LLM Monitoring

See what’s working, what’s drifting, and what’s breaking.
Get full-spectrum visibility into your AI’s behavior—with less noise and more signal—so you can fix issues before they scale.

LLM-Monitoring
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Evaluate prompt & response integrity

Surface mismatches in sentiment, language, or readability—line by line. Spot broken flows, poor phrasing, or off-target completions before they reach your users.

Superwise LLM monitoring dashboard evaluating prompt and response integrity
Superwise platform detecting data, concept, and retrieval drift in LLM applications

Detect data, concept & retrieval drift

Track how prompts, responses, or retrieval patterns change across time. From task drift to topic shifts, get early signals and keep your LLMs aligned with purpose.

  • Data drift: Break down prompts and responses into language components and track over time.
  • Concept drift: Identify changes in usage such as task drift and topic drift.
  • Retrieval drift: Leveraging a vector database or fine-tuning with an internal corpus? See over time if responses are drifting from your benchmarks.

Check out Elemeta!
Our open-source package for unstructured data

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Pinpoint & analyze hallucinations

SUPERWISE scans for fact mismatches, broken citations, and content anomalies. Know when your LLM makes things up—and decide whether to flag, reroute, or block those responses automatically.

Identify AI governance & privacy violations

Detect false facts, broken links, and mismatched citations on the fly. Flag or block hallucinated responses automatically and keep outputs grounded in truth.

Uncover malicious use & adversarial attacks

Flag prompt injections, data poisoning, jailbreaking, and leaking attacks the moment they happen. Investigate the root cause and take control before threats escalate.

Try the community edition

No credit card required.

Sign up for SUPERWISE

Easily get started with a free
community edition account.

				
					!pip install superwise

Build your project

				
					import superwise as sw
project = sw.project("Fraud detection")
model = sw.model(project,"Customer a") 
policy = sw.policy(model,drift_template)

Start monitoring

Fraud detection

Entire population drift – high probability of concept drift. Open incident investigation →

Fraud detection

Segment “tablet shoppers” drifting.
Split model and retrain.

Free community edition

Try the community edition

No credit card required.

Sign up for Superwise

Easily get started with a free
community edition account.

				
					!pip install superwise

Build your project

				
					import superwise as sw
project = sw.project("Fraud detection")
model = sw.model(project,"Customer a") 
policy = sw.policy(model,drift_template)

Start monitoring

Fraud detection

Entire population drift – high probability of concept drift. Open incident investigation →

Fraud detection

Segment “tablet shoppers” drifting.
Split model and retrain.

Free community edition
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