Models and deployment
Where your models run: in the EU cloud, in Schwarz Digits Cloud, in your own data center or hybrid. And how you define per group which model does the work.
See deployment and model choicePlatform architecture
Orchestrate any AI. Connect any system.
The neuland.ai HUB is the orchestration core between your systems and the models of your choice: with an ontology of your company, governance in one place, and sovereign operation.
Architecture overview
How the neuland.ai HUB is built. From data source to answer.
From bottom to top: ingestion takes in your data, the ontology organizes it, the Orchestrator turns it into answers.
Hover over or tap a component: it explains its role and highlights its connections.
External engines
Cohere North · n8n · any engine
Connected via SDK · MCP
Teams already working with Cohere North or n8n use the knowledge, permissions and orchestration of the neuland.ai HUB through them.
Platform to platform
Customer and partner platforms, e.g. from testing and certification organizations, banking groups or insurers
These platforms use the neuland.ai HUB as their orchestration core, white-label or embedded
Connected via SDK · APIs · MCP · federation
Customer and partner platforms use the neuland.ai HUB as their orchestration core, white-label or embedded.
Enterprise data systems
Wherever the system lives: secure, open MCP
Public cloud / API
Microsoft 365 / SharePoint · Salesforce · etc.
On-prem / private cloud
SAP S/4HANA · Navision · Abas · Orbit · Documentum (DMS) · File servers · custom applications · etc.
Your data systems, wherever they live: ingestion takes in their data, the neuland.ai HUB accesses them directly via MCP, and each system has its own connector ontology.
neuland.ai HUB
Sovereign AI management and orchestration platform
Native interfaces: built on the HUB
Chat
Direct dialogue with the neuland.ai HUB, on the same core as every other interface.
Agents
Agents with fixed specifications: created once, shared with the team, with the same permissions and the same knowledge.
Projects
Workspaces with their own knowledge, shared with the team, on the same core.
Custom apps
Interfaces built on the neuland.ai HUB for specific use cases.
SDK and APIs
Programmatic access to the core · REST · MCP · platform federation
Programmatic access to the core via REST, MCP and platform federation. This is where external engines and your own platforms connect.
Core
Orchestrator
Routes every task to the right ontology and the right model per department and application
deterministic or agentic
It breaks every task into steps and selects knowledge, tool and model for each step. Fixed workflows run deterministically, open-ended tasks agentically, and every step is logged.
Agentic retrieval and reasoning
Agentic RAG · recursive language models (RLM)
Agentic threads · deep agents · multi-hop reasoning
KAG (knowledge-augmented generation) · query planning · semantic re-ranking
Plans the search, links knowledge across multiple steps and ranks results by meaning before a model answers.
Ontology layer
Multiple ontologies, orchestrated per department and application · generated automatically from your data (Ontology Generator) and kept in sync
automatic generationcontinuously updated
Enterprise ontology
Business logic · entities · relations · domain knowledge
Connector ontologies
One per system · gives every access the context of its system
Materialized as
Knowledge graph · skills · rules · vector index. The basis for transparent, verifiable answers
The ontology layer describes your company and every connected system. It is generated automatically from your data and stays continuously up to date.
Governance and compliance
Roles and permissions · guardrails · audit trail
incl. EU AI Act · DORA · BaFin · GDPR · BRAO
Corporate values · stance · ethics
Explainability and traceability
Roles, permissions and guardrails apply to every interface and every agent. Every step remains traceable in the audit trail.
Observability and logging
Across all connectors, platforms and agentic processes
Tracing · metrics · cost and token usage
Prompt and response logs · evaluations
Tracing, metrics, costs and logs across all connectors, platforms and agentic processes.
Ingestion pipelines
Petabyte range · scalable · Kubernetes GPU cluster
The ingestion pipelines take in data from your systems, scalable to the petabyte range, on a Kubernetes GPU cluster.
Models: agnostic
Sovereignty means: where the model runs
External · cloud API
incl. GPT · Claude · Gemini
not sovereign · via cloud API
On-prem / sovereign · open source
incl. Qwen · gpt-oss · DeepSeek · Mistral · Llama · Cohere
self-hosted SLMs · fine-tuned
The Orchestrator selects the right model per department and application: external via a cloud API, or operated sovereignly, self-hosted and fine-tuned.
