NVS Kernel / MCP Server
Detect what
AI gets wrong.
NVS Kernel connects between AI systems and the tools they use — detecting, logging, and visualizing potential hallucinations in AI-generated responses before they cause downstream damage.
NVS Kernel is currently in preparation. Leave your email and we’ll let you know when it goes live.
Product
A semantic monitoring layer for AI-generated outputs.
NVS Kernel helps teams detect, record, and review risks in AI-generated outputs without relying on raw conversation text. It turns structural signals across AI workflows into visible, reviewable risk data.
Detect output risk
Identify signals associated with unsupported claims, inconsistent outputs, and unreliable AI-generated responses.
- Unsupported claims
- Source mismatches
- Context contradictions
Record process events
Capture process-level events such as tool usage, timing, semantic-state patterns, and warning signals.
- Detection history
- Context records
- Audit trail
Review risk patterns
Surface recurring risk patterns across AI workflows so teams can review where instability concentrates.
- Risk patterns
- Trend monitoring
- Review dashboard
Preserve privacy
Monitor structural signals without storing private conversation text as review data.
- No raw text
- Filtered signals
- Audit-safe records
How it works
From raw output to reviewable evidence.
Observe
Monitor structural signals behind AI-generated outputs, including tool interactions, timing, and semantic-state patterns.
Analyze
Evaluate signs of unsupported claims, inconsistency, semantic drift, and contextual risk.
Record
Store detection events, warning signals, and process-level context for review and evidence.
Trace
Preserve the reasoning trajectory and intervention history so output risks can be reviewed after the fact.
Technology
Semantic monitoring, not text surveillance.
NVS Kernel is called “semantic” because it observes how an AI process moves through a task — not just the words it outputs.
Instead of relying on raw conversation text, it captures structural telemetry across the workflow and turns it into signals a team can review.
- tool_usageWhich tools were called, and how their results were used.
- timingLatency and sequencing patterns across the run.
- semantic_stateShifts and drift in the model’s working context.
- warning_signalsMarkers correlated with unreliable output.
- trajectoryThe reasoning path, preserved for after-the-fact review.
NVS Kernel is coming soon
We’re putting the finishing touches on it. Leave your email and we’ll let you know the moment it’s available.
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