Designed for private enterprise deployment.
ClusterAI sits above your internal models, RAG systems, tools and inference runtimes. It provides routing, governance, health awareness, fallback and visibility — without replacing the components you already operate.
From the user request to the audit trail.
Each layer has a clear responsibility. The exact deployment depends on the customer infrastructure and pilot scope.
Users
Employees interact through one familiar chat interface or future API integrations.
ClusterAI client
Builds the request, conversation context and governance metadata.
Routing & policy layer
Classifies the request, evaluates permissions, data zone, node health, saturation, fallback rules and capability fit.
Capability registry
Each daemon declares what it can do: domains, custom tags, data zones, model class, supported capabilities and status.
AI capabilities
Legal AI, Contract AI, HR RAG, Code AI, Commercial AI, Product AI, Generalist AI and custom enterprise capabilities.
Inference runtimes
Designed to work with local and OpenAI-compatible backends such as Ollama, vLLM, TGI, LiteLLM, LocalAI or internal APIs.
Internal data sources
Documents, knowledge bases, SharePoint-like repositories, policies, contracts, codebases, databases and internal tools.
Observability & audit
Shows selected node, routing reason, status, fallback, logs and usage signals.
The daemon stays stateless. The client carries the conversation.
The selected daemon does not need to retain long-term memory. The ClusterAI client builds a governed conversation context, compresses recent history when needed, filters it according to role and data-zone rules, and sends it to the selected capability.
Not which server is free — which capability is right.
A classic load balancer asks which server is free. ClusterAI asks which AI capability is most appropriate, authorized, available and healthy for this request.
ClusterAI is designed to complement existing infrastructure. It can sit above local models, private servers, internal RAG systems, agent frameworks and AI gateways.
How ClusterAI is different.
The market already has AI assistants, RAG builders, gateways, LLMOps tools, cloud AI platforms and local inference runtimes. ClusterAI is not trying to replace every one of them. It provides the missing orchestration layer that makes internal AI capabilities discoverable, governable and operable.
AI search & enterprise assistants
- Centralized search and assistant experience
- Strong connectors and enterprise knowledge UX
- Focus on a single front-door product
- Routes between heterogeneous AI capabilities
- Not bound to a single assistant experience
- Can complement existing assistants as a router
Private chat & self-hosted interfaces
- Self-hosted chat with local models
- Document interaction and basic RAG
- Single front-end experience
- Sits behind or beside these interfaces
- Adds capability-aware routing and governance
- Operates a network, not a single chat
RAG, agent & workflow builders
- Rapid AI app and workflow creation
- Strong DAG / agent tooling
- Per-app governance
- Turns applications into routable nodes
- Cross-app routing and policy enforcement
- Network-level governance
LLM gateways & API routers
- Unified model APIs and budgets
- Fallback and logs at the model layer
- Endpoint-level routing
- Routes by business capability, not endpoint
- Considers data zone and conversation
- Designed for internal AI service operation
LLMOps & observability
- Traces, evaluation, prompt monitoring
- Retrieval and model observability
- Read path on AI behavior
- Control path: decides where the request goes
- Complementary to monitoring tools
- Observability can enrich ClusterAI capabilities
Cloud AI platforms
- Cloud model catalog and MLOps
- Enterprise agents and tooling
- Cloud-first deployment
- Private multi-capability layer
- Spans local, private and cloud nodes
- Designed for tenant-owned infrastructure
Local inference runtimes
- Serve models locally and efficiently
- Stream tokens and use GPU well
- Operate at the model layer
- Decide which capability should answer
- Sit above runtimes, not replace them
- Designed to work with these runtimes
Internal enterprise stacks
- Custom Kubernetes, scripts, proxies
- Maximum control and custom fit
- High operational burden
- Standardizes capability declaration
- Routing, governance, fallback, observability
- Reduces glue code across the AI stack
A load balancer sees servers. ClusterAI sees AI capabilities.
ClusterAI does not replace the AI stack. It makes the stack usable as an enterprise service.
Ready to turn AI prototypes into an internal AI service?
Your AI is the engine. ClusterAI is the operating layer that makes it usable across the enterprise.