Designing AI systems

Ensure Controllability & Transparency from the start

Many of the legal, business, ethical and social concerns about Artificial Intelligence (AI) derive from the “black-box” nature of the decision-making structures and logical pathways of AI-based systems. Opaque decision-making architectures enhance a feeling of mistrust across all levels of an organization, from the engineers who design and build the AI- system and all the way up to the C-suite.
So, how can we ensure that we govern our AI-systems in a way that:
  • Encourages the human-in-the loop principle
  • Defines and selects the proper (quality) characteristics of an AI-system to be tested and monitored (i.e. Controllability, Correctness, Fairness, Transparency, Robustness and others)
  • Ensures the appropriate KPIs are being used for measuring the selected characteristics of an AI-system.
Our experienced team of high-quality machine learning engineers and advisors can fully support you to design trustworthy AI systems and will provide support to teams in identifying potential issues ahead of time and helping them deliver the expected business value.

Implementing AI systems

Be trustworthy,
yet on-time

Even though the AI-based technology is becoming more prevalent and gaining more popularity in business, it still faces significant challenges especially towards its trustworthiness. Our experience indicates that AI-systems’ characteristics such as governance and controllability, data privacy and security, bias, transparency and robustness are typically an afterthought as the priority for the system is its time-to-market. To make matters even more interesting, all these characteristics must be combined with the urge for an AI-system to fulfill its business case.
Our expert team will help you overcome your AI-based implementation challenges, by adopting a step-by-step strategic approach in combination with our PyThia platform. The provided insights from PyThia put in context by our advisors will help your team during the course of an AI system’s implementation to find the right balance between its trustworthiness and time-to-market ensuring a safe and timely deployment in Production.

Operating AI systems

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The true power of AI systems comes from their capabilities to learn and improve. However, they cannot do it without meaningful feedback from the environment. Having a holistic diagnosis and fixes is critical in an AI’s system lifecycle in order to avoid major setbacks in its operation.
Our long-standing expertise in assessing large-scale software systems combined with the results of PyThia, our AI tool, gives us the capability to monitor and audit the Trustworthiness of AI-based systems when in operation. At constant intervals, PyThia can perform analyses, manifest alerts for potential issues and generate those explanations required for alleviating the “black-box” issue. These alerts and insights combined with our team’s actionable advice will help you to improve the quality of your AI solution, enhance adoption and keep your team at peak performance.

Acquiring AI systems


It is evident that investing in an AI company, one invests in the AI asset(s) that depends on it. Thus, the success of a good investment depends on the right insights about those assets at the right time especially during the lifecycle of a transaction.
At code4thought, we are able to provide the transparency investors need in order to drive value in every stage of a transaction’s lifecycle. As we would like to say, our team is committed to helping companies incorporate the best of what a target firm can bring while avoiding the pitfalls that can crush the value or cripple the integration of an acquired software asset.