Architecture
High-X is a sovereign AI infrastructure. The difference is architectural.
Core capabilities
- Multi-Node Distribution Distribute AI tasks across all your machines. Load is balanced automatically.
- Routing Engine Routes tasks based on model capabilities, node load, and historical performance. Routing accuracy increases with usage data.
- Cluster Monitoring Real-time health, hardware metrics, and connectivity diagnostics for every node in your local cluster.
- Private RAG Engine Index documents in the customer-controlled environment. For local-only workflows, semantic search and analysis can run without sending embeddings to an external provider.
- Workflow Automation Built-in workflow integration for chaining AI tasks into governed local workflows.
- Coding Agent Integrated development environment with code completion, refactoring, and test generation.
The architecture paradigm
High-X is designed for use cases where the architecture must provide data-flow control, audit evidence, and human oversight rather than only model access.
Durable workflow evidence
Regulated AI workflows need more than an output. They need execution context that can be reviewed later.
High-X persists workflow state and audit events so selected processes can produce reviewable execution histories. The exact retention, integrity, and export model must be validated per deployment scope.
Human oversight in the workflow
For defined high-risk workflows, oversight should be a recorded system event rather than only a process document.
High-X can pause selected workflows for an explicit approval or rejection signal and record identity, timestamp, decision context, and reason. Whether this satisfies a legal obligation depends on the use case and operating process.
The goal is evidence that a reviewer can inspect, not a blanket compliance promise.
Local control by design
AI architecture should make clear where data, models, infrastructure, logs, and external integrations sit.
High-X is built for local execution of selected workflows. External connectors are optional and should be enabled only when the customer's data-flow and security policy allow them.
Compliance mapping
High-X maps to EU AI Act control areas. Legal compliance and certification depend on the concrete use case, deployment scope, documentation, and external review.
| Article | Requirement | High-X Implementation |
|---|---|---|
| Art. 9 | Risk Management | Risk workflow, escalation, confidence tracking, anomaly and drift monitoring where enabled |
| Art. 10 | Data Quality & Governance | Typed validation, isolation for review, lineage support, role-based access controls |
| Art. 11 | Technical Documentation | API documentation, model metadata, architecture and deployment documentation |
| Art. 12 | Logging & Retention | Audit trail, configurable retention, structured retrieval for configured workflows |
| Art. 13 | Transparency | Confidence values on every output, source referencing for RAG answers, decision context visible |
| Art. 14 | Human Oversight | Approval or rejection signal recorded with identity, timestamp, context, and reason |
| Art. 15 | Accuracy & Robustness | Monitoring, baselines, rate limiting, hardening, and input validation where deployed |
| Art. 26 | Deployer Obligations | Deployer configuration tools, risk classification support, operator documentation |
System architecture
Request flow
Configured requests are classified by task type, routed to a suitable node, processed through governance checks, and logged for later review.
Technology stack
| Layer | System Component |
|---|---|
| Inference | Sovereign Inference Engine |
| Data | Semantic Knowledge Index |
| Workflow | Durable Execution Layer |
| Routing | Intelligent Routing Gateway |
| Tasks | Asynchronous Task Distribution |
| Storage | Distributed Metadata & Audit Store |
| Telemetry | Real-time Node Telemetry & Performance Metrics |