Claris now supports the RBI FREE-AI Framework — full automated compliance mappingLearn More
Conversational Governance

Ask Claris.

Get answers in plain English.

Any team member — data scientist, risk officer, or business analyst — can interrogate any model in natural language. Claris translates the question into metrics and returns a structured, explainable answer.

Key Metric
Zero Code Required
Plain-English QueriesPersona-Aware ResponsesAudit-Ready LogsEvery Model, Every Team
An enterprise AI governance platform by NodeX8 AI
app.claris.io / ask
Natural Language GovernanceReady
Can we check credit_v3 for bias, drift, and explainability as a glass box?

Evaluated credit_v3 across all three dimensions:

Bias: 0.04 ✓ LowPSI: 0.12 ✓ StableSHAP: Active ✓

Top driver: monthly_income (88%). No protected attributes in top 5 features. Model is compliant for production.

Summarise our FREE-AI compliance gaps for this model.

3 of 4 FREE-AI pillars compliant. Gap: Adverse Action Notice template not configured.

01 / 07
200+
AI Governance Metrics
40+
Model Algos Analysed
<4 hrs
Audit Response Time
99.7%
Explainability Consensus
The Problem

Your AI is making decisions
no one can explain

Traditional monitoring watches uptime and accuracy. It misses Silent Logic Decay— when model decision-making drifts dangerously while aggregate accuracy stays flat. Regulators don't accept "our dashboards looked green."

  • Single-method XAI tools give explanations you can't verify
  • Bias creeps in through proxy variables invisible to standard tools
  • Manual audit prep consumes 340+ analyst hours per cycle
The Claris Answer

Glass Box monitoring — inside your perimeter

  • Multi-algo consensus
    SHAP + LIME + surrogate models scored simultaneously. Every explanation certified or flagged.
  • 48-hour drift early warning
    PSI and concept drift detection alert your team before production impact.
  • Automated compliance evidence
    Continuous mapping to RBI, EU AI Act, IRDAI — audit-ready in under 4 hours.
  • Zero PII architecture
    Full governance engine in your VPC. Your data never moves.
How it works

Live governance in under 2 hours

01

Connect your model serving layer

A lightweight sidecar or webhook captures prediction inputs and outputs from MLflow, SageMaker, Vertex AI, or any REST endpoint — no code changes required.

02

Claris runs governance inside your VPC

XAI scoring, drift detection, fairness evaluation, and compliance mapping run entirely within your infrastructure. Zero PII ever transmitted.

03

Monitor, alert, and report in one place

A unified governance dashboard surfaces model health, bias signals, compliance gaps, and audit-ready reports — for every model, every team, in real time.

Natural Language Governance

Any question.
Any model.
In plain English.

Every dashboard in Claris speaks in natural language. Ask about bias, drift, or compliance — and get a structured, auditable answer without writing a single line of code.

Data Scientist"Compare drift scores for credit_v3 vs credit_v2 over the last 30 days"
Risk Officer"Summarise FREE-AI compliance gaps for our retail lending model"
Compliance"Which models have adverse action notices pending this quarter?"
Business User"Why was customer #4821 rejected and what would change that decision?"
See NLI in Action
app.claris.io / ask
Natural Language Interface● Live
Evaluate credit_v3 for bias, drift, and explainability as a glass box.

Evaluated credit_v3 across all three dimensions:

Bias: 0.04 ✓PSI: 0.12 ✓SHAP: Active ✓

Top driver: monthly_income (88%). No protected attributes in top-5 features. Ready for production.

Summarise FREE-AI compliance gaps for this model.

3 / 4 FREE-AI pillars ✓ compliant

✓ Explainability evidence — present

✓ Drift monitoring — active

✓ Fairness audit — passing

⚠ Adverse Action Notice template — not configured

Why was customer #4821 rejected?

Prediction: High Risk (0.82). Key factors: loan-to-value ratio exceeded threshold, employment tenure < 12 months. Counterfactual: reducing loan amount by 18% would flip to Low Risk.

Ask about any model…
LLM & Agent Governance

Govern what your LLMs
say and do.

Generative AI fails differently. Claris gives you an observability layer built for exactly these failure modes.

🔍

Hallucination Detection

Scores every output for factual grounding. Flags unverified claims before they reach users.

🛡️

Prompt Injection Guard

Detects adversarial instructions in both direct prompts and retrieved documents.

🗺️

Agent Trajectory Tracing

Immutable step-by-step replay of every agent action for audit and incident investigation.

📊

RAG Quality Scoring

Measures retrieval precision, context relevance, and answer faithfulness continuously.

Research Paper

The Shadow and the Shield:
Navigating the Global AI Governance Gap

How 195+ countries across the globe bridge the gap between policy ambitions and technical observability. Six archetypes of countries are developed on resilience, maturity and ambitions of AI governance.

  • 50+-page deep-dive on 5 governance dimensions
  • Six cluster benchmarks and readiness velocity over the years
  • 5+ global datasets integrated and analyzed
Read the Research Paper
Get started

Ready to govern
your enterprise AI?

Schedule a 30-minute technical walkthrough and see Claris running on your own models within the first session.

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