Private AI Platform for the Enterprise
Xyner AI runs AI models and autonomous agents entirely inside a boundary you control — private LLM hosting, governed agent execution, and complete audit trails across dedicated VPC, on-premise Kubernetes, sovereign cloud and air-gapped deployments.
What is a private AI platform?
A private AI platform runs AI models, agents and workflows inside infrastructure you control — a dedicated VPC, your own data centre or an air-gapped network — instead of sending prompts and data to shared, multi-tenant AI services. The models serving inference, the data they read, the outputs they produce and the logs that record it all remain within your security boundary, governed by your identity systems and inspected by your own auditors.
The term covers more than model hosting. A production private AI platform must also keep the surrounding machinery private: retrieval indexes and embeddings, agent memory, the credentials used to call your ERP and CRM, and the audit trail regulators will ask for. A setup that hosts the model privately but ships logs to a vendor's cloud is a public platform with extra steps.
Xyner AI was built for this complete definition. It is an agentic platform — agents plan and execute multi-step work across your systems, not just answer questions — and every component of that execution, from model routing to tool calls to audit, can run inside your boundary across six deployment postures described below.
Why enterprises choose private AI
The reasons are concrete, and they compound as AI moves from pilots into core operations.
Data that cannot leave
Customer records, patient data, trade positions, legal matters, citizen files. For many data classes the question is not whether a public AI vendor's terms are acceptable — it is that the data may not cross the boundary at all, contractually or by law. Private deployment removes the question instead of negotiating it.
Prompts are data too
A prompt that pastes a contract clause, a customer complaint or an incident description discloses exactly the information your DLP program exists to protect. On a private platform, prompts and completions are processed and stored where your other sensitive data lives — nowhere else.
Verifiable, not contractual, control
Public AI services offer assurances you must take on trust. A private deployment gives your security team network paths they can inspect, access controls they configure, and logs they hold. The difference matters most in front of an auditor or regulator.
Residency and sovereignty built in
When the platform runs in your region — your VPC, your data centre, a sovereign cloud — data residency stops being a vendor commitment and becomes an architectural fact. For requirements beyond residency, Xyner also supports fully sovereign and air-gapped postures.
Model choice without lock-in
Private does not mean one model. Xyner routes across 12+ LLM families — OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral and privately hosted models — under policy. Sensitive data classes can be pinned to private models while less sensitive work uses commercial ones, if your policy allows any at all.
Agents raise the stakes
A chatbot that leaks a prompt is bad; an agent with write access to your ERP is a different risk class entirely. Once AI starts acting in your systems — not just talking — keeping execution, credentials and approvals inside your boundary becomes the conservative default, not the exotic option.
Who needs a private AI platform?
If any of these describe your organization, public AI services will eventually hit a wall that private deployment removes.
Banks & insurers
Customer financial data, transaction history and risk models under supervisory oversight.
Government entities
Citizen data and administrative records that must stay within national jurisdiction.
Healthcare providers
Patient records and clinical documentation under HIPAA-aligned handling.
Critical infrastructure
Energy, water, transport operators where AI touches operational systems.
Legal & professional firms
Matter files and client confidences protected by privilege and engagement terms.
Manufacturers & R&D
Designs, formulas and process knowledge that are the company's actual value.
Telecoms & platforms
Subscriber data at a scale where any external processing is a board-level question.
Anyone bound by contract
Confidentiality clauses with your own customers often forbid third-party AI processing outright.
Six deployment postures, one platform
Privacy is a spectrum, not a switch. Xyner AI runs the same agent definitions across every posture, so you choose by requirement — and move later without rebuilding.
Dedicated VPC
A single-tenant deployment in a cloud account and region of your choosing. No shared infrastructure, your network controls, SaaS-like operations with a 99.95% SLA. The fastest route to private AI for most enterprises.
Managed control plane, in-VPC data plane
Xyner operates the control plane — orchestration logic, upgrades, agent definitions — while all inference, data access and execution happen inside your VPC. Business data never crosses the boundary. The full architecture is explained on our hybrid agentic AI page.
