Private AI
Private AI starts with
the data boundary.
Decide which workloads can use managed APIs, which need controlled cloud paths, and which should stay inside private environments.
AgenixHub reviews data sensitivity, retrieval needs, model paths, and operating controls before recommending a deployment route.
Boundary Review
Incoming workloads
Public API Safe
Controlled Cloud
Private / On-Prem
Review factors
Privacy
Sensitive data and identity risk
Latency
Response needs and proximity
Cost
Unit cost and operating impact
Quality
Accuracy, relevance, and consistency
Classification
What we classify before architecture.
Data sensitivity
Classify data by confidentiality, regulatory impact, and handling requirements.
Retrieval scope
Understand sources, vector stores, grounding needs, and retention constraints.
Model path
Determine which models can use public APIs, controlled cloud, or must stay private.
Operating control
Define guardrails, access, monitoring, and audit requirements.
Process Flow
Review route.
Map data boundary
We map data types, sources, flows, and risk boundaries.
Classify workloads
We classify each workload by sensitivity, retrieval, and model fit.
Recommend deployment path
We recommend the right path for each workload and why.
Map the boundary before choosing the model.
A focused boundary review reduces risk, controls cost, and improves outcomes.