Govern What Your LLMs Say and Do
Hallucination detection, prompt injection guards, RAG quality scoring, and full agent trajectory tracing — the observability layer your generative AI stack is missing.
Generative AI Has a Different Failure Surface
LLMs fail in ways that traditional monitoring was never designed to catch: they hallucinate facts, accept injected instructions, return inconsistent outputs across identical prompts, and execute multi-step agent actions with no replay trail. Regulated enterprises cannot deploy generative AI at scale without an observability layer purpose-built for this failure mode.
Four-Layer LLM Governance Pipeline
Runs inside your VPC alongside your LLM serving infrastructure — no data leaves your perimeter.
Intercept & Score
A lightweight sidecar intercepts LLM inputs and outputs at inference time. Every prompt–response pair is scored for hallucination risk, injection signals, and output consistency — in under 2 seconds.
RAG Quality Audit
For retrieval-augmented pipelines, Claris scores retrieval precision, context relevance, and answer faithfulness against the retrieved context. Identifies when the model ignores its own retrieval or fabricates beyond it.
Agent Trajectory Tracing
For multi-step agent workflows, every tool call, reasoning step, and intermediate output is logged to an immutable trace. Replay any agent run step-by-step for audit or debugging.
Alert, Explain & Report
Anomalous outputs are flagged with natural-language explanations of the failure mode. Evidence is assembled into compliance-ready reports mapped to your governance framework.
Capabilities Built for Generative AI in Production
Hallucination Detection
Scores every LLM output for factual grounding. Flags unverified claims, citation fabrication, and reasoning-chain drift using ensemble consistency checks.
Prompt Injection Guard
Detects adversarial prompt injections that attempt to hijack model behavior — including indirect injection via retrieved documents in RAG pipelines.
Agent Trajectory Tracing
Immutable, step-by-step trace of every agent action, tool call, and intermediate output. Full replay capability for audit and incident investigation.
RAG Quality Monitoring
Measures retrieval precision, context relevance, and answer faithfulness continuously. Detects when retrieved context is ignored or contradicted by the model.
Semantic Drift Detection
Monitors output distribution across LLM versions and fine-tunes. Catches behavioral regression when a model is updated — before it surfaces as a production incident.
Compliance Evidence Trail
Every scored inference, flagged anomaly, and agent trace is logged to the immutable Claris audit ledger — providing the evidence layer regulators will require for GenAI.
How Hallucination Scoring Works
Claris uses an ensemble of three detection strategies: (1) Consistency sampling — the same prompt is run N times; divergence in outputs signals low-confidence generation. (2) Retrieval grounding — for RAG pipelines, claims in the output are matched against the retrieved context using semantic similarity thresholds. (3) Knowledge boundary detection — outputs are checked against a domain knowledge graph to flag assertions outside the model's verifiable training distribution. A composite Hallucination Risk Score (HRS) from 0–1 is returned per inference.
Ready to Govern Your LLMs and Agents?
See Claris LLM & Agent Governance running on your own generative AI stack in a 30-minute technical walkthrough.