Private LLM hosting
Run models inside your infrastructure with control over sizing, isolation, and release cadence. Tune performance for batch and interactive workloads without exposing weights or prompts externally.
Product
Your own secure AI environment
A cohesive platform for model serving, retrieval, interfaces, and audit—deployed where your data already belongs.
Secure data layer
Ingest, chunk, embed, and retrieve internal knowledge through a private pipeline. Object storage and vector indices remain encrypted and segmented according to your policies.
Internal AI interface
Provide employees and systems with a governed console and API surface. Usage is authenticated, rate-limited, and observable—built for operators, not anonymous web users.
Access & governance
Model and data access follow role and attribute rules. Approvals, segregation, and retention integrate with how your security team already works.
Deployment flexibility
Standardize on private cloud regions or on-premises racks depending on latency, residency, and capital preferences—the software stack stays consistent.
Architecture
How data flows inside your perimeter
A logical view of components as they are typically deployed. Physical topology maps to your standards—this is the control and data path your teams need to reason about.
Users / internal apps
SSO, departments, workloads
Vault Systems interface
Console & developer surfaces
API gateway
AuthN/Z, quotas, policy
LLM serving layer
Private model runtime
Retrieval layer
RAG orchestration
Vector DB + storage
Encrypted index & objects
Client data sources
Files, tickets, repositories
Implementation without guesswork
Reference architectures, hardening guides, and runbooks are delivered with every engagement so your platform team is not starting from a blank wiki page.
See Vault Systems in your environment
We will map your sources, identity stack, and compliance drivers to a concrete deployment plan.