Private AI
Keep model execution and enterprise data within agreed access boundaries.
Music generation, video processing, digital humans and interactive AI demand more than model access. They need predictable scheduling, data flows, memory and security across different operating environments.
Keep model execution and enterprise data within agreed access boundaries.
Deploy around customer-controlled compute, networks and operating policies.
Coordinate workloads across private environments and cloud capacity.
Bring inference closer to devices and time-sensitive interaction.
Scope a local AI environment around a site, team or bounded workload.
Agree data boundaries, latency, capacity and operational ownership.
Select runtime, compute and integration requirements for the target environment.
Evaluate representative workloads and acceptance criteria with the customer.
Agree monitoring, change management, support and rollout scope.
Private inference, agent workflows and hybrid AI deployment.
AIMPLE provides the runtime, orchestration and infrastructure layer for deploying AI workloads across cloud, on-premise and edge environments.
Our application and deployment experience is the foundation of our infrastructure direction. Explore published projects, their context and the work behind them.
CES 2024 — the LLM-based digital human platform goes public.
Conference · 2024Proposing the future of AI and virtual artists at the MWM conference.
Web3 · 2023Virtual broadcast and digital human content production with Web3 support.