Private & Secure AI Deployment
When your data legally cannot leave your perimeter, the cloud is not an option. We deploy AI models on-premises, in your VPC, or fully air-gapped — with no compromise on capability.
of data and inference stays inside your security perimeter, with no public API calls
For healthcare, financial services, defense, and other regulated sectors, sending data to a public AI API is often not a risk decision — it is simply not allowed. Private AI deployment removes that constraint entirely. You get the same model capability, running on infrastructure you fully control, with nothing crossing a boundary that compliance or security has drawn a hard line around. A hospital can run an open-weight model like Llama 3 on its own GPU cluster to summarize patient notes, with no PHI ever leaving the building.
Inside Private & Secure AI
Private and secure AI deployment runs AI models entirely within your own infrastructure — on-premises servers, an isolated cloud VPC, or a fully air-gapped environment — so data and model outputs never traverse a public API or leave your security perimeter. We deploy open-source models on your own GPU infrastructure with the same performance characteristics as cloud-hosted proprietary models, for organizations where that boundary is non-negotiable.
Deployment pattern selection — we determine whether on-premises, VPC-isolated, or fully air-gapped fits your regulatory and operational needs.
Open-source model selection — we choose and validate open models like Llama 3 or Mistral against the accuracy your use case requires.
GPU infrastructure setup — we provision and configure the on-prem or private cloud hardware your models run on.
Network isolation — we design network topology, access controls, and encryption so nothing leaves the defined perimeter.
Compliance documentation — we prepare the evidence pack your auditors and regulators will ask for.
Performance validation — we benchmark the private deployment against your accuracy and latency requirements before go-live.
Is this right for you?
This service fits best when you recognise yourself below.
Healthcare organizations bound by HIPAA and similar data protection rules.
Financial services and defense organizations with strict data sovereignty requirements.
Companies whose data contains trade secrets that cannot touch a public API.
Organizations whose security policy mandates AI inference within infrastructure they fully control.
The problems behind the brief
Regulations that block public AI APIs outright
Some data simply cannot leave your environment. Private deployment removes that conflict entirely, not as a workaround but as the architecture.
Assuming private AI means worse performance
Open-source models on properly configured GPU infrastructure perform comparably to cloud-hosted proprietary models. We validate this for your specific use case.
Air-gapped environments are hard to build correctly
Network isolation done wrong leaves accidental gaps. We design and test the boundary rigorously before anything goes live.
No clear compliance evidence
Auditors need documentation, not just a private setup. We prepare the evidence pack as a standard deliverable.
Hardware and operational complexity
Running your own GPU infrastructure is unfamiliar territory for most teams. We hand over runbooks and can stay engaged for support.
A clear, repeatable process
No mystery. You always know what happens this week and what comes next.
We confirm the deployment pattern, select the model, and design the network isolation architecture against your compliance requirements.
We provision infrastructure, deploy the model, and configure network isolation and encryption, testing the perimeter rigorously.
We benchmark performance against requirements and prepare the compliance documentation and evidence pack.
We go live, confirm the perimeter holds under real use, and hand over operational runbooks and monitoring.
Deliverables
Concrete outputs you keep — not just a conversation.
What good looks like
Zero data or inference traffic crossing the defined security perimeter.
Model accuracy and latency meeting agreed targets on private infrastructure.
Compliance documentation that satisfies your auditors on first review.
A team that can operate the private deployment independently.
The stack behind the work
We pick tools to fit your needs, never vendor relationships.
LLM
- Llama 3
- Mistral
Infrastructure
- Kubernetes
- Docker
- Terraform
Common questions about Private & Secure AI
Straight answers to the questions we hear most.
Still have questions? Talk to our team
The natural next step
A private model still needs the data infrastructure around it to be sound. Data Pipelines and AI Cloud Infrastructure both apply inside a private perimeter the same way they would in any other environment.
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