AI on your terms — Greater control. Enhanced security. Smarter economics.

One endpoint

<p>An industry-standard, OpenAI-compatible API. Existing tools and code work unchanged — only the destination changes.
</p>

Any model

<p>Open-weight models on RE:AI infrastructure, and closed-weight models from Anthropic, OpenAI, Bedrock and Azure — resold under one contract.</p>

Governed by default

<p>Every request is authenticated, entitlement-checked, rate-limited, metered and attributed before it ever reaches a model.</p>

Billed per token

<p>Consumption pricing with our margin applied. No idle GPU on the customer's bill, no reconciliation at month end.</p>

Multi-tenant

<p>Organisations, teams, users and keys. Onboarding customer number fifty is a row in a table, not a new deployment.</p>

Sovereign-ready

<p>Runs in-country on our own infrastructure. Data residency becomes an entitlement we enforce, not a promise we make.</p>

Custom routing policies

<p>Set routing rules around enterprise needs including model, cost, performance and sovereignty requirements.</p>

Knowledge assistant​

<p>Find trusted answers faster.</p>

Customer service copilot

<p>Resolve customer queries faster.</p>

SOC security copilot

<p>Investigate threats faster,</p>

Developer productivity

<p>Accelerate coding and troubleshooting​.</p>

Sales + marketing

<p>Create and personalise at scale.</p>

How do we avoid being locked into one AI model?

<p>Access and switch across multiple models.
</p>

How do we keep AI costs under control as usage grows?

<p>Route by cost and manage token consumption.</p>

How do we scale AI without rebuilding integrations each time? 

<p>Integrate once, then add models and applications more easily.</p>

How do we keep AI access and usage governed? 

<p>Apply centralised policies and controls.
</p>

How do we choose the right model for each workload?

<p>Route by performance, cost and workload requirements.</p>

Before

<ul> <li>Integrate and manage multiple model providers</li> <li>Provision and manage GPU capacity</li> <li>Make infrastructure decisions for each workload</li> <li>Apply one infrastructure approach across workloads</li> <li>Apply one infrastructure approach across workloads</li> <li>Track AI usage across separate environments</li> <li>Carry more of the technology refresh burden</li> </ul>

With RE:AI TaaS

<ul> <li>Access supported AI models through one managed service</li> <li>Consume managed AI compute through tokens</li> <li>Match infrastructure to workload requirements</li> <li>Choose sovereign or cost-optimised options by workload</li> <li>Change models with less application rework</li> <li>Centralised consumption visibility and controls</li> <li>RE:AI manages the service as models and infrastructure evolve</li> </ul>