Platform
A provider-neutral AI Assurance Operating System.
Sovereign deployments, government programmes and regulated enterprises cannot depend on a single vendor SDK. Mindgraph is built as a layered, ecosystem-neutral architecture that works with any model, any agent framework and any deployment topology.
- Deployment
- Cloud · Sovereign · On-prem
- Models
- Any provider
- Core
- Assurance Ontology
- Auth
- SSO / RBAC
Semantic core
The Assurance Ontology is the organisational asset.
AgentRegistry · RiskTaxonomy · TestLibrary · FindingLineage · RegulatoryMappings · BenchmarkIndex. Every layer reads from and writes to the same semantic core, which is what turns scattered test output into institutional memory.
Because the ontology models the enterprise's own AI estate, it cannot be swapped out with a vendor. That is the difference between a report and a system of record.
AgentRegistry
Every agent, version, owner and deployment surface
RiskTaxonomy
A single shared vocabulary for AI risk across the enterprise
TestLibrary
Versioned tests, golden datasets and adversarial patterns
FindingLineage
Immutable trace from test to finding to fix to certificate
RegulatoryMappings
Controls crosswalked to every applicable framework
BenchmarkIndex
Cross-enterprise benchmarks that improve with the network
Layered architecture
Seven layers around one semantic core.
Recursive intelligence
Apps
Governance
Risk engine
Execution
Intelligence
Integration
All layers read & write the Assurance Ontology
Reference stack
No single-SDK dependence.
Applications sit on a provider-agnostic SDK layer, routed through a gateway, executed by an agent runtime, connected through MCP and tool integration — with Mindgraph assurance services underneath, above the model providers.
- 01AI applications
- 02Provider-agnostic SDK layer
- 03AI gateway & model routing
- 04Agent runtime
- 05MCP + tool & data integration
- 06AI Assurance services (Mindgraph)
- 07Model providers
Comparison
Why the platform beats the point tool.
The same dynamic that decided CRM and analytics is now playing out in AI governance. Point tools answer a question; platforms accumulate leverage.
| Dimension | Point product | Compliance SaaS | Mindgraph platform |
|---|---|---|---|
| Audit trail | PDF report only | Process audit only | Immutable technical lineage |
| Regulatory mapping | None built in | Questionnaire-based | FEAT · MAS · EU AI Act · NIST |
| Cross-agent view | Agent by agent | Inventory level only | Enterprise-wide intelligence |
| Agent security testing | Limited | None | 1,000+ adversarial patterns |
| Network effects | None — isolated data | Weak — shared templates | Cross-enterprise benchmarks |
| Switching cost | Low | Medium | High — the ontology is your asset |
Next steps
Bring your hardest AI system. We will assure it.
Technical deep-dive on your live agents, an 8–12 week pilot with agreed success metrics, and partnership or investment conversations.