Built in Birmingham for sectors where control, safety, and evidence matter.
Why RGBY exists
RGBY was developed from direct experience of compliance failure in regulated sectors. Not from academic theory, but from live operational environments where existing tools failed to detect deterioration, evidence response, or maintain control.
The platform began in social housing, where damp and mould compliance exposed fundamental weaknesses in how organisations monitor conditions, identify causation, and evidence their response. The Glaser method — the standard approach to condensation risk — was found to be insufficient for real-world conditions.
From that starting point, RGBY developed a deterministic reasoning approach that applies across any sector where compliance, safety, and auditability are non-negotiable.
The team
Gerald Manton
Strategic Development
Strategic development, product direction, and commercial positioning. Responsible for the Narrowhouse development — a 29-storey residential tower in Gas Street Basin, Birmingham, now being converted to the Birmingham Institute of AI.
John O'Leary
Operations & Product
20 years across social housing and major construction delivery. Head of operations delivering large construction frameworks. Identified the Glaser method failure, developed the RGBY approach, and hardened it for cross-sector deployment.
Hadley Christoffels
Board Member
Founder of dataDecisions.ai and author of 'The Architecture of Reality', bringing data-architecture and applied-AI depth to RGBY's work on structured, causal intelligence.
David Marriage
Board Member
Enterprise transformation and AI innovation leader with deep experience in regulated financial services, and a former PwC partner.
Jon King
Co-Founder
Co-founder leading RGBY's work on AI safety, governance, and cognitive-risk systems — focused on building provable assurance into how the platform reasons about and evidences its decisions.
Patent protected
RGBY's core technology is patent-protected under references A16626GB and A16512GB. The patents cover the deterministic reasoning approach, the CNVF vector engine architecture, and the structured compliance monitoring methodology.
What we believe
- Compliance should be evidenced, not assumed.
- Monitoring should be continuous, not reactive.
- Outputs should be deterministic, not probabilistic.
- Evidence should be structured, not narrative.
- Control should be maintained, not hoped for.
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For partnerships, pilots, technical discussions, and sector conversations.
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