1What does Advai do?
Advai is a company that focuses on how AI fails at different stages of its life cycle. Our mission is not to create AI but to assess and understand its failures in terms of robustness, performance, security, explainability and bias. We offer a range of three core products, which cover Discovery, Workbench and Dashboarding. We help clients mitigate the many ways that AI can fail and guide businesses to improve the trustworthiness of their AI systems.
2Why is Advai unique?
We don't make AI, we break it. Only clients will have access to our specialised library of first party, research-lead and world-leading adversarial and robustness methods. Our team is uniquely qualified to determine AI limits using these approaches. Our compliance and risk aligned frameworks enable a dashboard view that will help non-technical business decision makers comprehend and monitor their AI safety status.
1What are adversarial attacks?
The simplistic way to think of it is that you need AI approaches to test AI systems. Adversarial attacks are techniques used to deceive AI models by introducing optimised input data. These attacks can make the AI model behave unpredictably or incorrectly, leading to failures in performance and security. This enables us to pinpoint vulnerabilities and advise mitigation methods.
2What is robustness?
In AI terms, 'robustness' and 'robust' take a slightly different meaning from the definitions you might be familiar with. Robustness refers to an AI model's ability to maintain performance when faced with adversarial attacks, unexpected inputs, or changing environments. It's worth us highlighting that systems are mostly designed to maximise 'accuracy' scores, which can only be guaranteed at that moment in time and against the testing data. The reality of the real world is that things change and unexpected things happen. A robust AI system is better prepared to withstand such challenges and deliver more reliable outcomes over time.
1Who would benefit from Advai's services?
Organisations seeking to assert greater control, reduce the risk and enhance the safety of their AI systems.
2What is Advai Advance?
Advai Advance is an intensive discovery process, designed to assess and elevate your AI readiness, aligning your systems and priorities to ensure a safe, responsible, and secure AI integration.
3What is Advai Versus?
Advai Versus is our workbench of developer tools. It enables the running of advanced adversarial techniques and the testing & monitoring of your full AI model estate against the full bank of our robustness related metrics.
4What is Advai Insight?
Advai Insight is the non-technical gateway for business decision makers to make sense of their company wide AI status, from robustness, resilience, risk and compliance perspectives. It helps stakeholders align on AI.
1What does the typical journey look like when working with Advai?
The first step is to understand an organisation's AI architecture and operational environment. This also includes a review of any compliance requirements and a determination of risk appetites in relevant use cases. Next, we connect our tooling to your development environment, so our tests can be called in-line and we connect any relevant AI models. Finally, we connect all of this to a customised dashboard designed to provide decision makers a full view of their model estate and make risk and compliance related decisions.
2We have our own AI team, can we use your products oursleves?
Yes, once connected we can teach your internal teams how to run their models through our testing environment.
3At what stage of AI development should we get in touch?
Finding the fault tolerances of a system clearly needs a system to have been developed to begin with. However, 'AI Assurance' is a framework that encompasses organisational ethos, regulatory requirements, risk management implications, MLOps best practices and operational guidelines. A strategic, precautionary approach will help you avoid expensive mistakes and project failures, or worse public or regulatory blowback. Therefore, you should talk to us the moment you decide to invest in AI.
About Technical Details
1Our own data scientists provide assurance, why do we need Advai?
While internal data scientists often focus primarily on optimising model accuracy, our team exclusively focusses on causing, then preventing failure. Our team can at first be perceived as 'homework markers' to internal teams, but this couldn't be further from the truth: we're a specialised unit that gives your internal teams a unique edge in aligning AI systems with your specific business objectives by avoiding failure.
2Security is key to us, what assurances can you provide?
Our Assurance services mitigate AI vulnerabilities to give you protection from data leakage, adversarial attacks, and ensuring that sensitive data remains private. For example, we safeguard against hallucinations in language models and image manipulation in vision models. All of this can be processed behind maximum-security servers (used by MoD UK) on-premise to give you that extra layer of assurance.
3Under what architecture are your products deployed?
Our primary coding environment is developed in Python. We can work with a range of architectures, from on-premises to cloud or even on the edge, depending on your needs. Security is always our first consideration, regardless of the architecture you choose.
4What domains of AI do you focus on?
In general, our assurance services are valuable (and arguably necessary) across practically every application of AI. Our team has expertise across various AI domains including Vision, NLP, Large Language Models (LLMs), Reinforcement Learning (RL), Complex Systems, and Time Series. In complex systems, we manage multiple AI models that work collaboratively, processing multimodal data and generating signals for a second layer of models.
5What are the standards that you test against?
We align ourselves with a variety of international standards such as ISO and NIST, particularly those focusing on risk management. We also incorporate frameworks and standards from the fields of Risk Management, Data Operations, MLOps, and DevSecOps.