GenerIA AI solutions can operate wherever your work happens: on your own servers, in clouds of your choice, through plug-and-play appliances or directly on edge and mobile devices. They can run with or without a network connection and inside fully air-gapped environments.
APIs, connectors and containerized components make them easy to integrate into existing applications, proprietary systems and AI agent workflows. Their interfaces, features, telemetry and branding are fully customizable, whether you want a solution in your own colors or a completely white-labeled experience.
Your organization chooses the infrastructure, integration model, user experience and deployment path. GenerIA provides the intelligence without locking you into a particular cloud, vendor, interface or operating environment.
The right AI solution must adapt to your organization's technical and operational environment. It should not force that environment to be modified or rebuilt.
Any appropriate infrastructure - GenerIA solutions can run on your own servers, in private data centers, through hosting providers or in clouds of your choice, including GenerIA's in Canada and France.
Connected or offline - Depending on the use case and hardware requirements, GenerIA solutions can operate through conventional networks, at the edge, on mobile devices or within fully air-gapped environments.
Software or appliance - GenerIA technologies can be delivered as software components or as plug-and-play appliances combining the required hardware and software. When supplied as an appliance, the solution can be built by GenerIA on server platforms from the vendor of your choice so that it fits naturally into your existing infrastructure, procurement and support practices.
This range of options makes it possible to select the deployment model according to your latency, sovereignty, connectivity, performance and operational constraints.
Most organizations already rely on applications, data stores, identity systems, monitoring platforms and established workflows, as well as AI systems and services. A truly flexible AI solution should integrate with this environment on your terms, complementing or replacing individual components without disrupting what already works.
Application and data connectors - GenerIA solutions can communicate with databases, document repositories, APIs and proprietary middleware. Our architecture provides many extension points through which connectors can be implemented using the programming languages, protocols and data formats best suited to each existing system.
Machine-to-machine operation - AI capabilities can be exposed to other applications and services without requiring a conversational interface. They can also participate in multi-agent workflows or interact with specialized processing components through MCP-compatible interfaces, standard APIs or direct ad hoc integrations.
Compatible observability - Logs, metrics and traces can feed your organization's current monitoring architecture. Default collection, storage and visualization components can be replaced when another technology is already standardized internally.
Flexibility also applies to the visible product.
Any brand or white-label - GenerIA interfaces can be adapted in terms of styles, functions, terminology, interaction patterns and branding. A solution may appear as a GenerIA application, become part of an existing interface or be delivered as a fully white-labeled service.
Voice capabilities, multilingual interactions and audience-specific interfaces can be added according to the needs of each group of users.
Built-in scalability - A use case may begin on one device or within one department and later serve many applications, sites or user groups. Containerized components allow the same functional foundation to evolve toward larger or more distributed deployments, with support for horizontal scaling from the outset.
Extensible by design - Because our interfaces, AI services, retrieval systems, telemetry and data connectors are modular, individual components can be changed, extended or replaced without redesigning the entire solution.
Modular service architecture - Model inference, retrieval, data access, authentication, application APIs, interfaces and observability are separated into discrete functional components. Each component can be configured, scaled or replaced according to the target environment.
Containerized deployment - GenerIA solutions are available as Docker containers, with support for other container formats when required. Containers package runtime dependencies consistently across development, testing, on-premises and hosted environments.
Configuration-driven integration - Environment-specific settings - service endpoints, enabled models, authentication providers, data sources, permissions and telemetry destinations - are independent from the application code. The same solution can therefore be deployed in different contexts without maintaining separate codebases. This is the exact opposite of rigid, monolithic solutions.
Stable service contracts - APIs and machine-to-machine interfaces isolate consuming applications from the internal implementation of the AI pipeline. Models, indexes or orchestration logic can evolve while preserving the contracts used by business applications.
Replaceable connectors - Data and functional connectors provide controlled boundaries around proprietary systems. A source application can be upgraded or replaced by adapting its connector rather than rebuilding the AI solution.
Hardware-aware model packaging - Model capacity, numerical precision and runtime configuration can be selected according to available CPU, GPU, memory and storage resources. You can therefore target anything from compact edge deployments to large multimodal pipelines.
Autonomous offline bundles - For disconnected operation, the required model weights, retrieval indexes, application services and interfaces can be deployed together inside the controlled environment. Runtime behavior does *not* depend on the availability of external APIs.
Decoupled user interfaces - The frontend communicates with GenerIA services through defined interfaces and can therefore be replaced, embedded or customized independently. Web, mobile, kiosk, business-application and white-label experiences can share the same backend capabilities.
Portable observability - OpenTelemetry provides a standard and replaceable mechanism for exporting applicative metrics, logs and traces. Organizations can retain GenerIA's default observability components or route the same data into their existing persistence, visualization and alerting systems.