Retrieval-augmented generation
RAG retrieves relevant information from an approved knowledge source before the model writes an answer. Plan document ingestion, access rules, retrieval and source references together.
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Private LLM Inference
Private LLM inference.
Built around your business.
Run a selected open-weight language model on dedicated capacity in the EU. GreenGridLabs helps define the model endpoint, business data connections and operating scope for your application.
PRIVATE AI
Your models.
Your next step.
Shape a private AI deployment around your applications and users. The selected hardware and operating scope follow the needs of your workload.
Discuss your workload ↗
Enterprise AI / Practical use cases
Connect the model
to useful work.
A private LLM deployment starts with a clear application and representative evaluation data. Retrieval, application integrations and workflow automation are project services, scoped around the inference endpoint.
Extraction and summarisation
Evaluate a model on your document types, language and output format. Agree accuracy checks and human review for decisions that need oversight.
Application-connected workflows
Scope how a model can use business tools. Define permissions, approval steps, time limits and audit records before allowing a workflow to take actions.
Dedicated inference / EU data residency
Choose the right deployment.
Dedicated endpoint or a shared model API?
A dedicated endpoint allocates a defined configuration to your workload. A shared API typically pools capacity across users. Compare model control, throughput, data handling and operating requirements when choosing the right approach.
A private data layer needs more than a GPU.
Define where source documents, embeddings, prompts, outputs and logs are stored and processed. Include identity and access controls, retention and any external services used by the application.
Size the model before scaling the service.
Model size, precision, context length and concurrent requests affect GPU memory and response times. The Southern Finland pilot starts with one selected model up to the 8B class. Larger models and additional capacity require their own configuration review.
Plan for the next generation of capacity.
Our expansion with Rebels AI introduces current-generation NVIDIA GPUs into the development programme. For larger private models or more concurrent users, discuss the GPU configuration, memory and delivery window your workload needs.
Integrate through a familiar API.
An OpenAI-compatible interface offers a practical integration route. Confirm the functions your application needs, including streaming, structured output and tool calls, during the technical evaluation.
Questions to start with.
What is private LLM inference?
It means running a large language model within a defined environment for your organisation. The agreed configuration sets out dedicated compute, access, data handling and the connection to your applications.
Do you provide RAG as part of every inference endpoint?
Retrieval, document storage and indexing are scoped separately to match your use case. A dedicated model endpoint is the starting point, and a connected data layer is developed around the application.
Can we use this for agentic AI?
We can assess the inference and integration requirements for an agentic workflow. Tool compatibility, access permissions and action controls need validation for your model and application.
Does an EU deployment automatically make our application GDPR compliant?
The deployment supports your data-protection requirements through defined locations, access and processing terms. Your organisation’s obligations also depend on the application, its data and how it is used.
Tell us what your model should do.
Share your application, preferred model and data requirements.