Safe Intelligence

Safe Intelligence: formal verification for machine learning models

Safe Intelligence brings verification techniques out of the research lab and into ML production workflows. A spinout of Imperial College London, the company was built on technology developed by founder and CTO Alessio Lomuscio, Professor of Safe Artificial Intelligence in Imperial's Department of Computing, with CEO Steven Willmott leading the commercial side. The core idea: standard testing checks a model on the data points you have, while formal verification can prove how it behaves across whole regions of input space — catching fragilities that test sets miss.

The platform is organized around three capabilities. Validate runs deep analysis of model performance, formally verifying behavior against perturbations across deep neural networks, decision trees, and random forests. Robustify analyzes regions of input space, checks for domain shift, and removes identified fragilities — improving fairness and reducing unexpected behavior. Monitor keeps watch after deployment, tracking metrics and alerting on emerging issues.

Key Features

• Formal verification of model behavior under perturbations, going beyond point-wise test-set evaluation
• Multi-model support: deep neural networks, decision trees, and random forests
• Robustification that identifies and removes fragilities, improving fairness and reliability
• Domain-shift checking and regional input-space analysis
• Continuous monitoring with alerts for emerging issues in deployed models
• Spec27, launched April 2026: spec-driven validation extended to AI applications and agents

Ideal Use Case

Safe Intelligence fits organizations deploying ML in safety- or business-critical settings — aerospace, healthcare, finance, and other regulated environments — where "it passed the test set" isn't sufficient evidence for a risk committee. Validation teams and ML engineers use it to produce stronger assurance artifacts, harden models against edge-case inputs, and keep monitoring them once live. With Spec27, that same discipline now extends to validating AI applications and agents against explicit specifications.

How Safe Intelligence differentiates

The company raised a £4.15M seed round led by Amadeus Capital Partners, joined by OTB Ventures and Vsquared Ventures, and its verification methods come directly from Imperial College London's Safe AI research group. Most model-QA tooling is empirical — more tests, more metrics. Safe Intelligence's formal-methods foundation lets it make provable statements about model behavior across input regions, which is a categorically different level of assurance.

FAQ

What model types are supported? Deep neural networks, decision trees, and random forests, with validation for AI applications and agents via Spec27.

How is this different from ordinary ML testing? Testing samples individual inputs; formal verification analyzes whole regions of input space and can prove robustness properties rather than just sampling for failures.

Where did the technology come from? It's a spinout of Imperial College London, based on research by Professor Alessio Lomuscio's Safe Artificial Intelligence group.

Is there a free trial? No public trial or pricing — the company runs demo-led enterprise sales, so contact them for an evaluation.

tl;dr

Safe Intelligence is an Imperial College spinout that validates, robustifies, and monitors ML models using formal verification — assurance for AI in high-stakes environments.

Related

Looking for more options? Browse the Security & Governance directory or read our best AI security tools listicle. Safe Intelligence is also tracked on Crunchbase.