Platforms and Data
Private Enterprise AI and AI Governance
Build a private AI platform dedicated to your company with tighter control over data, usage policies, internal integrations, and governance.
Scope
We help companies adopt AI with tighter control over data, access, and integrations. Instead of relying exclusively on public platforms, we design private environments dedicated to the business, reducing information-leak risk, reinforcing governance, and improving cost predictability for enterprise-scale usage.
- The company wants to use AI without sending sensitive data to public platforms.
- There is a need to integrate internal documents, technical knowledge bases, or corporate repositories.
- The environment must meet security, audit, or governance requirements.
- Operations want to expose AI capabilities through internal APIs.
When this service makes sense
The company wants to use AI without sending sensitive data to public platforms.
There is a need to integrate internal documents, technical knowledge bases, or corporate repositories.
The environment must meet security, audit, or governance requirements.
Operations want to expose AI capabilities through internal APIs.
There are open questions about GPU, CPU, storage, network, and security sizing.
How we work
Execution combines technical design, validation, and documentation to reduce rollout risk and support later operations.
Assess the current scenario, data sources, and security requirements.
Design the model, vectorization, inference, and integration architecture.
Define the technical rollout plan, sizing, and access segregation.
Execute with validation of flows, policies, and API consumption.
Document the environment and transfer technical knowledge to operations.
What we deliver
Local LLM architecture and orchestration.
Enterprise RAG connected to prioritized internal sources.
Private APIs for enterprise system integration.
Access control, segregation, and technical governance.
Infrastructure sizing for inference and future growth.
Operational documentation, integration notes, and knowledge transfer.
Technologies and integrations
A private AI environment dedicated to the company keeps control over data, permissions, usage history, and internal integrations away from scattered public tooling. That reduces information-leak risk, strengthens governance, and avoids unpredictable cost growth tied to tokens, API calls, or user volume.
Expected outcomes
The outcomes below are expressed as operational and governance criteria typically pursued in this kind of engagement. The final design depends on the environment, constraints, and depth of the work.
References handled under confidentiality
In many engagements, topology details, volumes, integrations, and timelines remain under contractual confidentiality. Even so, the delivery pattern is consistent across critical environments like these.
Operations with restricted change windows
Projects where rollout, migration, or recovery must be executed with risk control, validation, and formal documentation.
Environments with multiple integration layers
Scenarios where networking, virtualization, storage, backup, observability, and access policies need to evolve in a coordinated way.
Infrastructure that demands governance
Work where architecture, segmentation, operational traceability, and technical handover matter as much as the implementation itself.
Related solutions
Other solutions in the same technical domain to expand scope, integration, and operational continuity.
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Frequently asked questions
Common questions that usually come up before a deeper environment assessment starts.
Do we need dedicated GPUs to run local LLMs?
It depends on model size, usage volume, and latency targets. CPU can support lighter pilots, but enterprise environments with multiple users usually require dedicated GPU sizing.
How long does a typical private AI rollout take?
The timeline depends on security constraints, integrations, and baseline infrastructure readiness. We usually start with assessment, design, and a controlled scope before scaling.
Do company data ever leave the internal environment?
The architecture is designed to keep sensitive data inside the perimeter defined by the organization. Any external integration depends on an explicit governance and architecture decision.
Can it integrate with legacy systems through APIs?
Yes. The design usually includes private APIs, queues, or connectors that fit the existing environment while respecting authentication, segmentation, and audit requirements.
Need to assess this environment?
Send a short summary of the current scenario and we will respond with an initial technical approach.