Surface mismatches in sentiment, language, or readability—line by line. Spot broken flows, poor phrasing, or off-target completions before they reach your users.
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.
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.
Detect false facts, broken links, and mismatched citations on the fly. Flag or block hallucinated responses automatically and keep outputs grounded in truth.
Flag prompt injections, data poisoning, jailbreaking, and leaking attacks the moment they happen. Investigate the root cause and take control before threats escalate.
No credit card required.
Easily get started with a free
community edition account.
!pip install superwise
import superwise as sw
project = sw.project("Fraud detection")
model = sw.model(project,"Customer a")
policy = sw.policy(model,drift_template)
Entire population drift – high probability of concept drift. Open incident investigation →
Segment “tablet shoppers” drifting. Split model and retrain.
No credit card required.
Easily get started with a free
community edition account.
!pip install superwise
import superwise as sw
project = sw.project("Fraud detection")
model = sw.model(project,"Customer a")
policy = sw.policy(model,drift_template)
Entire population drift – high probability of concept drift. Open incident investigation →
Segment “tablet shoppers” drifting. Split model and retrain.
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