On-premise, sovereign & air-gapped
Full installation on your own Kubernetes, in a sovereign cloud, or in a network with no external connectivity at all — with private models hosted alongside. For the requirements driving this choice, see on-premise agentic AI and self-hosted deployment.
SaaS is also available for workloads without privacy constraints — useful for evaluation, and for the common pattern where a pilot runs in SaaS while the private environment is provisioned. Full details on the deployment page.
Private LLM hosting on Xyner AI
The model layer of a private AI platform, handled properly.
Open-weight models in your environment
Meta Llama, Mistral and other open-weight families run on GPU infrastructure inside your VPC or data centre, served through the same router as every other model — so applications and agents are unaware of where inference physically happens.
Policy-based routing
Routing rules bind data classes to models: personal data and privileged documents to private models only; public-facing drafting to a commercial model if policy allows. The rule set is auditable and versioned — your model governance, enforced in the platform rather than in a memo.
12+ families, one interface
OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral and private on-prem models behind one governed interface. Swap or add models as the landscape shifts — without touching agent definitions. See multi-LLM orchestration.
Everything around the model stays private too
Embeddings, retrieval indexes, agent memory, evaluation data and logs are generated and stored inside the boundary. These artifacts reconstruct your source data; a private platform that exports them is leaking by design.
What private AI agents do in production
Private deployment is the posture; agentic execution is the payoff. Problem → agent action → outcome.
Confidential document processing
Contracts, claims and case files too sensitive for external APIs → agents classify, extract and summarize on private models with citations → document-heavy work accelerates without a single byte leaving the boundary.
Customer service on customer data
Service workflows need full account context → agents read and act on CRM and billing records inside your environment → resolution improves while customer data handling stays within existing commitments.
Financial operations
Reconciliations, exception handling and reporting touch regulated data → agents execute against ERP and finance systems in-boundary with approval gates on postings → cycle times drop with evidence auditors accept.
Clinical & claims administration
Referrals, prior authorizations and claims assembly involve patient data → agents prepare cases for clinician review under HIPAA-aligned handling → administrative load falls without new disclosure risk.
Internal knowledge agents
Policies, engineering docs and institutional knowledge are competitively sensitive → retrieval and reasoning run on private indexes → employees get grounded answers; the knowledge base never leaves.
IT & employee services
Access requests, onboarding and ticket resolution expose identity data → agents execute in ITSM and HR systems with scoped credentials → faster service with per-action audit.
Risk & compliance review
Screening and monitoring workflows read the data regulators care most about → agents assemble review files and flag anomalies for human decision → coverage expands without expanding data exposure.
Procurement & supplier operations
Supplier contracts and pricing are commercially confidential → agents validate invoices, chase documents and reconcile exceptions in-boundary → back-office throughput rises at zero disclosure cost.
Air-gapped operations
Some environments have no external connectivity at all → the full platform, models included, runs inside the gap → teams that were excluded from AI entirely get governed agents.
How a private AI deployment comes together
From boundary definition to continuous improvement — the lifecycle your architects will run.
1. Define the boundary
Decide what must stay inside — data classes, logs, embeddings, model weights — and pick the posture that enforces it: VPC, hybrid, on-premise or air-gapped.
2. Provision the environment
Stand up the platform in your cloud account or on your Kubernetes, wired into your identity provider, network controls and secrets management.
3. Set up the model layer
Deploy private models where required, connect permitted external models, and write the routing policy that binds data classes to models.
4. Connect your systems
Wire agents to ERP, CRM, ITSM, databases and document repositories through governed connectors with scoped, vaulted credentials.
5. Configure governance
RBAC, approval gates, action budgets and audit configuration — set before the first agent touches production data, not after.
6. Operate and improve
Monitor every step, run evaluation suites against changes, measure outcomes and expand to adjacent workflows with evidence in hand.
