AI Observability & Governance Platform
See every AI decision. Score every response. Prove every control.
PRISMtrace is the observability and governance platform for teams running LLMs and AI agents in production. Capture traces, enforce guardrails, evaluate quality, and generate compliance evidence from one platform.
- 121
- compliance controls
- 5
- frameworks built in
- <5 min
- to first trace
- real-time
- guardrail enforcement
Capabilities
One platform, every observability primitive
Capture, enforce, score, and prove. The same data that runs your AI in production produces the evidence your examiners reference.
Traces and Sessions
Every prompt, completion, tool call, and agent step captured as a structured, queryable trace. Sessions group multi-turn conversations into a single thread.
Guardrails and PII Redaction
Real-time inbound and outbound scanning for PII, PHI, prompt injection, and policy violations. Allow, flag, block, or redact, scoped per agent, project, or knowledge base.
Quality Evaluations
Five-dimension scoring across Accuracy, Relevance, Completeness, Safety, and Efficiency. Continuous, automated, on every trace.
Agent Trajectory Tracing
Multi-step agent runs decomposed into ordered steps and scored on goal adherence, tool compliance, efficiency, and safety, asynchronously on ingest.
Model Audits
Structured pre-deployment review of model behavior, risk profile, and readiness. Side-by-side comparison across candidates and versions.
Red Teaming
Adversarial testing for prompt injection, guardrail bypass, information extraction, and multi-turn escalation, before models reach production.
Integrations
Zero-code proxy, Python SDK, LangChain and LangGraph callbacks, OpenTelemetry, plus Databricks, Snowflake, Azure, and AWS connectors.
Why it matters
Production AI is a black box by default. PRISMtrace makes every prompt, response, and agent step accountable, queryable, and audit-ready — from one platform.
Traces & Sessions
Every conversation, every call, every token, captured.
Structured traces give you the full story of what your AI said, why it said it, how long it took, and what it cost. Sub-millisecond overhead on your application's critical path.
- Spans for every operation: LLM, tool, retrieval, guardrail
- Sessions group multi-turn conversations as one thread
- Custom metadata for slicing by segment, flag, or environment
- Real-time ingestion with sub-millisecond overhead
Guardrails & PII
Stop sensitive data and unsafe content before it reaches users.
Real-time inbound and outbound scanning for PII, PHI, prompt injection, and policy violations. Allow, flag, block, or redact, scoped per agent, per project, per knowledge base.
- Six built-in detection categories with format and checksum validation
- Inbound scans prompts; outbound scans model responses
- Four dispositions per rule: Allow, Flag, Block, Redact
- Scoped per agent, project, or knowledge-base topic
Evaluations
Measure what good looks like, automatically, on every trace.
Five-dimension scoring across Accuracy, Relevance, Completeness, Safety, and Efficiency. Catch regressions before users do, with rubrics you define and evaluators that run continuously.
- Five quality dimensions, scored on every interaction
- Define rubrics from templates or custom-built for your domain
- Experiments and prompt versioning with statistical rigor
- Human-in-the-loop annotations for edge cases
Agent trajectories
See every step your agent took, and score whether it should have.
Trajectory evaluation decomposes multi-step agent runs into ordered steps and scores each on goal adherence, tool compliance, efficiency, and safety, asynchronously on ingest.
- Steps, tool usage, decision points, and final outcome captured
- Four-dimension scoring on every trajectory
- Background async scoring, zero impact on agent latency
- Automatic for Claude tool-use via proxy
Why PRISMtrace
Built for teams where a wrong answer has a cost.
Compliance-native
121 controls mapped across NIST AI RMF, EU AI Act, NAIC, NYDFS, and CFPB. Deterministic verdicts from observable platform state.
Sub-5-min onboarding
Zero-code proxy gets a first trace flowing in minutes. SDK, callbacks, and OpenTelemetry for teams that want explicit instrumentation.
Continuous evaluation
Five-dimension scoring on every trace. Catch model drift and quality regressions before users escalate.
Self-hosted option
Deploy in your own VPC for data-residency-sensitive workloads. Native connectors for Databricks, Snowflake, Azure, and AWS.
Integrations
Connects to the platforms your data already lives on.
Native connectors for Databricks, Snowflake, Azure, and AWS. Plus zero-code proxy, Python SDK, LangChain callbacks, and OpenTelemetry for everything else.
See all integrationsAligned to the frameworks your examiners reference
Frequently asked
Common questions
How long does it take to see my first trace?▾
Under five minutes with the zero-code proxy. Swap one base URL on your LLM client and every call becomes a structured trace. The Python SDK takes under an hour to wire up if you want explicit decorators or context managers.
Where does the trace data live?▾
Your PRISMtrace tenant by default. For data-residency-sensitive workloads, we offer self-hosted deployment in your own VPC, plus native export to Databricks Delta Lake, Snowflake, AWS S3, and Azure.
What's the difference between guardrails and evaluations?▾
Guardrails enforce policy in real time: PII detection, prompt-injection blocking, content moderation. Evaluations score quality after the fact across five dimensions (Accuracy, Relevance, Completeness, Safety, Efficiency). One blocks bad outcomes; the other measures whether outcomes are good.
How do compliance reports get generated?▾
PRISMtrace maps 121 controls across NIST AI RMF, EU AI Act, NAIC, NYDFS, and CFPB to observable platform state — traces logged, guardrails active, evaluation coverage, PII detection enabled. Verdicts are deterministic (PASS / WARNING / FAIL), not LLM-generated opinions. Controls requiring external data default to WARNING with clear guidance.
Does PRISMtrace work with Anthropic Claude?▾
Yes. Claude tool-use trajectories are captured automatically via the proxy, with no manual instrumentation. The same applies to OpenAI, Azure OpenAI, AWS Bedrock (multi-model), Databricks Mosaic AI, and Snowflake Cortex.
Production AI shouldn't be a black box.
Capture traces, enforce guardrails, evaluate quality, and produce compliance evidence from one platform. See your first trace in under five minutes.