ConservaTech Networks

Private AI Platform & Infrastructure

Control the Infrastructure Behind Your Intelligence.

Run private models, RAG, enterprise search, MCP integrations, and AI-assisted workflows on customer-controlled infrastructure—with governed access to outside models when they provide the right capability or economics.

More than a server. A complete operating environment.

Private AI combines infrastructure, models, knowledge access, integrations, policy, and ongoing operations inside one customer-controlled environment.

  1. Infrastructure

    GPU and CPU compute, storage, networking, identity, security, monitoring, backup, and recovery.

  2. Model hosting

    Private hosting for language models, embedding models, rerankers, extraction models, and other approved workloads.

  3. Private knowledge access

    Private RAG, enterprise search, document ingestion, permissions, provenance, and retrieval evaluation.

  4. Integration

    MCP servers, internal APIs, databases, repositories, business systems, and approved operational tools.

  5. Hybrid routing

    Local-first model routing with provider allowlists, data controls, redaction, fallback, and cost policies.

  6. Managed operations

    Monitoring, updates, model lifecycle management, capacity planning, incident response, and governance review.

Open-source stack

Open from infrastructure through intelligence operations.

ConservaTech avoids unnecessary proprietary lock-in by using open technologies across the private AI stack. Customers retain control over deployment, models, knowledge, integrations, and long-term operating choices.

  1. Infrastructure
  2. Model Serving
  3. Retrieval
  4. Orchestration
  5. MCP and Tools
  6. Knowledge Systems
  7. Observability
  8. ConservOS Governance

Keep sensitive context inside the customer boundary.

Enterprise retrieval, source documents, identity, permissions, and organizational memory remain within the approved environment. Only explicitly permitted context may be sent to an external model.

Private evidence is assembled and filtered inside the customer-controlled environment. Only approved context is sent to an approved external model. The result returns for local validation, required approval, audit, and preservation.

Customer-Controlled Environment

Private preparation

  1. Assemble Private Evidence

    Private retrieval assembles relevant evidence from approved internal sources.

  2. Apply Policy and Redaction

    Data classification, permissions, provider policy, and redaction determine what may leave.

  3. Release Only Approved Context

    Only the minimum authorized context is released for the bounded task.

Only approved bounded context crosses

Approved External Processing

Approved external processing

Approved External Model

An approved provider performs the selected specialized, multimodal, or high-complexity task.

Result returns to the customer boundary

Governed Return

Governed return

  1. Validate Locally

    The result returns to the private environment and is compared with local evidence and rules.

  2. Apply Required Approval

    Human or policy authority determines whether the result may be used or acted upon.

  3. Audit

    The request, released context category, provider, result, validation, and approval are recorded.

  4. Preserve Approved Outcome

    Approved results, decisions, corrections, and outcomes become governed organizational memory.

Deploy inside the boundary your organization requires.

  1. Customer on-premises

    AI infrastructure operates inside the customer environment.

  2. Customer private cloud

    The customer controls the cloud account, network, policies, and data boundary.

  3. ConservaTech dedicated hosted

    ConservaTech operates an isolated environment for the customer.

  4. Hybrid

    Core workloads remain private while approved external models or elastic compute are used selectively.

Private AI is defined by control, policy, isolation, and governance—not by one deployment location.