Reliable AI
at every layer
From model internals to real-world deployment, we design, validate, and improve AI systems for challenging applications.
For challenging and specialized situations
Our approach
We focus on what makes AI succeed in high-stakes settings: systems that are understandable, validated, and right-sized.
- Explainability is cited as the #1 adoption driver by 40% of firms [McKinsey]
- Physicians rank transparency as the top factor in trusting AI [Nature]
- Regulatory compliance demands reliability—the EU AI Act requires it [EU AI Act]
We bring a unique combination of academic rigor and industry experience.
Led by Dr. Marco Virgolin—10+ years across academic AI research and industry leadership, with publications at top AI venues and experience spanning large enterprises and AI startups. 🔗 Learn more about Marco.
Feasibility and Scoping
We ground our advice to your unique context: objectives, constraints, risks, data availability, ROI w.r.t. existing baseline.
Rigor over Hype
We draw from our wide knowledge of AI to advise the right-sized solution: be it classic supervised learning or LLM agents.
From Concepts to Code
We support you end-to-end, from drawing reliability guidelines to implementing highly specialized AI architectures.
What We Do
From high-level requirements to deep technical work, we help you build AI systems that are reliable, understandable, and maintainable for the real world.
AI Strategy & Advisory
Make the right decisions before investing into building the wrong thing.
- Feasibility and risk assessment
- System and model design choices
- Evaluation strategy and success criteria
- Executive briefings and decision support
Reliable & Interpretable AI
Make AI systems understandable, testable, fair, and robust.
- Model interpretability and explainability
- Debugging and failure analysis
- Monitoring and evaluation pipelines
- Human-in-the-loop and traceability design
Advanced AI R&D
When existing approaches fall short, we implement unique solutions.
- Neural and symbolic approaches
- Multi-objective optimization
- Counterfactual and causal analysis
- Tailored domain-specific constraints
Use cases across industries
Helping clients with specialized AI needs — from life sciences to semiconductors
AI-First Biotech
Advising on explainable AI:
- Traceable agents for omics data
- Explainable model discovery
Regulatory Pharma & MedTech
Bringing technical AI expertise:
- Integration and validation of AI
- Evals for regulatory LLM agents
Semiconductor Industry
Improved interpretable AI algorithm:
- 30% KPI improvement
- 20× speed-up
Insights & Articles
Perspectives on building reliable AI systems
Mar 2026
How to Develop Evals for LLM Agents
A pragmatic 9-step playbook for designing evaluation pipelines that help you build, debug, and ship LLM-powered agents with confidence.
Feb 2026
Black-box AI Models: to Explain or not to Explain?
Exploring when post-hoc explanations help vs. when truly interpretable models are the better path — and how to decide for your use case.
Have a challenging AI problem to solve?
Whether you need a second opinion, a technical strategy partner, or hands-on help with a complex system: let's talk.