Sovereign deployment
The neuland.ai HUB runs in Schwarz Digits Cloud (STACKIT), on Microsoft Azure, Google Cloud, on-premises or hybrid. Your data stays under your control.
Options
The interchangeable foundation: the environment follows your protection needs, while the neuland.ai HUB on top stays the same.
Structured knowledge
The ontology models your company. Terms, relationships and rules, not just documents.
A search finds documents. The ontology layer knows the relationships behind them: who is a customer, which contract applies, who is responsible. It is generated automatically from your data, stays continuously in sync and is available as a knowledge graph, rules and a vector index. The basis for this is not a sample: ingestion takes in entire data estates, up to the petabyte range.
- Enterprise ontologyWhen someone asks about the framework agreement with Muster Pumpentechnik, the neuland.ai HUB knows the customer, the contract and the responsible sales team, even if they sit in three systems.
- Connector ontologiesSAP knows framework agreements, Salesforce knows contacts: for each connected system, a connector ontology describes what lives there and what an access means, for reads and writes alike.
- Agentic retrieval and reasoningInstead of just searching for similar passages, the neuland.ai HUB follows the relations: from customer to contract, from contract to term. Every answer stays verifiable down to the source.
- Up to the petabyte rangeThe ingestion pipelines run on a Kubernetes GPU cluster and grow with your data, from the first folders to petabytes. The technology behind it is developed by our own neuland.ai Research Team, and it makes querying large data estates ten times more cost-efficient.
Model-agnostic
The model changes. Your agents stay.
When a better model comes along, you swap it in without rebuilding: agents, knowledge, permissions and workflows stay as they are. Admins decide which models a group may use.
Including these models, closed and open
OpenAI
Claude
Gemini
Llama
Mistral
DeepSeek
Qwen
Kimi
GLM
Cohere
Aleph Alpha
Model benchmark
How strong, fast and expensive common models are, measured independently by Artificial Analysis, and which ones can run in your own data center.
Go to the model benchmarkTools and data
It doesn’t matter where a system lives. The neuland.ai HUB reaches it.
Public cloud or your own data center: the ingestion pipelines take in the data from your systems, and the neuland.ai HUB accesses them directly via MCP. Complex system landscapes and legacy systems can be connected too.
- Petabyte-scale ingestionEven large data sets such as complete DMS archives and file servers are ingested, with ten times the cost efficiency in querying.
- Open MCPThrough the open MCP standard, the neuland.ai HUB also reaches systems that have no ready-made connector, such as custom applications.
Platform to platform
The neuland.ai HUB as the core of your AI and platform. White-label or embedded.
If you run your own AI platform, for example as a testing and certification organization, banking group or insurer, the neuland.ai HUB provides orchestration, knowledge and model selection in the background. Your customers keep working in your interface, under your brand.
- Your brand stays up frontYour customers only see your interface; the neuland.ai HUB orchestrates in the background.
- APIs, MCP and federationYour customer portal queries the core via API or MCP, for example for framework agreement RV-2027-014, and gets the term and conditions back with their source.
- Permissions and audit trail includedRoles, permissions and the audit trail of the neuland.ai HUB also apply through your platform.
Deployment and documentation
You choose where the neuland.ai HUB runs. The platform stays the same.
The same applies in every environment: the same agents, the same permissions, the same log. The documentation is in the Trust Center.
DeploymentYour choiceFrom the German cloud to your own data center, including mixed operation.
TrainingExcludedYour data is never used to train language models, regardless of where the neuland.ai HUB runs.
PermissionsPer roleGranular permissions and tenant isolation, the same in every environment.
DocumentationISO 27001, DPAAvailable documentation includes an ISMS per ISO 27001, a DPA under Art. 28 GDPR and a DORA addendum.
Your systems, your models. We bring the core.
Together we define which systems connect, which models you approve and where the neuland.ai HUB runs.
After your request
- 01You name your systems and your deployment requirements
- 02We show where models, data and permissions come together in the neuland.ai HUB
- 03You receive a proposed architecture for your system landscape