Product · Institutional Artificial Intelligence
Private Institutional AI
From using Artificial Intelligence to building your own AI capability.
We design and implement an AI platform under the organization’s control, powered by its own knowledge and authorized data, complemented by open-source models and supported by dedicated GPU infrastructure and internal human capabilities.
Let’s discuss your institutional AIYour own capability
It is not just a chatbot. It is intelligence infrastructure.
The organization builds a capability that can access its institutional knowledge, assist its teams and evolve toward increasingly specialized use cases, while maintaining control over the architecture, information and future development.
Product Architecture
Four capabilities built together
Institutional Knowledge
Documents, procedures, knowledge bases, authorized data and institutional expertise prepared for use by AI.
Your Own AI
A private architecture with RAG, vector databases and open-source models such as Llama or DeepSeek, configured for the organization’s needs.
GPU Infrastructure
Design, sizing and configuration of GPU servers for local inference, institutional knowledge and progressive growth.
Trained People
Practical training so teams and leaders can understand, use, validate and develop new applications on top of the installed capability.
Private Infrastructure
The model runs on dedicated computing capacity.
We configure local or private GPU infrastructure suited to the initial scope of the project. The architecture can begin with a high-performance workstation and evolve toward higher-capacity configurations.
The goal is not to oversize hardware, but to create a controlled, scalable technology foundation aligned with the institution’s use cases.
How It Works
From institutional knowledge to usable AI
and data
base
vectors
model
AI
and processes
People + Technology
The capability does not end at the server.
Implementation includes workforce training, area-specific workshops, identification and prioritization of use cases, knowledge preparation, user testing and capability transfer.
The institution does not simply receive a platform: it develops internal expertise to use it, supervise it and continue evolving it.
Implementation
A Progressive Evolution
Scope, infrastructure and duration are tailored to each organization. The first stage aims to install a functional, validated capability that can later grow toward more advanced applications.
What We Deliver
An institutional foundation to build upon.
The result combines a private AI architecture, prepared institutional knowledge, prioritized use cases, configured infrastructure, trained users, controlled testing and a roadmap for future stages.
The platform can evolve toward specialized domains, integrations with internal systems and higher-capacity models as data, use cases and institutional maturity grow.
Outcome
From consuming AI to developing your own AI capability.
An organization with the infrastructure, knowledge, people and technology needed to build and control its own evolution in Artificial Intelligence.
Let’s discuss your project