AIQURIS evaluates every AI use case in its specific context. Rather than applying generic checklists, it determines which risks, requirements and controls actually matter for that use case by combining its context with applicable governance frameworks, standards and regulations. The result is a transparent, evidence based deployment decision that can be trusted, explained and defended.
AI governance frameworks define what organisations should achieve. They do not determine what a specific AI system requires in its deployment context. That translation is still left to interpretation. AIQURIS bridges this gap with a deterministic reasoning engine that derives deployment specific requirements, defines the necessary controls, and produces a provable deployment decision backed by evidence.
The AIQURIS Methodology transforms your specific deployment context into continuous assurance, systematically turning operational data into audit-ready evidence, and evidence into defensible decisions. Every stage builds on the last, ensuring your AI use cases are evaluated with complete, end-to-end traceability.
You start by describing your AI use case. The AIQURIS platform guides you through a structured assessment, transforming your inputs into a complete deployment context for every subsequent decision.
AIQURIS analyses your deployment context using its proprietary reasoning methodology to identify who could be affected, how impacts may occur and their potential severity. The result is a structured impact assessment tailored to your AI use case.
AIQURIS evaluates your AI use case across the six AI assurance pillars: Safety, Security, Legal, Ethics, Performance and Sustainability. This creates a comprehensive risk profile that reflects the unique characteristics of your deployment.
Based on your deployment context and risk profile, AIQURIS determines which standards, regulations and organisational policies apply. It translates them into deployment specific controls, acceptance criteria and evidence requirements, with every requirement traceable to the risks identified in the previous stage.
AIQURIS evaluates the available evidence, identifies any remaining gaps and assesses residual risk. The result is a transparent, defensible deployment decision, together with the conditions and ongoing monitoring needed to maintain assurance throughout the AI lifecycle.
Every deployment follows the same methodology, but reliable AI assurance requires multiple layers of intelligence working together. AIQURIS combines a Knowledge Foundation, Contextual Intelligence, Proprietary Reasoning, Documents Mapping and Risk Aggregation & Output into a deterministic reasoning architecture that produces deployment specific decisions with complete traceability.
Combines all assessment results into a complete risk profile, evaluates residual risk and produces evidence, reports, contracts and a defensible deployment decision.
Transforms regulations, standards and policies into machine processable requirements and generates the assessment questions needed to evaluate each AI deployment.
Transforms deployment information into structured context, identifying the characteristics, operating environment and organisational maturity required for an accurate assessment.
Applies formal logic, constraints and search algorithms to derive deployment specific requirements, controls and evidence with deterministic, explainable results.
Structured knowledge of AI standards, regulations, policies and risk domains, organized in a knowledge graph that enables system specific reasoning.
Why trusted AI deployment depends on operationalising standards, controls, and evidence for each use case.
AI regulations and frameworks define what compliance looks like, but they cannot tell you if a specific AI system is safe, reliable, and fit for purpose in its actual operating context. To protect safety, quality, and public trust, organisations need an operational layer that translates high-level policies into structured, repeatable processes. This means identifying material risks, specifying controls, and verifying evidence for defensible deployment decisions.
Read our whitepaper to learn how to move beyond static governance frameworks and operationalise trusted AI deployment across your entire portfolio.
Trust does not come from AI alone. It comes from decisions that are consistent, explainable and based on explicit reasoning. Every AIQURIS assessment is evaluated against six universal decision properties.
What must be done under applicable laws, standards, and policies.
What is allowed, for whom, and under what conditions.
Limits and boundaries that shape acceptable decisions.
When rules apply, change, expire, or trigger actions.
What evidence is required, how it is verified, and how decisions are linked.
Every decision is explainable, auditable, and defensible to any authority.
The longer uncertainty around risk, controls, and accountability remains unresolved, the harder it becomes to deploy, govern, and defend your AI with confidence. Start with the next step that fits your situation.
Book a free 30 minute consultation to discuss your AI use case, key risks, and next steps.
Whether you are exploring options, need a quote, or want a second opinion, send us a message and we will connect you with the right expert.
AI governance defines expectations. AI assurance livers the proof. The challenge is translating general frameworks into specific, actionable decisions for your unique AI use case.
Our repeatable methodology transforms your specific deployment context into continuous assurance, systematically turning operational data into audit-ready evidence, and evidence into defensible decisions. Every stage builds on the last, ensuring your AI use cases are evaluated with complete, end-to-end traceability.
Stop generating paper compliance. Start deploying AI with provable assurance.
STEP 01
You start by describing your AI use case. The AIQURIS platform guides you through a structured assessment, transforming your inputs into a complete deployment context for every subsequent decision.
AIQURIS analyses your deployment context using its proprietary reasoning methodology to identify who could be affected, how impacts may occur and their potential severity. The result is a structured impact assessment tailored to your AI use case.
AIQURIS evaluates your AI use case across the six AI assurance pillars: Safety, Security, Legal, Ethics, Performance and Sustainability. This creates a comprehensive risk profile that reflects the unique characteristics of your deployment.
Based on your deployment context and risk profile, AIQURIS determines which standards, regulations and organisational policies apply. It translates them into deployment specific controls, acceptance criteria and evidence requirements, with every requirement traceable to the risks identified in the previous stage.
AIQURIS evaluates the available evidence, identifies any remaining gaps and assesses residual risk. The result is a transparent, defensible deployment decision, together with the conditions and ongoing monitoring needed to maintain assurance throughout the AI lifecycle.
The result? AI risk becomes concrete, measurable, and controllable.
AI governance defines expectations. AI assurance livers the proof. The challenge is translating general frameworks into specific, actionable decisions for your unique AI use case.
Our repeatable methodology transforms your specific deployment context into continuous assurance, systematically turning operational data into audit-ready evidence, and evidence into defensible decisions. Every stage builds on the last, ensuring your AI use cases are evaluated with complete, end-to-end traceability.
Stop generating paper compliance. Start deploying AI with provable assurance.
The result? AI risk becomes concrete, measurable, and controllable.
Every deployment follows the same methodology, but consistent, explainable decisions require more than generative AI. AIQURIS combines a Knowledge Foundation, a proprietary Reasoning Engine and Contextual Intelligence to derive deterministic, deployment specific decisions with complete traceability.
ISO/IEC • DIN • IEEE • ESG
Organisation deploying the AI system
Team or vendor building the AI system
e.g. Straik.ai, test labs
e.g. Credo AI, Holistic AI
e.g. Arize AI, Aporia
Big4, NCS, Nortal
e.g. TÜV SÜD
AI liability underwriting
Regulatory interpretation
