Sophic Compute offers a software layer that transparently accelerates digital twin simulations and AI/ML pipelines across heterogeneous, distributed infrastructure. By abstracting parallelization, data movement, and runtime coordination, it lets existing applications scale on‑prem, cloud, or edge resources without code changes, delivering higher throughput and lower latency for engineering and data science teams.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise and cloud workloads that rely on digital twins and AI/ML require massive computational power, but existing parallelization and distribution solutions are complex, labor‑intensive, and often leave performance untapped.
Solution
Sophic Compute provides a software layer that transparently accelerates digital twin simulations and AI/ML pipelines across heterogeneous, distributed infrastructure. By abstracting parallelization, data movement, and runtime coordination, the platform lets existing applications run at scale without code rewrites or specialized expertise. It dynamically allocates compute resources—on‑prem, cloud, or edge—to match workload demands, delivering higher throughput and lower latency. The solution integrates with common development frameworks and orchestration tools, enabling enterprises to deploy large‑scale simulations such as computational fluid dynamics, finite element analysis, or in‑silico clinical trials more efficiently.
Target Audience
Primary customers are engineering and data science teams in industries such as aerospace, energy, pharmaceuticals, and manufacturing that run large‑scale digital twin or AI/ML workloads.
Features
- Runtime engine that automatically partitions and schedules simulation or ML tasks across CPUs, GPUs, and accelerators
- Transparent scaling across on‑premise clusters, public cloud instances, and edge nodes without application changes
- Adaptive load balancing and data caching to minimize communication overhead and maximize resource utilization
- Compatibility layers for popular AI/ML libraries (TensorFlow, PyTorch) and simulation frameworks (ANSYS, OpenFOAM)
- Monitoring dashboard showing real‑time performance metrics, resource usage, and cost estimates