Everything you need to know about Angèle.

Angèle is an enterprise AI orchestration platform. It brings assistants, agents, data sources, connectors, several AI models and governance rules together in one environment, with the support needed to turn those building blocks into concrete business usage.

Angèle is for organisations that want to industrialise their use of AI while keeping control over data, access, models and usage rules. The approach suits mid-sized companies as well as more complex organisations, depending on the perimeter and deployment mode chosen.

Those tools give access to powerful AI capabilities. Angèle adds a layer of orchestration, connection to data, governance, model choice and support, in order to build a coherent AI environment across the enterprise.

Not necessarily. Angèle complements an existing environment, organises usage and integrates certain building blocks where it makes sense. The starting point remains the business need and the level of control you are looking for.

Because an organisation does not always benefit from depending on a single model or a single tool. Angèle lets you combine models, data, agents, connectors and governance rules so as to use the right building block in the right place and evolve the whole over time.

Yes. The recommended approach is to start with a first limited, measurable use case, then extend the environment to other teams, sources, assistants, agents or models once the value is demonstrated.

An assistant supports the user: it searches, summarises, analyses, drafts or helps decide. An agent goes further by carrying out an objective or a workflow, potentially in several steps and using different tools or sources.

Yes. An assistant can be configured for a function, a data perimeter, access rules and specific objectives: leadership, sales, human resources, legal, customer service, IT, and so on.

Yes, where the use case lends itself to it. Agents can automate certain steps, interact with tools or data and return a result; the level of autonomy and human validation is set according to risk and context.

For a first project, a targeted assistant or simple automation is often more relevant: the need is easier to frame, the value quicker to measure and the risks simpler to manage.

Yes. Teams have different assistants or agents while sharing the same platform, shared governance and authorised sources adapted to each profile.

Yes. The Angèle ecosystem integrates specialised solutions when they bring real value to the use case. The aim is to assemble the right building blocks rather than reinvent what already exists.

Yes. Angèle connects to documents, databases, business applications and other authorised sources in order to give the AI the context relevant to the use case.

RAG, or retrieval-augmented generation, lets the AI first search for relevant information in your sources, then use that context to generate its answer. This helps produce more contextualised answers and use your knowledge without retraining a model.

Where the use case draws on document sources and the integration allows it, Angèle returns the references used, making verification and traceability easier.

Angèle applies the access rules configured in the platform and can, depending on the source and the connector, align with existing rights. The exact behaviour is defined and tested during integration.

Depending on the environment and the connectors available: documents, collaboration spaces, ERP, CRM, document management systems, databases, business applications, APIs and other authorised internal or external sources.

Yes. Where no standard connector meets the need, a specific integration is studied based on the application, the APIs available, the security rules and the use case.

Not necessarily. For many uses, RAG makes it possible to use the organisation's knowledge without retraining the model. Fine-tuning or other approaches are considered only where they bring real value.

Angèle orchestrates local models compatible with its technical environment and, if you authorise it, models accessible through external providers. The catalogue evolves with the technologies available, the capacity of the infrastructure and client needs.

Yes, in a local deployment or in the configurations designed for it. The model must be compatible with the technical environment and qualified against the infrastructure and the use case.

The two subjects need to be distinguished. Business data stays in the reference environment defined. The request, the useful context and the answer, however, are processed in the local environment or in the Private Cloud, or at an external provider if a public model has been deliberately enabled.

Depending on the use case and the configuration chosen, the request and the necessary context may be sent to the external provider to generate the answer. The level of control, retention and sovereignty then depends on the model chosen and the associated contract.

With a local model operated inside your environment, your information is not sent to the model's publisher. For an external provider, the rules on use, retention or training depend on the contract and the configuration chosen; those conditions are verified before activation.

Because the best model depends on context. A model can excel at reasoning, multimodal work, code or speed while involving external processing or a different level of sovereignty. Angèle helps you weigh performance, security and use.

Yes. That is one of the benefits of a multi-model architecture: reserving local models for sensitive uses and authorising, where relevant, certain external providers for performance or specialisation needs, under clearly defined rules.

The choice is made with you, based on the sensitivity of the information, the performance expected, regulatory constraints and the architecture. Angèle provides the analysis and recommendations needed to make an informed choice.

Users, roles, assistants, models and authorised sources are defined according to your organisation's rules. The aim is to give each profile only the access it needs.

Angèle is designed to allow logging and traceability of usage, access and configurations according to the functions enabled. This supports internal controls, audits and usage analysis.

Depending on the traceability functions enabled, the environment retains the elements needed to identify the model, the sources, the access and the context of a use. The level of detail is set according to your governance needs and the platform's capabilities.

