Map and mitigate your AI Risk
Ensure your AI models, systems, applications, and infrastructure are sustaining your growth by assessing risks and performance – from development to production.
Know exactly where
your AI stands
Assess proactively identifies issues with AI data quality, model design, compliance and cybersecurity, and threats to business performance – while giving you expert recommendations for mitigating each issue.
Align stakeholders with business goals
Validate AI initiatives in line with business goals, and set the right KPIs for measuring AI performance and efficiency across the board.
Ensure regulatory compliance
Make sure there are no roadblocks on your business’ path to growth by ensuring your AI initiatives are scaling compliantly in line with regulations.
Identify and mitigate risks & performance
Discover existing and upcoming issues across your AI models, systems, applications, and infrastructure. And get expert advice on how to fix them.
A proven framework for assessing risk & performance
Assess uses our proprietary AI Impact Assessment (AIIA) framework which ensures your AIs are reviewed in a methodical manner – taking into account the far-reaching business impact of AI automated decision systems.
Assess covers the following elements of AI through in-depth questionnaires, tools and visualizations
Data Dimensions
Assuring data quality and accuracy, as well as accuracy of data sources
LLM Query Logging + Embedding
Semantic cache for LLM queries where LLM services might exhibit slow response times, especially when dealing with a significant number of requests
Stakeholder Communication
Evaluate stakeholders understanding of the benefits and risks of AI models, building trust and transparency and improving overall alignment with the business
Data Shifts
Monitoring structural and generative drifts in data and concept through measures and techniques of observability
Model Explainability
Evaluate stakeholders understanding of the benefits and risks of AI models, building trust and transparency and improving overall alignment with the business
Model Selection
Analyzing the reasons for creating each model and understanding their overall concept and design
Infrastructure Integrity
Reviewing the design and implementation of the tech infrastructure supporting development, version control, ongoing monitoring and continuous integration and delivery
Standardization
Evaluate AI interactions and ensure consistency across different models and use cases, making it easier for everyone to understand what to expect and how to use AI
Alignment with Business Needs
Ensure that AI models are up-to-date and aligned with changing business needs, reducing the risk of disruption and facilitating smoother change management
Research Assumptions
Assuring the reasonableness of model assumptions and data selection assumptions
