VOICE
Your Data. Your Know‑how. Any Agent. Any LLM.
Enterprise AI doesn’t need another data silo. It needs governed access to existing knowledge, persistent memory, and the ability to act.
VOICE (Virtuoso Open Interoperable Context Engine) combines Virtuoso, OPAL, and LOAM (Linked Open Agent Memory) into a loosely coupled infrastructure for AI agents. It doesn’t require enterprise data, AI agents, or LLMs to converge on a single proprietary platform: it connects them through open standards, shared semantics, and fine-grained governance.
Your enterprise already has Data Spaces. Your AI agents should work across them, not require their replacement.
Most AI-ready data platform strategies bring data, analytics, Knowledge Graphs, and AI capabilities closer together within one ecosystem. VOICE starts from a different premise: existing systems stay where they are, and agents reach them through open interfaces.
What each layer holds
Read top to bottom: people and agents ask, OPAL orchestrates, memory recalls, Virtuoso harmonizes, and the Data Spaces stay authoritative.
AI Agents & Users
Who and what asks.
- Human UsersAnalysts, domain experts, knowledge workers
- AI AgentsClaude, ChatGPT, Grok Bot, Muse, and others
- Agent FrameworksLangChain, AutoGen, CrewAI, custom harnesses
- LLMsGPT, Claude, Gemini, Grok, Muse, and others
OPAL — Orchestration Harness
Turns intent into governed action.
- Natural Language InterfaceIntent understanding and planning
- Skills and Tool RoutingLoosely coupled Agents, Skills, Tools and Data Spaces
- LLM RoutingCost-based model selection
- Policy and GovernanceIdentity, ABAC, guardrails and audit
LOAM — Entity Relationship Graph Collective
Linked Data memory that accumulates across sessions.
- Session GraphConversations, plans, goals
- Entity GraphPeople, organizations, products, assets
- Task GraphActions, tools, workflows
- Trace GraphExecution traces, provenance
- Domain GraphsSubject-specific knowledge (e.g., contracts, compliance)
- Preference GraphUser and agent preferences
- Additional graphsFurther domain-specific graphs
Virtuoso — Multi-Model DBMS
The semantic harmonization (virtualization) layer.
- Data FederationConnect and query across heterogeneous Data Spaces
- Semantic HarmonizationUnified view using RDF, ontologies and Linked Data
- Multi-Model SupportRelational, RDF, property graph and files
- Standards-Based AccessSQL, SPARQL, GQL, GraphQL, HTTP, ODBC, JDBC, WebDAV, etc.
Enterprise Data Spaces
Your data remains where it is.
- Relational DatabasesPostgreSQL, MySQL, SQL Server, Oracle, etc.
- Knowledge BasesWikis, document repositories, content management systems, enterprise knowledge bases
- FilesystemsDocuments, PDFs, reports, logs, media
- APIsMCP tools, OpenAPI services, REST/SOAP, enterprise applications
- Other SourcesIoT, data lakes, streaming data, etc.
How the capabilities work together
The distinction isn’t any single feature. It’s how these capabilities work together without tight coupling to one database, one agent framework, one LLM, or one proprietary ecosystem.
| Capability | VOICE approach | Enterprise benefit |
|---|---|---|
| Data access | Federated, standards-based access across existing Data Spaces | Less migration and duplication |
| Semantic context | RDF, Linked Data, ontologies, and semantic data virtualization | Consistent entity identification and relationship discovery |
| Agent orchestration | Loosely coupled Agents, Skills, Tools, and Data Spaces | Freedom to change agent frameworks and tools |
| LLM selection | Cost-based routing across supported models | Greater flexibility and cost control |
| Identity and governance | Verifiable identity (YouID) and fine-grained ABAC | Governed access for humans and agents |
| Persistent memory | RDF-based session history, relationships, and provenance | Reusable knowledge across interactions |
| Execution traces | Machine-computable records of relevant agent activities and outcomes | Auditability, debugging, and reproducibility |
| Progressive enrichment | New findings and relationships incorporated into persistent context | Knowledge that accumulates rather than disappearing between sessions |
Enterprise contract intelligence
“Which customer contracts are affected by a supplier’s new compliance requirements, and what actions should we take?”
The next agent session doesn’t have to rediscover everything from scratch. Knowledge accumulates. Context improves. Actions remain governed.
An architectural choice, not a replacement
VOICE complements the data, analytics, graph, and AI platforms an enterprise already runs, as well as offering an alternative to tightly integrated AI context stacks. Rather than making a particular platform the center of enterprise AI, it emphasizes six kinds of independence.
What VOICE is designed to do for an enterprise
- Reduce integration costs by reusing existing systems and open standards.
- Reduce AI operating costs through selective LLM routing and reusable context.
- Reduce repeated work by preserving relevant session histories, execution traces, and accumulated knowledge.
- Improve agent effectiveness by combining current enterprise information with progressively enriched semantic context.
- Strengthen governance through fine-grained control over data access, memory retrieval, and action execution.
- Protect existing investments by avoiding unnecessary migrations and platform dependencies.
Most AI platforms help agents work with data. VOICE is designed to help agents work across Data Spaces, remember what they’ve learned, and execute governed actions using that knowledge.
From intent to governed action
Context that grows more valuable over time.