AI products engineered
for enterprise scale.
Infrastructure products and operating patterns for teams building production AI across compute, data, serving, security, and lifecycle operations.
Infrastructure products.
Designed for real operations.
Turnkey compute blocks for enterprise AI
GPU Infrastructure Pods
Purpose-built GPU infrastructure assembled around workload profile, facility constraints, networking, storage, observability, and operating model. Designed for teams that need a production path from capacity planning through operations.
Model serving patterns for production teams
Inference Platform
A deployment architecture for serving LLM, embedding, and vision workloads with model versioning, rollout controls, GPU-aware scheduling, and operational visibility across the serving path.
A governed data layer for AI workloads
AI Data Fabric
A data platform pattern that connects enterprise data sources to AI infrastructure through ingestion, transformation, feature preparation, lineage, policy controls, and secure serving interfaces.
A controlled interface for model access
AI Gateway
A single access layer for model requests, authentication, routing, quota policy, cost visibility, and fallback behavior across proprietary, open-source, and third-party models.
Lifecycle controls from experiment to operations
MLOps Workbench
A practical operating layer for experiment tracking, model registry, release pipelines, monitoring, and retraining workflows that fit enterprise governance and security needs.
Isolated environments for sensitive workloads
Secure AI Enclave
Dedicated compute environments for sensitive training and inference use cases, with isolation, key management, policy enforcement, and deployment patterns aligned to regulated enterprise operations.
Product strategy that respects
the infrastructure underneath.
Prognity products are shaped by the same infrastructure work behind data center design, cloud operations, AI platforms, and secure enterprise delivery.