Private AI deployment inside your own environment
Use AI without sending sensitive data to shared services. We plan each private AI deployment around your data rules, then run models, retrieval and voice inside your boundary.
Book an architecture callSee the ARIA case study
Ollama
Qdrant
Whisper
NVIDIA
KubernetesWhat is a private AI deployment?
A private AI deployment runs models and supporting services within an environment you control, such as a dedicated cloud account, private data centre or isolated network. You decide where data goes, who can reach it and how long it is kept.
Private does not automatically mean compliant. Good results also depend on identity, encryption, logging, retention and vendor review, and your legal or compliance team should confirm what your rules require.
What a private AI deployment can include
We deploy only what your use case needs, and keep the design simple to operate.
Self-hosted models
Language and embedding models that run within your boundary.
Self-hosted LLMs →Private knowledge search
RAG over internal documents that respects existing permissions.
RAG infrastructure →Private voice AI
Speech services that keep audio and transcripts in your environment.
Voice stack →Identity and access
Connect single sign-on and role-based access for users and agents.
Audit and retention
Log prompts, tool calls and responses with agreed retention rules.
Compliance support
Provide technical evidence for reviews, including DPDP-related work.
DPDP compliance →Choosing a private AI environment
Private cloud account
Best forQuick delivery with strong cloud controls
Watch forConfiguration and provider terms need review
On-premises
Best forStrict data location and existing data centres
Watch forHardware lead time and operations
Air-gapped
Best forHighly restricted environments
Watch forManual updates and limited external tools
How we deliver private AI
Security and data requirements are agreed before any model is chosen.
Classify
Identify data and applicable rules.
Architect
Define boundary, access and hosting.
Build
Deploy services and integrations.
Test
Check quality, access and data flows.
Hand over
Document, monitor and support.
Private AI deployment checklist
Map every data flow
A private AI deployment is only as private as its weakest connection. List where prompts, documents, logs and telemetry travel, including monitoring tools and model downloads. Block unnecessary outbound access and keep a record your security team can review.
Connect to existing identity
Use your single sign-on and role structure instead of creating separate accounts. Agents should have the least access needed, and retrieval should respect source permissions. Sensitive actions should require a person to approve them before anything changes.
Decide what to log
Logs are vital for debugging and audits, but they can also contain sensitive information. Agree what is recorded, how it is masked, who may view it and how long it is kept. Review those rules with your legal and security teams.
Prepare for isolated updates
In restricted networks, model and security updates need a controlled route. Set up an internal registry, test updates in staging and keep rollback steps ready. Including this from the start keeps a private AI deployment secure as it ages.
Official resources: Kubernetes security, OWASP Top 10 for LLM applications and NIST AI Risk Management Framework.
Private AI deployment questions
Does private AI mean no data ever leaves?
It can, if the design uses only internal services and blocks outside connections. Some designs intentionally allow selected external calls, so we document every data flow clearly.
Is private AI automatically compliant?
No. Hosting location is only one part of compliance. Access control, retention, consent and vendor terms also matter, and your compliance team should confirm the requirements.
Can private AI work with frontier models?
Yes. A hybrid design can keep sensitive work on hosted models and send only approved tasks to external models.
How is it managed after launch?
Your team can run it with our handover, or we can provide ongoing monitoring and upgrades through managed AI services.
Related AI infrastructure and services
Plan your private AI deployment
Tell us about your data rules and use case, and we will propose a suitable architecture.
Talk to our AI infrastructure team