
- Docker
- GitHub
Kubernetes
Linux
PostgreSQL
Pure Storage- Python
R- Terraform
Components and flows
- PostgreSQL catalogue for datasets, parameters and provenance; shared storage for files and results
- Docker images built from identified GitHub versions, then executed through Airflow and Kubernetes
- Python and R processing, result publishing and execution records
Applications and integration
Design and development of business software and web, mobile or desktop applications. Implementation of user interfaces, databases and APIs, followed by integration with existing software and production deployment.
Technical services
Application architecture, data models and interface prototypes
Source code and web, Android, iPhone or desktop applications
Backend services, documented APIs and enterprise-system connectors
Processing engines, synchronisation protocols and local storage
Integrated AI features and development-assistance tools
Automated tests, CI/CD pipelines and maintenance documentation
Related projects
Explore reference architectures
In this shipment-tracking architecture, Android and iPhone applications send events to a command API. Kafka distributes them to consumers that update PostgreSQL views exposed through a read API. An analytical copy feeds Snowflake and Power BI for volume and lead-time analysis.
- Docker
- .NET
- GitHub
JavaScript
Apache Kafka
Kotlin- Kubernetes
- PostgreSQL
- Power BI
- Python
SAP
Snowflake
Swift
Components and flows
- Mobile and ERP events submitted to a .NET API with event identifiers and retry handling
- Kafka distribution, PostgreSQL projections and separate handling of failed events
- Tracking access through a read API; analytical copy in Snowflake and Power BI
A web and mobile application uses .NET services hosted on Azure, connected to PostgreSQL, Business Central and Microsoft 365. One example is a custom business portal for following quotations and orders, with access to related documents and records from the business management system.
Azure App Service
Azure Database for PostgreSQL- Business Central
- .NET
- Microsoft Entra ID
- GitHub
- Microsoft Graph
- JavaScript
- Kotlin
Microsoft 365
n8n- Power BI
- Terraform
Components and flows
- Web and mobile access to .NET application services, with Entra ID authentication
- n8n integrations with Business Central and Microsoft 365 through their APIs
- Azure App Service and PostgreSQL deployment, with private data access and Power BI publishing
The storefront records orders in PostgreSQL, publishes their events through Kafka and synchronises them with Business Central. This setup can connect an online shop with central product, inventory and billing management, while sales data feeds a separate analytical history in Snowflake.
- Business Central
- dbt
- Docker
- .NET
- GitHub
- JavaScript
- Apache Kafka
- Kubernetes
- PostgreSQL
- Power BI
- Python
Redis- Snowflake
Components and flows
- Web access through a .NET API; orders and pending events stored in a PostgreSQL transaction
- Worker publishing to Kafka, followed by order synchronisation with Business Central
- Snowflake history, dbt models and Power BI analysis; Redis caching for frequent requests
Azure and Google Cloud
Azure and Google Cloud architecture for applications, data platforms and AI models. Infrastructure automation, service deployment, data migration and sizing of CPU, memory and storage resources.
Technical services
Architecture diagrams and network, identity and cloud-service configuration
Terraform modules and automated configuration scripts
Container images and application and data deployment configuration
Continuous integration and delivery pipelines (CI/CD)
Transfer scripts, cutover procedures and rollback plans
Monitoring dashboards and capacity and cost reports
Related projects
Explore reference architectures
A web and mobile application uses .NET services hosted on Azure, connected to PostgreSQL, Business Central and Microsoft 365. One example is a custom business portal for following quotations and orders, with access to related documents and records from the business management system.
- Azure App Service
- Azure Database for PostgreSQL
- Business Central
- .NET
- Microsoft Entra ID
- GitHub
- Microsoft Graph
- JavaScript
- Kotlin
- Microsoft 365
- n8n
- Power BI
- Terraform
Components and flows
- Web and mobile access to .NET application services, with Entra ID authentication
- n8n integrations with Business Central and Microsoft 365 through their APIs
- Azure App Service and PostgreSQL deployment, with private data access and Power BI publishing
A local gateway collects equipment measurements and events, then a Kafka and Spark platform combines them with SAP data for applications and analytics. One example is a production line whose current state is available through an application, alongside analysis of its production and quality history.
- Docker
- .NET
- GitHub
- Apache Kafka
- Kotlin
- Linux
- PostgreSQL
- Power BI
- Python
- SAP
- Snowflake
- Apache Spark
- Terraform
Components and flows
- Equipment data read by a Python gateway on Linux; transmission to Kafka through a controlled network interface
- Spark time series processing and loading into PostgreSQL and Snowflake
- Enrichment with SAP data, .NET applications and Power BI analytics
A Google Cloud platform brings market feeds and information extracted from financial documents into a BigQuery warehouse. One application is building a history of financial instruments and portfolios for analysis in Power BI and use by applications through APIs.
