06 / Service
Perimeter
Private AI
Full-stack private AI deployment. Your models, your infrastructure, zero cloud exposure.
Inside Your Boundary
Models, data, logs, and inference traffic stay inside your VPC or on-prem environment. No third-party API calls required.
Open-Weight Models
We select, deploy, and tune open-weight models for your workloads, so capability does not depend on a closed cloud provider.
Compliance-Ready
Access controls, audit trails, encryption, and operational hardening are designed for regulated teams handling sensitive data.
Managed Operations
We monitor inference, tune performance, manage model updates, and keep the private AI stack reliable after launch.
Private AI
capabilities
We build the full stack for secure AI inside your environment: model serving, retrieval, fine-tuning, monitoring, governance, and managed operations.
Deployment Models
AI where your
data already lives
The deployment model follows your security posture. We adapt the stack to your infrastructure, controls, and operational team.
Private VPC
AI infrastructure deployed inside your AWS, GCP, Azure, or private cloud account with controlled network access.
On-Prem
Local inference running on your own servers or GPU cluster for the strictest data residency and access requirements.
Hybrid
A practical architecture that keeps sensitive workloads private while allowing controlled use of approved external systems.
The Process
From audit to
managed operations
Audit
We map your AI use cases, sensitive data paths, compliance requirements, and current infrastructure constraints.
Architecture
We design the private AI stack, choose models, size infrastructure, and define security and operations boundaries.
Deploy
We provision inference, model serving, retrieval, monitoring, and access controls inside your environment.
Tune
We evaluate quality, fine-tune where useful, optimize latency and cost, and harden behavior for production.
Operate
We manage model updates, reliability, observability, and ongoing improvements as your private AI usage grows.
Use Cases
Built for sensitive
enterprise data
Healthcare AI
Assist clinicians and operations teams with PHI-aware systems that keep medical data inside approved infrastructure.
Financial Services
Build AI workflows for regulated financial data without sending sensitive records to third-party model APIs.
Legal & Professional Services
Analyze privileged documents, contracts, and case materials with strong data boundaries and auditability.
Enterprise IP Protection
Give teams AI capability over proprietary product, engineering, and customer data without exposing core IP.
FAQ
Private AI
questions
Do we need our own GPUs
Not always. We can deploy in a private cloud account, on dedicated GPU infrastructure, or on-prem if your data residency and control requirements demand it. The right option depends on workload volume, latency, budget, and compliance needs.
Which models do you use
We choose based on the workload. That can include Llama, Mistral, Qwen, Gemma, or other open-weight models. We evaluate quality, latency, licensing, and operational fit before recommending a stack.
Can this replace OpenAI or Anthropic APIs
For many internal workflows, yes. Some frontier-model tasks may still perform better through external APIs, but Perimeter is designed for teams where privacy, control, and compliance matter more than relying on a public model endpoint.
How do you handle compliance
We align the architecture with your compliance requirements: encryption, access controls, audit logs, network boundaries, data retention, and operational procedures. Your compliance team stays involved so the implementation matches your policies.
What happens after deployment
We can stay involved with monitoring, model upgrades, evaluation, fine-tuning, and incident response. Private AI is infrastructure, not a one-time integration, so operations are part of the engagement.
Bring AI inside
your perimeter
Let's map the private AI stack your team needs: models, infrastructure, controls, and operations built around your data boundaries.