Prism AI Observability
AI observability built for compliance teams.
Every LLM call captured, scored, and stored with PII scrubbed before it lands in your database. Regulator-ready exports in under 60 seconds.
- #4821Credit Risk Query1.3s5/5PII redacted
- #4820Underwriting Decision0.9s3/5Drift detected
- #4819Policy Lookup0.4s5/5Grounded
Audit pack ready
47 traces · 60s export
Prism
Measure what good looks like, automatically, continuously, at scale
Define quality rubrics, score every interaction, and catch regressions before users do, with automated evaluators that run on every trace or on a schedule you control.
- Accuracy: factual correctness and source fidelity
- Relevance: alignment with the user's actual question
- Completeness: coverage of required information and disclosures
- Safety: guardrail compliance and harm avoidance
- Efficiency: resource usage relative to answer quality
The problem
"Is the AI working well?" is the question everyone asks and no one can answer with data. Manual review does not scale. User satisfaction surveys lag by weeks. NPS tells you something is wrong but not what. You need continuous, automated quality measurement tied to the criteria that actually matter for your use case.
Capabilities
What you get with Prism
Five-dimension scoring
Accuracy, Relevance, Completeness, Safety, and Efficiency. Each scored automatically on every trace by an independent judge model.
Define evaluators
Create scoring rubrics with criteria, thresholds, and weights, from templates or custom-built for your domain.
Run continuously
Attach to projects to run on every trace, on a sampled subset, or on-demand against specific datasets.
Experiments and versioning
A/B test prompt variants against labeled datasets. Compare scores, cost, and latency with statistical rigor and full version history.
Human-in-the-loop
Annotators review flagged traces, correct evaluator outputs, label domain-specific issues, and feed corrections back into the scoring pipeline.
How it works
From instrumentation to evidence
- 1
Define evaluators
Create scoring rubrics with criteria, thresholds, and weights, either from templates or custom-built for your domain.
- 2
Attach to projects
Evaluators run automatically on every trace, on a sampled subset, or on-demand against specific datasets.
- 3
Review scores
Dashboards show aggregate pass/fail rates, score distributions, trend lines, and drill-down to individual traces that failed specific criteria.
- 4
Iterate
Use evaluation results to improve prompts, adjust retrieval, refine guardrails, and validate changes with experiments before shipping.
What teams use it for
In production, every day
Prompt experiments
A/B test prompt variants against labeled datasets, comparing quality, cost, and latency with statistical rigor so changes are data-driven, not anecdotal.
Regression detection
Continuous scoring on production traces flags quality drops before users escalate, with trend lines that pinpoint when behavior changed.
Human-in-the-loop review
Annotators review flagged traces, correct evaluator outputs, and feed corrections back into the scoring pipeline.
Scoring framework
Five-dimension quality rubric
| Dimension | What it measures | Example criteria |
|---|---|---|
| Accuracy | Factual correctness and source fidelity | Does the response match ground truth? Are citations real? |
| Relevance | Alignment with the user's actual question | Did the model answer what was asked, or drift to adjacent topics? |
| Completeness | Coverage of required information | Did the response include all required disclosures, steps, or data points? |
| Safety | Guardrail compliance and harm avoidance | Were any guardrails triggered? Did the response contain policy violations? |
| Efficiency | Resource usage and response quality | Token usage relative to answer quality; unnecessary verbosity; cost per quality point. |
Dimension
Accuracy
What it measures
Factual correctness and source fidelity
Example criteria
Does the response match ground truth? Are citations real?
Dimension
Relevance
What it measures
Alignment with the user's actual question
Example criteria
Did the model answer what was asked, or drift to adjacent topics?
Dimension
Completeness
What it measures
Coverage of required information
Example criteria
Did the response include all required disclosures, steps, or data points?
Dimension
Safety
What it measures
Guardrail compliance and harm avoidance
Example criteria
Were any guardrails triggered? Did the response contain policy violations?
Dimension
Efficiency
What it measures
Resource usage and response quality
Example criteria
Token usage relative to answer quality; unnecessary verbosity; cost per quality point.
Experiments and prompt versioning
Version history gives full lineage: what prompt produced what scores at what time. Compare quality scores, cost, and latency across versions, so prompt changes ship with data, not anecdote.
Regulatory alignment
Built for Engineering Leads, Product Managers, Compliance
Related capabilities
LLM Observability: Trace Logging Built for Compliance
Structured traces give you the full story of what your AI said, why it said it, how long it took, and what it cost.
LLM Guardrails: PII Redaction and Prompt Injection Blocking
Real-time detection and enforcement for PII, PHI, prompt injection, content policy violations, and off-topic responses, scoped per agent, per project, per knowledge base.
Session Review: Conversation-Level AI Audit View
Compliance officers read sessions like chat transcripts: no JSON, no log parsing, no engineering ticket.
Prism X: AI DLP for Employees Using ChatGPT, Claude, Gemini
Prism X enforces data loss prevention policy in the browser, before prompts and uploads reach third-party AI services. Signed policy, real-time enforcement, audit-grade events.
Start tracing in 5 minutes
One SDK. Five minutes. Full audit trails, PII redaction, and guardrail enforcement, from day one.