Private AI that can still reach your systems
A private platform is useless if agents cannot act. Xyner ships 1,000+ connectors that run inside your boundary — the integration traffic stays private too.
ERP & finance
SAP, Oracle and finance systems — invoices, orders, master data, reconciliations.
CRM & service
Salesforce, ServiceNow and service desks — cases, tickets, SLAs, customer records.
HR & workforce
Workday and HR platforms — onboarding, requests, approvals, employee records.
Databases & warehouses
Governed queries and retrieval over the data stores already inside your boundary.
Document repositories
Contract stores, content systems and file shares — retrieval with lineage, indexes kept private.
REST & SOAP APIs
In-house and legacy systems through the same governed execution layer — every call permissioned and logged.
Security and governance in private deployments
Private infrastructure narrows the attack surface; platform controls govern what runs inside it.
Your identity stack
The platform binds to your IdP; RBAC scopes every user and agent.
Human approvals
Sensitive actions pause for sign-off — thresholds set per workflow, enforced in-platform.
Audit you hold
Every step logged and replayable, stored inside your boundary — see audit & observability.
Guardrails & budgets
Controlled tool access, input/output policies and action budgets on every agent.
Model routing policy
Rules decide which models may see which data classes — private models for the sensitive ones.
Residency by architecture
Data, logs and inference stay in your chosen region or site — see data residency.
Certifications
SOC 2 Type II and ISO 27001 certified; GDPR-compliant; HIPAA-aligned where relevant.
Continuous evaluation
Regression suites and drift detection run in-boundary against every agent change.
For the threat model behind these controls — prompt injection, tool misuse, exfiltration paths — see the secure agentic AI platform page and the platform security overview.
Public SaaS AI vs. private AI postures
An honest comparison — each posture trades something. The question is which trade your data can afford.
| Dimension | Public SaaS AI | Private cloud AI (VPC / hybrid) | On-premise / air-gapped |
|---|---|---|---|
| Where prompts & data are processed | Vendor's multi-tenant infrastructure | Your VPC, your region | Your data centre or isolated network |
| Who can verify the controls | Vendor attests; you trust | Your security team inspects | Your security team controls end to end |
| Model choice | Vendor's catalogue | 12+ families incl. private models | Private / open-weight models (plus external, if connected) |
| Data residency | Contractual, region options vary | Architectural — you pick the region | Architectural — your site |
| Operational effort for you | Lowest — vendor runs everything | Low — Xyner manages, 99.95% SLA | Highest — your team operates infrastructure |
| Time to first deployment | Days | Weeks — within the 6–12 week use-case cycle | Longest — hardware and procedures add lead time |
| Upgrades & new features | Immediate | Managed by Xyner (hybrid/VPC) | Scheduled by your team; delayed in air-gapped sites |
| Suits data that cannot leave | No | Yes, for most regulated classes | Yes, including sovereign mandates |
A practical roadmap to private AI
Ten steps from requirement to a scaled private AI capability.
1. Classify your data
Decide which data classes may use external AI, which need private processing, and which need isolation.
2. Pick the posture
Map those classes to VPC, hybrid, on-premise or air-gapped — most enterprises need one primary posture, not all of them.
3. Choose the first workflow
One measurable, document- or case-heavy process whose data justifies private deployment.
4. Provision the environment
Platform into your VPC or Kubernetes; identity, network and secrets integration from day one.
5. Stand up the model layer
Private models deployed where required; routing policy written and reviewed by security.
6. Integrate the systems the workflow touches
Scoped credentials, governed connectors — only the systems this use case needs.
7. Configure governance
RBAC, approval gates, action budgets, audit retention — signed off by risk before testing.
8. Test with real users
Evaluation suites plus the process owners who will live with the agent, on real (in-boundary) data.
9. Go live and measure
Cycle time, completion rate, escalation rate — evidence for the scale-up decision.
10. Scale on the same boundary
The environment, model layer and governance are reusable — the second workflow ships far faster.
