Private AI platform

Company AI without giving up company control.

Deep Conduit combines controlled infrastructure, local models, authorized company knowledge, approved tools, and managed operations into one employee-ready system.

More than a model

The value is in the system around the model.

A DGX Spark, GPU server, or Mac Studio can run a model. It does not automatically connect that model to identity, enforce departmental permissions, ingest company knowledge, supervise tools, or keep the system reliable.

Deep Conduit does not sell hardware as the product.
The product is the secure deployment, orchestration, company knowledge, access-control, agent, integration, and managed-operation layer that makes private AI useful.
Platform layers

Designed as one governed system

Controlled infrastructure

On-premises or controlled-environment deployment using hardware sized for actual workloads, users, context, and performance requirements.

  • NVIDIA and GPU servers
  • Apple Silicon where appropriate
  • Local and open model serving

Secure company knowledge

Retrieval over approved documents and business systems, with current sources supplied to the model at request time.

  • File shares and SharePoint
  • Google Drive and knowledge bases
  • Source citations and refresh pipelines

Identity and policy

SSO, roles, group membership, and source-level permissions determine what can be retrieved before a prompt reaches the model.

  • Department isolation
  • Least-privilege retrieval
  • Audit-ready request records

Employee experience

A simple conversational interface hides model routing, embeddings, GPU management, and infrastructure details.

Agents and workflows

Approved integrations support document analysis, proposals, reports, research, and controlled automation with sandboxing where appropriate.

Evaluation and specialization

Company RAG, curated adapters or LoRA, department-specific assistants, benchmarks, and approved feedback improve fit over time.

Local and cloud

Choose the boundary that fits the work

Private deployment

  • Company-controlled infrastructure
  • Local model inference
  • Custom identity and retrieval policy
  • Greater control over data flow
  • Capacity planned around real use

Public AI subscription

  • Fastest general-purpose starting point
  • Per-user or usage-based costs
  • Provider-defined product boundaries
  • Limited company-specific integration by default
  • Useful where data policy permits

Private AI is not automatically the right answer for every workload. The assessment identifies where local control creates enough value to justify the operational responsibility.

Evaluate a private AI pilot

Choose one workflow, a defined user group, and measurable requirements.

Explore a Pilot →