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
Every conversation, every call, every token, captured and queryable
Structured traces give you the full story of what your AI said, why it said it, how long it took, and what it cost.
- Traces: full lifecycle from initial prompt through final response
- Spans: individual operations with latency, tokens, model, and cost
- Sessions: multi-turn conversations grouped as coherent threads
- Custom metadata: slice by user segment, feature flag, environment, A/B variant
The problem
Your LLM application handles hundreds or thousands of interactions daily. Without structured logging, you are debugging from user complaints and guessing at cost. When an auditor asks what your AI told a customer on a specific date, you cannot answer.
Capabilities
What you get with Prism
Traces
The full lifecycle of a user interaction: from initial prompt through final response, with every intermediate step captured.
Spans
Individual operations within a trace: LLM calls, tool invocations, retrieval queries, guardrail evaluations. Each with latency, token counts, model identifier, and cost.
Sessions
Related traces grouped into user sessions, so multi-turn conversations appear as a single thread instead of isolated API calls.
Metadata
Custom key-value pairs your application attaches: user segment, feature flag, environment, A/B variant. Slice and filter by what matters to your business.
Real-time ingestion
Sub-millisecond overhead on your application's critical path. Search, filter, and drill into any trace from the explorer in real time.
CSV and JSON export
Export filtered traces for offline analysis, regulator submissions, or evidence packages.
How it works
From instrumentation to evidence
- 1
Instrument your stack
Pick the path that fits: Python SDK decorators and context managers, LangChain or LangGraph callbacks, OpenTelemetry spans, or zero-code proxy.
- 2
Stream traces in real time
Traces flow to your PRISMtrace tenant with sub-millisecond overhead on the application's critical path.
- 3
Search and drill in
Filter the explorer by time range, model, cost, latency, guardrail outcome, evaluation score, or custom metadata, then drill into any trace.
What teams use it for
In production, every day
Debugging production issues
Open the trace for a hallucinated response, see the retrieval miss, and pinpoint the prompt that caused the drift.
Cost management
Track token usage and cost per model, per feature, and per customer segment to catch runaway spend before the invoice arrives.
Audit response
Pull the exact trace for a specific interaction on a specific date, with timestamps, model versions, and full content.
Data model
What PRISMtrace captures
Traces
The full lifecycle of a user interaction, from initial prompt through final response, including every intermediate step.
Spans
Individual operations within a trace: LLM calls, tool invocations, retrieval queries, guardrail evaluations. Each with latency, token counts, model, and cost.
Sessions
Related traces grouped into user sessions so multi-turn conversations appear as a coherent thread, not isolated API calls.
Metadata
Custom key-value pairs your application attaches: user segment, feature flag, environment, A/B variant. Slice and filter by what matters to your business.
Regulatory alignment
Built for Developers, Engineering Leads, Compliance
Related capabilities
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.
LLM Evaluations: Five-Dimension Automated Quality Scoring
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.
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.