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Agentic AI Platform
With the Agentic AI Platform, Your AI, in your company. Private. Centralized. With your own look and feel. Move beyond experimenting with AI and start using it in your business, with full control.
Agentic AI, configured with your data, according to your rules, and within your environment.
Uncontrolled AI: The Invisible Risk Within Your Company
AI is already being used, but often without a common framework. The challenge is not to adopt it, but to do so in an organized way to generate real and scalable impact.
THE (REAL) PROBLEM
Your company is already using AI.
But not in the way you think.
It’s being used like this:
No offense intended.
But it’s happening.
WHAT’S AT STAKE
It’s not just a mess. It’s a real risk:
And the worst part is: you have no insight into any of this.
The Inconvenient Truth
Public AI isn’t designed for your company:
Your team is more productive… but your company is losing control.
Your company doesn’t have an AI adoption problem.
It has an AI control problem.
Three Steps to Stop Experimenting and Start Trading
An environment where your company moves from testing AI to actually implementing it.
Operate AI. With control.
How to Get Started (Your Way):
You choose how to proceed:
From Demo to Production
It doesn’t end with the workshop. It’s just the beginning.
You have everything you need to move forward:
Result
It’s not about using AI. It’s about working with AI.
It’s not cloud-based AI.
It’s AI running on YOUR AWS, under YOUR rules.
This is just the beginning.
The real difference lies in moving from the demo to a production environment.
FAQS
Quick answers to your questions
Where does the solution flow?
The solution runs 100% on AWS.
- It is displayed within the customer’s account
- Use native AWS services
- It maintains full control over data, access, and operations
This ensures security, scalability, and compliance by design.
Which AWS technologies are involved?
The architecture is based on native AWS services, including:
- Amazon Bedrock (AgentCore / models)
- Knowledge Bases
- AWS Messaging (integrations such as WhatsApp)
- AWS Lambda / APIs
- CloudFormation (automated deployment)
- Identity and Security Services
What is the technical setup like?
Deployment on AWS is performed using automated templates (e.g., CloudFormation):
- Installation in Minutes
- Standard Configuration
- Reproducibility Across Environments
It can even be deployed as a “one-click deploy” on the customer’s account.
Where is the data stored and processed?
The data is stored and processed within the customer’s AWS environment.
- They are not sent to public platforms
- They are not exposed outside the defined environment
- They are governed by their own security policies
What level of control do I have over data and access?
In summary, using native AWS capabilities:
- IAM (roles and permissions)
- Access Control by Agent and Service
- Audit and Traceability
- Integration Governance
How does it integrate with our current systems?
Via:
- APIs
- MCP Servers (Model Context Protocol)
- Connected Services within AWS
The integrations that have been built can be registered as tools within the agent system.
What architecture does the solution use?
Decoupled Architecture on AWS:
- AI Core (AgentCore, Gateway)
- Data and Knowledge (Knowledge Bases)
- Backend and frontend in the customer’s account
- External integrations via MCP
This allows for scalability without compromising security.
Is the solution multi-tenant or dedicated?
Hybrid model:
- Optimized core (multi-tenant)
- Customer instance in their own AWS account
This combines efficiency with data isolation.
Is it possible to scale up to production without rewriting the solution?
Yes.
The design on AWS allows for:
- Horizontal scalability
- Introduction of New Services
- Gradual evolution without reimplementation
Can it be tested without relying on the entire environment?
Yes.
The workshop allows participants to:
- Run components in isolated environments (e.g., on-premises + AWS)
- Validate concepts
- Then integrate them into the production architecture
What is MCP, and how does it work on AWS?
MCP (Model Context Protocol) allows agents to connect to external tools.
On AWS:
- MCP Servers are being built
- They are registered as “tools” in the Gateway
- Agents consume them dynamically
This enables a modular and extensible architecture.
What do participants receive at the end of the workshop from a technical standpoint?
Functional environment deployed on AWS
Real, Connected Integrations
MCP Server is up and running
Platform ready for production deployment