- Apache Airflow
- BigQuery
- Cloud Run
- Cloud Storage
- dbt
- GitHub
- Power BI
- Terraform
- Vertex AI
Components and flows
- Source files retained in Cloud Storage and loaded into raw BigQuery tables
- dbt transformations and data quality checks before publishing fact and dimension tables
- Document extraction through Vertex AI, analysis in Power BI and application access through Cloud Run APIs
Servers and hosting
Design and operation of servers, virtualisation clusters, SAN/NAS storage, directories and email. Access configuration, data replication, backups, and monitoring of resources and services.
Technical services
Configured Linux and Windows servers and deployment procedures
Virtualisation platforms, storage and database services
Directories, single sign-on (SSO) and permission matrices
Configured and integrated Microsoft 365, Google Workspace or kSuite environments
Monitoring dashboards, alert rules and administration scripts
Backup configurations, restoration procedures and migration documentation
Related projects
Explore reference architectures
A compute platform runs Python and R analyses of genomic sample batches in containers. Each run retains references to input files, the Git commit, container image and parameters so results can be compared and the pipeline rerun on another dataset.
- Apache Airflow
- Docker
- GitHub
- Kubernetes
- Linux
- PostgreSQL
- Pure Storage
- Python
- R
- Terraform
Components and flows
- PostgreSQL catalogue for datasets, parameters and provenance; shared storage for files and results
- Docker images built from identified GitHub versions, then executed through Airflow and Kubernetes
- Python and R processing, result publishing and execution records
This architecture connects school administration, Microsoft 365 and learning applications through a shared Entra ID identity. One example is a school portal linked to enrolment records, online courses and collaboration spaces, with accounts and groups reused across the platforms.
- Azure App Service
- .NET
- Microsoft Entra ID
- GitHub
- Microsoft Graph
Microsoft Intune- Microsoft 365
- n8n
- PostgreSQL
- Power BI
Components and flows
- Data exchange from the school information system to applications and Microsoft Graph services
- Entra ID authentication for Microsoft 365, the learning platform and the application portal
- Intune device management, PostgreSQL application data and Power BI models
Solutions and applications
Artificial intelligence
Development of document assistants, agents using APIs and AI extraction pipelines. Integration of models with data and software, response evaluation and production application deployment.
Technical services
Assistants and agents, connected tools and context configuration
Document indexes, semantic search and RAG pipelines
Extraction, classification and multilingual processing pipelines
AI APIs and interfaces integrated with web and mobile applications
Evaluation datasets, comparative tests and performance monitoring
Inference servers and connectors to cloud-model APIs
Related projects
Explore reference architectures
A private platform connects a research agent to a document index and a local Llama model, with retrieval filtered by access permissions. For example, it can support review of a contract file by locating relevant passages in the firm’s documents and reference texts, then preparing an answer with source references.
- Docker
- .NET
- Microsoft Entra ID
Infomaniak
LangChain- Linux
Llama (Meta)
Model Context Protocol- Microsoft 365
- PostgreSQL
- Python
Components and flows
- Extraction of Microsoft 365 documents and reference collections, followed by text and vector indexing in PostgreSQL
- LangChain agent using a Llama model and read-only MCP search tools, with permission filtering and bounded tool calls
- Available context checked before generation, with references presented in a .NET application
A platform connects mobile data collection and Salesforce CRM to a PostgreSQL warehouse, with Tableau analytics and multilingual document search. One application is a programme operating across several countries, bringing locally collected information together with financial records and related documentation in different languages.
- Docker
- .NET
- Infomaniak
- Kotlin
- LangChain
- Microsoft 365
- n8n
- PostgreSQL
- Python
Salesforce
Tableau
Components and flows
- Mobile collection through a .NET API and Salesforce CRM integration with programme data
- Python pipelines feeding a PostgreSQL warehouse, with Tableau analytics and API access
- Multilingual search in Microsoft 365 documents, using LangChain and an Infomaniak model API
A Google Cloud platform brings market feeds and information extracted from financial documents into a BigQuery warehouse. One application is building a history of financial instruments and portfolios for analysis in Power BI and use by applications through APIs.
- Apache Airflow
- BigQuery
- Cloud Run
- Cloud Storage
- dbt
- GitHub
- Power BI
- Terraform
- Vertex AI
Components and flows
- Source files retained in Cloud Storage and loaded into raw BigQuery tables
- dbt transformations and data quality checks before publishing fact and dimension tables
- Document extraction through Vertex AI, analysis in Power BI and application access through Cloud Run APIs
Practical information
Who carries out the work?
The engineer carries out the architecture, development and integration work directly.
Do you resell hardware or licences?
Independent consulting, engineering, development and systems operations, with no hardware or licence resale and no supplier commissions.
Who owns the code that gets developed?
The contract defines code rights and source-code delivery. Pre-existing components and dependencies retain their respective licences.
Where is the data hosted?
Hosting on Azure, Google Cloud, Swiss hosting platforms or private infrastructure, with the environment and regions selected during project design.
How do you bill?
Fixed-price assessments, project scope and pricing agreed before work begins, and a monthly retainer for ongoing operations. Prior approval for any additional work.
A first conversation, no commitment
A discussion of your project, implementation options and timeline. A direct reply, usually within one working day.