Compliance never depends on a platform alone: it also depends on the use case, the data, the organisation, the models and the client's responsibilities. Angèle provides governance, control and traceability mechanisms designed to support those requirements.

An AI model can produce an inaccurate answer. Angèle helps reduce that risk through model choice, the use of controlled sources, traceability and appropriate validation. A generated answer should not be treated as automatically accurate, nor as a human decision.

No mechanism today can guarantee that a language model will never be wrong. Angèle does reduce the risk by using reference sources, choosing the right model, structuring instructions, tracing usage and maintaining human validation where the stakes require it.

The Private Cloud environment is hosted in Luxembourg on infrastructure operated by Rcube. Rcube is a support PSF and Angèle relies on hosting infrastructure certified to ISO 27001.

The security of workstations, devices, networks and local access falls under the client's security policy. Angèle secures its own perimeter: hosting where provided, orchestration, access, flows, governance and traceability mechanisms.

Yes. A client may decide to work only with local models or within the Private Cloud, depending on their architecture and governance rules. Enabling external providers remains a framed choice.

Locally, Angèle is deployed on the client's infrastructure. In Private Cloud SaaS, the environment is hosted and operated in Luxembourg. The choice depends on your infrastructure constraints, the level of control you are seeking and your organisation.

They depend in particular on the model or models chosen, the volumes, the performance expected, the connectors and the level of availability sought. Technical sizing is carried out before deployment.

Yes, through standard connectors or specific integrations depending on your environment: collaboration tools, documents, ERP, CRM, document management systems, databases, business applications or APIs.

It depends on the use case, the data and the integrations required. The recommended approach is to start with a limited, measurable perimeter, then extend progressively once the value is demonstrated.

The ecosystem is designed to evolve: new models, connectors, agents, applications or areas of expertise are integrated when they bring real value and remain compatible with security and infrastructure constraints.

Support and assistance depend on the perimeter agreed. Depending on your needs, they cover running the environment, assistance, configuration changes, adding new uses or calling on partner expertise.

We favour four criteria: a real frustration, accessible data, a measurable result and managed risk. The best first project is not necessarily the most spectacular, but the one that demonstrates concrete value quickly.

The approach follows five stages: understand the need, design the solution, deploy, support adoption then evolve usage. The level of detail and the deliverables are adapted to your context.

No. Angèle is designed to be used by business teams with appropriate support. IT, security or compliance teams step in where their expertise is needed for integration, access or governance.

Indicators are defined during scoping according to the objective: processing time, quality, reliability, productivity, adoption, error reduction or any other relevant business indicator. The aim is to measure a concrete result rather than count usage.

Support includes training, communication, onboarding, assistance, usage monitoring and adjustments. The aim is for AI to fit into your teams' real practices.

The project does not stop at deployment. The environment is monitored, adjusted and enriched with new use cases, models, agents or connectors as needs arise.

That is precisely the point of an open ecosystem. We examine a new integration, a specific agent, a connector or a partner application when the need brings real value and fits within your security and governance framework.

Angèle draws on several areas of expertise: AI platform, infrastructure and hosting, cybersecurity, external consultants, specific integrations and specialised AI applications. Partners step in where their expertise brings real value to the project.

Because an enterprise AI project combines very different subjects: business, infrastructure, cybersecurity, integration, agents, specialised applications and support. Angèle orchestrates that expertise so you do not have to build your own supplier chain.

No. New building blocks, technologies, applications or areas of expertise are integrated when they become relevant. Client needs and market developments feed directly into how the ecosystem evolves.

Yes, if the solution can be integrated into the architecture and meets the project's requirements. Angèle does not impose a closed catalogue; the aim is to build the most relevant combination.

Because model choice, governance, integration, adoption and value measurement matter as much as the platform itself. The ecosystem makes it possible to call on the right expertise at the right moment.

Depending on the maturity and the objective of the project, several schemes may be relevant, notably Fit 4 Digital, Fit 4 AI, SME Package Digital and SME Package AI. Conditions and terms evolve: we point to the official information published by the Luxembourg Government.

The choice depends on your maturity, the type of need, the scoping already carried out and the objective sought: assessment, roadmap or concrete implementation. We help you structure the project and identify the scheme that appears most relevant.

No. Eligibility and the granting of aid rest exclusively with the competent bodies. We can, however, help you frame the need, prepare the necessary elements and point you to the right contacts or partners.

It depends on the scheme: assessment, scoping, objectives, use cases, action plan, indicators or implementation elements may be requested. The exact deliverables are defined by the programme and the official requirements in force.

Yes, and it is generally useful: clarifying the need, the data, the objectives and the expected value helps steer the project. Any commitments made before a decision must, however, be checked against the rules of the scheme concerned.