We combine rigorous research, transparent validation, and advanced verification algorithms to ensure AI outputs can be trusted in high-stakes environments.
Evidence-grounded verification
Every AI output is assessed against traceable evidence sources, with explicit reasoning steps and confidence levels. This ensures conclusions are not only plausible, but defensible and auditable in real-world use.
Reasoning error detection
Our systems are designed to identify structured reasoning failures such as unsupported assumptions, incorrect generalisation, and misapplied rules, enabling issues to be flagged before they propagate into decisions.
Advancing accuracy and Integrity in AI.
At Logical Metrics AI we are committed to advancing the safe and ethical use of AI by ensuring the accuracy and integrity of every AI-generated output. Built on rigorous validation frameworks, transparent methodologies, and continuous research, our solutions empower organisations to rely on AI with confidence.
By uniting industry-leading expertise with innovative quality-assurance technology, we help clients navigate an evolving digital landscape with tools that are not only trustworthy and responsible, but also at the forefront of AI innovation.
Pioneering a ground breaking approach to fact-checking.
Andrew Jackson
Driven by a personal journey through the complexities of autism and ADHD, I transitioned from a rewarding career in hospitality to the forefront of technology.
Richard Clarke
With his passion for technology, years of experience and unique approach, Richard brings both strategic vision and hands-on execution to Logical Metrics AI.
Gary Dobson
Gary brings over 30 years of senior leadership experience across the global technology sector, with a strong track record in driving commercial growth and scaling high-performing teams.
About Logical Metrics AI
Logical Metrics AI was founded to address a fundamental limitation in modern artificial intelligence systems: the absence of reliable, auditable reasoning.
As AI systems are increasingly deployed in domains such as healthcare, science, policy, and compliance, correctness alone is no longer sufficient. Decisions must be traceable, evidence-grounded, and capable of withstanding scrutiny. Our work focuses on ensuring that AI systems do not simply produce answers, but produce conclusions that are logically sound, appropriately bounded, and transparently justified.
Beyond traditional fact-checking
Conventional approaches to AI safety and quality assurance tend to focus on output filtering, heuristic guardrails, or post-hoc moderation. These methods may reduce obvious errors, but they do not address the underlying problem of flawed reasoning.
Logical Metrics AI takes a different approach. We analyse how conclusions are formed by breaking outputs into verifiable claims, evaluating them against authoritative evidence, and identifying structured reasoning errors such as unsupported assumptions, incorrect generalisation, causal inversion, and missing logical steps. This allows issues to be detected at the reasoning level, rather than after harm has already occurred.
Evidence, transparency, and accountability
Our systems are built around explicit evidence handling and transparent validation. Every verified output is linked to traceable sources, accompanied by uncertainty indicators, and supported by an auditable reasoning trail. This design supports both human oversight and regulatory expectations, making AI systems more suitable for high-stakes and regulated environments.
We place particular emphasis on accountability by design. Rather than treating transparency as an optional feature, we embed it into the verification process itself, ensuring that decisions can be reviewed, challenged, and improved over time.
A foundation for trustworthy AI deployment
Logical Metrics AI is not tied to any single model or application. Our frameworks are designed to operate alongside existing AI systems, providing an independent layer of reasoning verification that can be adapted across domains.
By focusing on reasoning integrity rather than surface-level accuracy, we aim to provide organisations with the tools required to deploy AI responsibly, scale its use with confidence, and maintain trust with users, regulators, and the public.
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