Why Xyner for private AI
Agentic capability without the data trade-off
Xyner AI combines 250+ agentic capabilities and 1,000+ enterprise integrations with deployment postures that keep models, prompts, data, outputs and logs inside your boundary — operated from offices in New York (Global HQ), Chennai, Dubai, Riyadh, Singapore and Manama.
Frequently asked questions — private AI platforms
What security, data and platform teams ask before committing to private AI.
What is a private AI platform?
A private AI platform runs AI models, agents and workflows inside a boundary you control — a dedicated VPC, your own data centre or an air-gapped environment — so prompts, enterprise data, model outputs and logs never pass through shared multi-tenant infrastructure. Xyner AI is a private AI platform for building and governing enterprise AI agents under that posture.
How is a private AI platform different from using a public AI API?
With a public API, your prompts and data leave your network and are processed on infrastructure you do not control, under terms you negotiate but cannot verify. A private platform keeps inference, orchestration, memory and logs inside your boundary, so your own security team — not a vendor's — defines and verifies the controls.
What stays inside the boundary with Xyner AI?
In private deployments: the models serving inference, every prompt and completion, retrieved documents and embeddings, agent memory, tool calls into your systems, and the full audit log. In hybrid mode, only control-plane metadata such as agent definitions and version state crosses the boundary — never business data.
Does private AI mean weaker models?
Not necessarily. Xyner AI routes across 12+ LLM families, including private and on-premise models such as Llama and Mistral deployments, and can combine them — private models for sensitive data classes, with optional governed access to commercial models where your policy allows it.
Who needs a private AI platform?
Organizations whose data cannot leave their control: banks and insurers, healthcare providers, government and defence-adjacent entities, critical infrastructure, legal and professional firms, and any enterprise with contractual confidentiality, data-residency or regulatory obligations that public AI services cannot satisfy.
Can a private AI platform still run AI agents, not just chat?
Yes. Xyner AI is agentic: agents plan multi-step work, call your ERP, CRM and ITSM systems through 1,000+ governed connectors, and complete tasks under approval gates and action budgets — all executing inside your private boundary.
What deployment options does Xyner AI offer?
Six postures: SaaS, dedicated VPC, hybrid with a managed control plane and in-VPC data plane, on-premise Kubernetes, sovereign cloud, and fully air-gapped. The same agent definitions run across all of them, so you can start in one posture and move to another.
How does private LLM hosting work on Xyner AI?
Open-weight models such as Meta Llama and Mistral run on GPU infrastructure inside your VPC or data centre, served through the platform's model router. Routing policy decides which workloads use the private models and which, if any, may use external commercial models.
Is a private AI platform compliant with GDPR and similar laws?
A private deployment makes compliance easier to demonstrate because data processing locations and access paths are under your control, with RBAC and complete audit trails as evidence. Xyner AI is SOC 2 Type II and ISO 27001 certified, GDPR-compliant and HIPAA-aligned; legal compliance obligations remain with the data controller.
How long does it take to stand up a private AI platform?
A first production use case typically takes 6–12 weeks, including environment provisioning, private model setup where required, system integration, governance configuration and user testing. Air-gapped installations add time for infrastructure and transfer procedures.
What does Xyner manage versus what do we manage?
That depends on the posture you choose. In dedicated VPC and hybrid modes, Xyner manages platform operations and upgrades while your data stays inside your environment with a 99.95% SLA. In on-premise and air-gapped modes, your team operates the infrastructure and Xyner supports the platform.
How do we evaluate private AI platform vendors?
Verify what actually stays inside your boundary (including logs and embeddings), which private models are supported, integration coverage, governance depth, certifications such as SOC 2 Type II and ISO 27001, and whether one platform serves VPC, on-premise and air-gapped postures as needs change.
Go deeper on private AI
Adjacent reading, depending on where your requirement is strictest.
Run AI where your data already lives
Tell us your boundary — VPC, on-premise or air-gapped — and we'll walk through how Xyner AI runs inside it.
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