Private AI deployment runs AI models inside your own environment. The model, the data, and the outputs stay on infrastructure you control. Prompts never reach a public API.
Tayana selects open-weight models for your workload, deploys them on your specified infrastructure, integrates them with your existing systems, and operates within your security policies.
A private AI deployment is structured across multiple layers. Tayana selects, deploys, and integrates each layer inside your environment.
Discovery and requirements
Tayana meets your IT, security, and operations leads to confirm the use case, regulatory scope, and infrastructure constraints.
Model selection
Tayana evaluates open-weight models against your workload and recommends one based on accuracy, hardware footprint, and licensing. You approve before procurement.
Infrastructure design
Tayana produces a deployment design covering GPU requirements, network isolation, identity controls, and audit logging, fitting your on-premise, private cloud, sovereign cloud, or hybrid environment.
Deployment and integration
Tayana installs the inference stack, configures access controls, and integrates the model with your applications via API. Nothing leaves your environment during inference.
Validation
Tayana tests against your real workload, measures accuracy and latency, and tunes the configuration. You sign off on production readiness from documented test results.
Handover
Tayana documents the deployment and trains your team. You operate internally or retain Tayana under a managed service agreement.
8 to 12 Weeks
From kickoff to production sign-off. Variation depends on infrastructure provisioning, integration complexity, and whether fine-tuning on your data is included.
Scoped on Engagement
Investment depends on infrastructure provisioning, integration points, and whether fine-tuning on your data is included. Tayana scopes each deployment before issuing an estimated efforts.
If private deployment is not the right fit for your workload, Tayana will say so before you commit to infrastructure.
Private AI deployment runs AI models on infrastructure you control: on-premise, private cloud, or sovereign cloud. Prompts and outputs stay inside your environment instead of crossing to a third-party API.
For most enterprise tasks, yes. Open-weight models in the Llama, Mistral, Qwen, and Gemma families match or exceed public API accuracy on document analysis, classification, summarization, and structured extraction. For frontier reasoning on novel problems, public models still lead.
A production deployment typically uses one or two GPU servers with 80 GB of GPU memory, sized to your model and concurrent user count. Tayana sizes hardware against your measured workload, not a default recommendation.
Private deployment supports HIPAA compliance because protected health information stays inside your controlled environment. Compliance also depends on your access controls, audit logging, and vendor management. Tayana documents the deployment so it fits inside your existing HIPAA program.
The validation phase catches this before production. If accuracy or latency fall short, Tayana adjusts the model, fine-tunes on your data, or revises hardware sizing. If private deployment is the wrong fit for your workload, we say so before you commit to infrastructure.
Any open-weight model with a license permitting commercial deployment. Common choices are Llama, Mistral, Qwen, Gemma, and DeepSeek. Tayana recommends a model based on your workload, not a default preference.
Book a thirty-minute call. Tayana will confirm whether private deployment is the right fit for your workload, your infrastructure, and your compliance requirements.