Wide-Reaching Impact, See LOCI in Action.
Health layer monitoring of GPU/CPU In the complex landscape of AI inference and serving, LOCI addresses the critical bottleneck where models that perform flawlessly in 'laboratory' environments encounter devastating failures in production—experiencing power spikes, latency surges, and escalating costs that destroy changes ROI planning. LOCI hardware-aware agent is powered by Aurora Labs' proprietary per-machine Large Code Language Model that specializes in op codes, provides autonomous, predictive optimization that analyzes static code, models, and configurations to identify and resolve inefficiencies before deployment, serving as a specialized orchestrator within the Agentic AI Layer to ensure optimal performance across the entire AI stack.
In the fast-paced world of HPC, LOCI, powered by Aurora Labs' proprietary Large Code Language Model, is tailored to meet the demanding needs of HPC environments and health management systems, helping to identify resources optimization and uptime.
Financial institutions and security firms require impeccable code quality and robust testing. Aurora Labs offers advanced anomaly detection and compliance features, making it an ideal choice for these high-stakes industries.
With the rise of connected and autonomous vehicles, the automotive industry demands robust software solutions. Aurora Labs caters to this sector by providing AI-driven tools for SDV, that ensure safety-critical systems are thoroughly tested and optimized. Read More >
LOCI provides data centers with the intelligence needed to make data-driven decisions about energy consumption and workload management. By shifting observability left and providing actionable insights from compiled binaries, LOCI empowers data centers to optimize their infrastructure for maximum performance, efficiency, and environmental sustainability.
Connected device vendors, ranging from consumer IoT units and fleet telematics to robotics and industrial control systems, are increasingly relying on embedded software to deliver real-time control, automation, and analytics. These systems must perform reliably at the edge, often under severe power, memory, and thermal constraints. LOCI by Aurora Labs provides function-level performance diagnostics directly from compiled binaries. Using a domain-optimized Large Code Language Model (LCLM), LOCI flags timing instabilities, inefficiencies, and hardware-related regressions, without requiring source code, runtime data, or physical testbeds. It helps embedded software teams catch regressions earlier and release with more confidence across diverse microcontroller and SoC platforms.
Telecom infrastructure often lacks accessible source code due to legacy systems, outsourced firmware, and multi-vendor integration, yet the need for software optimization and security is critical. Traditional debugging fails to reveal issues in compiled binaries, especially hardware-specific problems that only emerge in production environments. Telecom systems—from base stations to core switches—rely on optimized binaries where hardware behavior varies across deployments, source code is unavailable, and performance issues are hard to detect pre-production. LOCI provides zero-source observability, surfacing performance bottlenecks, power inefficiencies, and security risks directly from compiled code without instrumentation or source access.
Aerospace and defense organizations developing complex airborne platforms—including UAS, advanced avionics, and ISR systems—face the challenge of ensuring flawless performance in safety-critical, resource-constrained environments. LOCI by Aurora Labs provides observability insights using proprietary Large Code Language Model trained on mission-critical software, directly from compiled binaries before runtime testing, enabling aerospace engineers to identify performance issues, timing instability, and resource inefficiencies earlier in the development cycle.
