RGBY.ai
RGBY is an operational-intelligence platform for regulated sectors — it catches risk and drift early, and turns every call into an auditable, defensible decision, not a black-box guess.
Deterministic AI across housing, maritime, construction, financial services, energy, defence, and AI governance. Same inputs, same answer, every time — with a full evidence trail.
01 Signals in
02 Resolve · 4 channels
03 Verdict
This is the engine, live. The same input vector always yields the same coherence score — so every decision is reproducible and auditable, not a black-box guess. Hover to inject noise and watch it re-resolve, then recover.
The engine, in plain English
The panel above scores a case across four channels — Demand, Packing, Structure, Noise. Below is what each means for a housing case, and the one question they answer: are we sure enough to act — and could we defend that decision?
Flat 14 · possible damp
- Evidence — stronghumidity climbing for 3 weeks, 2 repair requests, a condensation flag at the last inspection
- Agreement — strongthe sensor, the tenant, and the inspector all point the same way
- Grounding — strongfull property and ventilation record on file
- Noise — lowclean, consistent data
Same home — the sensor's been faulty a week and two inspection notes now contradict each other
- Evidence — still concerningthe picture still looks like damp
- Agreement — weakthe two inspection notes disagree
- Grounding — shakya gap where the record should be
- Noise — highthe faulty sensor is feeding unreliable readings
Same facts in, same answer out — every time, and all of it logged. When Awaab's Law or the Ombudsman asks why you did (or didn't) act on this home, the answer is reproducible and defensible — not a black-box guess.
From raw signals to decisions you can defend
One engine behind every sector. RGBY runs on the R-KID protocol — a structured reasoning layer that sits between raw AI and the decisions your regulator will scrutinise.
Detect the signals that matter
RGBY reads the data your operation already produces — records, reports, sensor readings, model outputs — and surfaces emerging risk, inconsistency, and drift early.
e.g. across a social-housing stock, spotting the conditions for damp and mould before a complaint — not after.
Reason under hard constraints
The R-KID protocol applies your rules, standards, and domain knowledge — Constraints · Knowledge · Inference · Detection — to interpret what's happening. Deterministically: same inputs, same conclusion, every time.
No black-box variability — a result you can reproduce and explain.
Evidence every decision
Every output ships with its reasoning, the constraints applied, and a full audit trail — evidence-ready for regulators, boards, and inquiries. Deploy in your cloud or on-prem.
Defensible by design, not after the fact.
Not a black box. Where a general AI gives you a plausible answer you can't justify, RGBY gives you a consistent, traceable one you can defend.
Platform
The platform
A deterministic reasoning and monitoring layer for complex operational environments.
RGBY brings together signal detection, structured reasoning, drift monitoring, and auditable outputs to help teams interpret change, prioritise action, and maintain control in regulated settings.
Learn more about the platformSectors
One engine. Multiple regulated sectors.
RGBY supports monitoring, control assurance, and auditable decision-making across environments where failure carries legal, financial, operational, or safety consequences.
Social Housing
Damp and mould monitoring, resident safety assurance, and evidence-led compliance support.
Maritime
Vessel behaviour monitoring, sanctions exposure, cargo anomaly detection, and route integrity analysis.
Financial Services
Portfolio oversight, control monitoring, decision traceability, and MiFID II / FCA audit support.
Defence
Structured reasoning, procurement integrity, assurance workflows, and decision support in sensitive environments.
Construction
BSA gateway support, HRB compliance evidence, and regulatory control monitoring.
Energy
Grid and infrastructure monitoring, supply-chain visibility, and control assurance in constrained environments.
AI Governance
Structured controls, drift detection, and audit-ready outputs for AI-enabled workflows.
Scope
Signals of scope
4.4M
Social homes in scope
29
HHSRS hazard categories
12K+
High-rise buildings in scope
Deterministic
Auditability layer
Protocol
The R-KID Protocol
A governance layer for AI systems.
R-KID adds structured controls, consistency monitoring, and auditable decision traces to AI-enabled workflows operating in regulated or high-consequence settings.
Constraints
What the system must not do.
Knowledge
The rules, procedures, and structures it must follow.
Inference
How it interprets patterns, signals, and context.
Detection
How it monitors drift, inconsistency, and control failure over time.
Product
DewPoint Guardian
A damp and mould intelligence system for risk, causation, mitigation, and compliance.
Not just detection.
Not just reporting.
A new layer for understanding what is happening, why it is happening, what needs to change, and how the response is evidenced.
People
The team
Gerald Manton
Strategic development, product direction, and commercial positioning.
John O'Leary
20 years across social housing and major construction delivery. RGBY was developed from live failures in compliance, safety, and operational control.
Hadley Christoffels
Board member. Founder of dataDecisions.ai and author; data architecture and applied AI.
David Marriage
Board member. Enterprise transformation and AI in regulated financial services; former PwC partner.
Jon King
Co-founder. AI safety, governance, and cognitive-risk systems; building provable assurance into the platform.
Connect
Talk to us
For pilots, partnerships, technical discussions, and sector use cases.
Request BriefingAvailable for technical reviews, sector conversations, and early-stage deployment discussions.