C‑Gen.AI offers Nucleaton™, an AI infrastructure orchestration platform that centralizes GPU fleet scheduling, real‑time telemetry, and automated fault handling across cloud and on‑premise environments. The platform dynamically reallocates idle GPUs between training and inference, provides granular cost attribution and multi‑tenant isolation, enabling enterprises and AI‑as‑a‑Service providers to maximize utilization and monetize GPU assets.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and AI-focused organizations often operate large GPU clusters that remain under‑utilized because manual provisioning, scheduling, and fault handling require specialized DevOps or HPC expertise. This leads to high operational spend, idle hardware, and delayed model deployment, especially when workloads shift between training and inference phases.
Solution
C‑Gen.AI delivers Nucleaton™, a turnkey AI infrastructure orchestration platform that abstracts the complexity of GPU fleet management. The solution unifies job scheduling, real‑time observability, access control, and usage‑based cost attribution into a single control plane, enabling teams to provision, run, and monetize GPU clusters without dedicated engineering resources. Dynamic resource allocation automatically shifts compute between training cycles and inference endpoints, eliminating idle GPU capacity. Integrated telemetry and automated fault isolation keep workloads running reliably, while hardware‑agnostic support spans AWS, private clouds, and on‑premise environments. The platform also provides multi‑tenant isolation and audit‑ready logs for AI‑as‑a‑Service (AIaaS) offerings, turning otherwise idle hardware into revenue streams.
Target Audience
Primary customers are AI‑focused startups accelerating model time‑to‑inference, data‑center operators offering AI‑as‑a‑Service, and large enterprises deploying secure, private‑cloud GPU workloads.
Features
- Centralized scheduling engine with priority queues and GPU‑aware placement policies for deep‑learning training and high‑throughput inference workloads
- Real‑time hardware telemetry and automated fault management that isolates defective nodes to prevent job crashes
- Dynamic resource reallocation module that redistributes idle GPU capacity from training to inference in seconds
- Granular cost attribution and usage reporting, enabling per‑project or per‑tenant billing and ROI tracking
- Hardware‑agnostic orchestration layer supporting AWS, private cloud, and on‑premise GPU clusters with seamless failover
- Role‑based access control and immutable audit logs for compliance‑driven enterprises
- Multi‑tenant isolation and API hooks for building AI‑as‑a‑Service platforms on shared GPU infrastructure
- Integrated observability dashboard with metrics, alerts, and API access for CI/CD pipelines