MIRAGE
Multi-model Inference Reliability & Agreement Gate Engine. The protected architecture is centred on controlled conflict handling, selective re-evaluation and runtime release control.
Research & IP
Built as technology. Protected as technology.
MIRAGE combines a filed patent application, proprietary implementation know-how and a research agenda focused on reliable AI decision systems.
01
Runtime control
Conflict is not only observed; it can activate a different execution path.
02
Selective inference
Additional computation is focused on the dimensions that require validation.
03
Auditable decisions
Relevant executions can retain an inspectable and replayable decision trail.
Research focus
Reliable AI decisions.
MIRAGE research focuses on how multiple AI systems can disagree, appear to agree, and still require controlled validation before a decision is released.
Multi-model divergence
Measuring and interpreting disagreement across heterogeneous AI models.
Apparent consensus
Investigating situations in which agreement may not represent genuine independence.
Decision assurance
Selective inference, model independence, release control and auditable execution.
AI Decision Assurance
A control layer for risk-aware AI decisions.
MIRAGE is a model-agnostic AI Decision Assurance layer. It is designed to verify, compare, document and govern AI-driven decisions across multiple models or agents, with explicit conflict handling, selective routing, validation, auditability and human escalation where required.
The objective is not to claim that AI is always right. The objective is to make the path from inference to action more controllable, inspectable and accountable.
AI governance context
Designed with governance in mind.
MIRAGE is being developed to support auditable and risk-aware AI decision processes. Its research and documentation are intended to engage with major AI governance and risk-management frameworks.
EU AI Act
A regulatory context that increases the importance of transparency, risk management, traceability and human oversight in AI systems.
NIST AI RMF + GenAI Profile
Risk-management references for governing, mapping, measuring and managing AI risk, including generative-AI-specific considerations.
ISO/IEC 42001
A management-system standard for organisations developing, providing or using AI systems.
Positioning note. These references describe the governance context MIRAGE is designed to support. They do not constitute certification, legal compliance claims or conformity assessment.
Collaboration
Research partnerships without exposing the proprietary core.
Validation and benchmarking can be structured around observable behaviour and measurable outcomes while implementation-sensitive logic remains confidential.
Universities
Joint validation, benchmark design and applied research.
Industrial R&D
Domain-specific testing of reliable decision workflows.
Public innovation
R&D programmes, grants and funded technology pilots.
Scientific output
Publications and experiments that preserve confidential know-how.
IP strategy
Patent protection + trade-secret discipline.
Protect what defines the architecture. MIRAGE uses patent protection for the core technical system and retains implementation-sensitive production logic as proprietary know-how.
Thresholds, calibration methods, routing policies, optimisation logic, datasets, prompts and proprietary heuristics are not part of the public disclosure layer.