C0rr3l8 Product
One platform. From raw signal to defensible action.
C0rr3l8 gives technical and business teams a shared decision surface built on evidence, not assumptions.
The platform pulls operational signals from across your GPU infrastructure, correlates cross-domain patterns that single-tool monitoring misses, and produces role-specific outputs — from operator-level evidence to executive-ready financial summaries.
Cross-Domain Correlation
Scheduler queues, storage throughput, network behavior, GPU utilization, and change records — analyzed together, not in silos. Patterns that only appear when you look across the full stack.
Confidence-Aware Recommendations
Every finding is labeled as projected or verified. Teams know exactly what’s estimated and what’s proven. The platform will refuse to recommend if evidence is insufficient.
Change Control Integration
Correlate performance changes with approved change records from ServiceNow — closing the loop between “something changed” and “something broke.”
Built for Your Whole Team
Executives see projected savings and downloadable reports. Architects see infrastructure visualizations with before-and-after comparisons. Operators see evidence, patterns, and pipeline controls.
Closed-Loop Verification
After a recommendation is acted on, the platform measures whether the expected improvement actually happened. Projected value converts to verified value only when the evidence supports it.
Deploy in 15 Minutes
One command brings up the full platform — API, database, storage, and UI — on any machine with Docker. No complex infrastructure. No cloud dependency.
Guided Decision Workspace
Every page explains what to do next. Run the analysis pipeline step by step — report what happened, decide whether to act, measure the improvement, and project the savings. Plain language throughout. No manual required.
Confidence Ladder
A visual progression from insufficient evidence to projected to verified — with specific guidance on what’s needed to reach the next level.
Savings Attribution
Every dollar figure traces back to a specific pattern, a recommended action, a measured improvement, and transparent assumptions. No mystery numbers.
Sensitivity Analysis
Vary one assumption at a time — jobs per day, GPUs per job, or cost per GPU-hour — and see how projected savings change before committing to action.
Every decision path is traceable and reviewable. Teams can audit exactly why a recommendation was made, what evidence supports it, and what assumptions were used.