Predicts power spikes and Performance inefficiencies before test or inference. Optimizes code, configs, and serving — autonomously.
Up to 40% of engineering hours lost diagnosing regressions, bottlenecks, and power spikes.
Modern software & AI workloads are too complex for manual optimization.
Performance inefficiencies and defects raise MTTR, SLA breaches, and operational risk.
To stay safe, teams often over-provision servers, GPUs, and power headroom. This inflates RCC, increases power spend, and wastes hardware cycles.
How LOCI helps: By predicting hot spots before test or inference and optimizing at the opcode/basic-block level, LOCI reduces excess server provisioning, smooths power spikes, and delivers more throughput per watt.
Forecast performance issues before they impact production systems
Deep hardware understanding enables precise efficiency improvements
Maximize throughput per watt while reducing infrastructure costs
Reliability & optimization agent with deep CPU/GPU understanding.
Goal-oriented AI that goes beyond observability to make optimization decisions And automatically surface performance and power problems before test or inference
Embedded into your engineering lifecycle as a trusted team member.
Current tools and agents stop at code syntax. LOCI models HW counters ,Registers and SW in their native dialects
Performance optimization (code & config).
Proactive issue detection pre-test.
Mission-critical stability.
A single scan can flag regressions that would take 3 engineers × 10 hours of debugging effort
Convert days of reactive debugging into hours of guided fixes.
One scan = 27 hours saved.
Predict hotspots, thermal spikes, and bottlenecks before execution.
Optimize batching, tune serving layers, and simulate workloads.
Dynamic power recommendations and reliability trade-offs.
Understands CPUs & GPUs at the opcode level.
Every change is re-measured, validated, and tracked.
Reduce P95/P99 spikes before deployment.
Identify hot paths and stalls across serving layers and kernels.
Flag tail-heavy basic blocks pre-release; visualize basic block energy spikes.
Forecast & smooth GPU/CPU power under load; manage thermals.
Instruction-level efficiency guided by LCLM.
Token budgeting, KV cache, smart batching; tokens throughput metric.
Kernel fusion, memory layout; thermal guard-banding.
Step-time variance, data loader stalls, gradient hotspots.
