The more critical the operation, the higher the cost of AI without governance.
Financial institutions, insurers and public bodies handle large volumes of sensitive data and processes that do not tolerate errors, rework or improper exposure of information.
Sensitive data outside the perimeter
Public SaaS AI used for tasks involving critical institutional data, with no sovereignty or traceability over where the information travels.
Scattered knowledge with no standard
Each professional uses a different tool and a different criterion to analyze processes: no consistency, audit or institutional governance.
Document volume and operational rework
Large volumes of underused data, recurring manual analysis and long response times in processes that could be safely accelerated.
Individual productivity and institutional intelligence solve different problems.
Tools like ChatGPT, Copilot or Gemini speed up everyday tasks. But when the process involves critical institutional data or scales across teams, the answer is another layer: Enterprise AI.
| SaaS AI | Enterprise AI | |
|---|---|---|
| Individual productivity | yes | no |
| Content creation and public research | yes | no |
| Uses institutional data and criteria | no | yes |
| Intelligent agents and enterprise processes | no | yes |
| Governance, audit and traceability | no | yes |
| Strategic and sensitive data (Brazil's LGPD) | no | yes |
| Institutional scalability | no | yes |
SaaS AI
- Individual productivity
- Content creation and public research
Enterprise AI
- Uses institutional data and criteria
- Intelligent agents and enterprise processes
- Governance, audit and traceability
- Strategic and sensitive data (Brazil's LGPD)
- Institutional scalability
The secret is not choosing one over the other. It is SaaS AI + Enterprise AI, each for its own purpose, and the second layer is what Vibe builds on Red Hat infrastructure.
Red Hat provides the foundation and Vibe builds the institutional intelligence with specialized services.
RHEL AI and OpenShift AI are not the end product; they are the infrastructure that ensures your Enterprise AI runs securely, portably and fully supported within your perimeter.
Large and small models coexist depending on the case: not every task needs the largest model available, and smaller specialized models usually cost less and perform better within a domain.
Assessment and use cases
We map processes, available data and business priorities to define where AI creates real value.
- Assessment of use cases, processes and available data
RHEL AI
We deploy the base with Granite models and customization through InstructLab, within your data perimeter.
- RHEL AI and OpenShift AI as the foundation
- Large and small models, depending on the case
OpenShift AI
We put training, serving and monitoring pipelines into production, with governance from day one.
- Agents connected to enterprise sources
- MLOps: training pipelines, serving and monitoring
- Audit trail and access control
Ongoing support
A Vibe squad tracks the model's performance, costs and evolution, backed by official Red Hat support.
- Ongoing support: model performance, cost and evolution
AI is not a standalone tool. It is institutional intelligence.
Within the perimeter
Critical institutional data stays where the institution is accountable for it. Sovereignty and traceability come before choosing the model.
The criteria belong to the institution
The value is not in the generic model, but in the organization's own data and rules applied to a process that already exists.
Governance from day one
Audit trail and access control are requirements in regulated processes. Adding them later costs more and convinces less.
Ecosystem
- Red Hat AI Platformwhat Vibe delivers on Red Hat →
- RHEL AIOpenShift AIGraniteInstructLabOpen and hybrid architecturesPython
Before the conversation starts
Does Enterprise AI replace the SaaS AI tools we already use?
No. They coexist. SaaS AI remains useful for individual productivity and content creation. Enterprise AI comes in where there is critical institutional data, regulated processes and a need for auditing, functions a public tool was not designed to cover.
Do we need to containerize every application to use this foundation?
No. You can start with the generative AI workflows in containers, keep the rest of the environment as it is, and move forward with containerization at your own pace, without stopping critical operations.
Does this work 100% on-premise, for data sovereignty and LGPD reasons?
Yes. RHEL AI and OpenShift AI were designed to run on-premise, in hybrid cloud or multicloud, with no hardware or cloud lock-in. Institutional data stays under your organization's control, with a complete audit trail.
How long does it take from assessment to the first model in production?
It depends on the complexity of the data and of the prioritized use case. The typical path goes through an initial assessment, followed by deploying the foundation and a first use case in production. The exact schedule is defined together during the assessment stage.
Do we license directly from Red Hat, or does Vibe deliver everything?
As a Red Hat Premier Partner and Brazil's first AI Platform specialist recognized by Red Hat, Vibe handles licensing, deployment, integration and ongoing support under a single contract, with official Red Hat support behind the entire operation.
Where does AI fit for you?
Tell us the challenge. We can help assess, build, modernize or sustain the system that keeps your business running.
Talk to Vibe →