Enterprise AI Layer (REL-1)

The challenge with traditional AI

Data fragmented across silos and rigid schemas.
Business context lost during data processing.
Black-box models with limited explainability.
High compute cost and complex data pipelines.

What REL-1 does

REL-1 organises data by relationships, relevance and meaning rather than by folders and schemas. It rebuilds the context that processing strips out — without moving or duplicating your data — and sits as a contextual layer in front of your models, so AI systems reason with context rather than with raw records.

How it strengthens our AI & ML work

Context-aware unification

Connects enterprise, cloud, IoT, SAP and legacy systems into one intelligence layer.

Explainable and auditable

Every insight is traceable and compliance-ready — no black box.

Low compute, high efficiency

Reduces reliance on heavy model training and large-scale data duplication.

Augments, no rip and replace

Works alongside existing ML models, generative AI, AI agents and analytics pipelines.

The platform

REL.ODE

APIs and SDKs for AI/ML integration.

REL.DOT

Universal data connectors.

REL.AHA

Contextual discovery and pattern detection.

REL.LOT

Experimentation and AI innovation sandbox.

Value to the enterprise

Smarter, explainable and scalable AI.
Faster proofs of concept and enterprise deployments.
AI systems built on context, trust and sustainability.

Privacy and security

Access control is role-based with LDAP integration, and anonymisation can be applied at the element, system or role level. CloudNine AI states that REL-1 is classified by the US Department of Defense at Impact Level 6 (IL6), and is capable of fully private, on-premise deployment for highly sensitive data.

REL-1 is built with CloudNine AI. Product capabilities and the IL6 classification are as described by CloudNine AI.

Ask about the Enterprise AI Layer