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AI observability and governance

Govern. Observe. Evaluate. Improve.

Build trust in AI.

PrismAI connects prompts, model calls, tool activity, cost, latency, and evaluations across your LLM and agent applications.

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PrismAI overview showing trace volume, model costs, evaluation scores, and model usage.

One operating view for production AI

Turn every AI run into evidence.

Full observability

Follow requests, prompts, generations, tools, retries, and outcomes in one trace.

Measurable evaluations

Track quality scores, pass rates, regressions, and human review signals over time.

Controlled governance

Organize projects, users, environments, tags, and audit evidence around each AI workflow.

Actionable insights

Find expensive prompts, slow stages, failure clusters, and model-level performance changes.

Enterprise AI governance

Built for control, evidence, and confidence.

Give engineering and governance teams a shared record of what happened, why it happened, and how the system performed.

Policy and guardrails

Measure rule-based, human, and model-graded checks against traces and generations.

  • Quality scorecards
  • Safety evaluation signals
  • Custom scoring rules
  • Release comparisons

Access and ownership

Separate projects and environments while preserving clear user and session attribution.

  • Project isolation
  • User and session context
  • API key management
  • Environment filtering

Audit evidence

Keep prompts, outputs, scores, metadata, and timestamps connected to the run that produced them.

  • Trace history
  • Prompt versions
  • Evaluation records
  • Exportable evidence

Data governance

Keep observability data in your controlled deployment with explicit retention and access decisions.

  • Private deployment
  • Controlled storage
  • Retention policies
  • Sensitive-data discipline

End-to-end visibility

Observe every step of the AI lifecycle.

Move from a user request to an evaluated response without losing the prompts, model calls, tools, timing, cost, and quality signals in between.

  1. User requestInput and context
  2. LLM or agentPrompt and generation
  3. Tools and dataSpans and dependencies
  4. ResponseOutput and outcome
  5. EvaluationQuality, cost, and safety

Example metricTotal costBy project and model

Example metricTotal tokensInput and output usage

Example metricP95 latencyBy stage and generation

Example metricEvaluation pass rateAcross releases

Operate AI with evidence

Make AI performance visible and governable.

Use PrismAI to reduce blind spots, improve reliability, control cost, and explain production AI behavior with connected evidence.

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