Illuminate AI's layers, from black-box to crystal-clear
We consult in explainable AI, from explaining black-box systems to benchmarking agent behaviors, to reduce risks and deliver insights you can trust.
Why Explaining AI Matters
Transparency, trust, and accountability separate reliable AI systems from risky ones. 40% of firms cite poor explainability as a major barrier to AI adoption [🔗 McKinsey 2024]. Physicians rank explainability as the top factor in trusting AI in healthcare [🔗 Nature 2024]. And technically unreliable models can breach regulations and incur severe penalties—up to 3–7% of turnover [🔗 EU AI Act 2024].
At the same time, there is no silver bullet for AI explainability. Different techniques rely on different assumptions and approximations, making proper use non-trivial: misuse can lead to overconfidence, confirmation bias, and harmful interventions.
At Unlayer AI, we help you understand your models from the development stage onward, replace unnecessarily complex black-box models with transparent alternatives, and build tracing and evaluation suites to monitor your AI systems—whether you use tree ensembles, deep neural networks, or multi-agent systems.
Our Approach
1
Context Matters
We propose algorithms tailored to your real-world context—factoring in who’s affected, what data you have, your model’s purpose, and how much autonomy it needs.
> Clear mapping between risks and mitigation strategies.
2
Simplicity First
We don’t overcomplicate things. We assess whether simpler, more modular, and more generalizable solutions can get the job done before resorting to complex black-box models.
> Faster implementation, easier debugging, and clearer value.
3
Lasting Impact
We use accessible tools, document our work, and provide you with knowledge to stay in control as your data and systems evolve over time.
> Long-term reliability and maintainability.
Our Services
We are specialized in technical implementation of responsible AI solutions, focusing on explainability, interpretability, and compliance. Our services include:
Model Interpretability
Uncover how AI systems come to their decisions or replace black-box models with inherently interpretable ones.
- Feature attribution & counterfactuals
- Rule-based & symbolic models
- Causal machine learning
- Multi-agent tracing systems
Fairness & Regulations
Identify & contain risks to safeguard your users and minority groups, addressing regulations such as the EU AI Act.
- Implementation of risk mitigation
- Fairness checks & bias detection
- Synthetic data for enhancing privacy
- Continuous monitoring frameworks
Explainability Training
Empower your team with the knowledge to build, maintain, and communicate about explainable AI systems.
- Technical workshops
- Executive briefings
- Documentation templates
- Scientific paper co-authoring
Get In Touch
Have questions about explainable AI or want to discuss how we can help your organization? Reach out to us using the form or contact details below.