SuperviseAI — Trust in AI through Monitoring, Evaluation and Autodidact Learning
Why SuperviseAI?
Trusted Quality Benchmarks
Our LLM-as-a-Judge technology leverages safety-grade taxonomies, nuanced rubrics, and evidence-rich context — not just pass/fail checks.
Human Oversight
We use expert HITL where it matters. Calibrated reviewers, adjudication queues, and IRR/bias checks ensure sensitive judgments are consistent and defensible.
Real-Time, Actionable Reports
We provide full transparency with real-time dashboards, error case audit trails, and data-science grade metrics to clarify agent performance.
Ongoing, Automated Improvement
From monitoring to continuous quality improvement, we close the loop through a reinforcement feedback learning cycle powered by error analysis and human-in-the-loop context.
Creating Trust with Quality Measurement
SuperviseAI provides incident rates (safety, leakage, hallucinations), grounded-answer scores, time-to-adjudication, and clear error cases that measure your AI agent quality.
How to Use SuperviseAI
SuperviseAI empowers teams to confidently deploy and manage AI agents by providing continuous oversight, performance evaluation, and real-time learning. It helps you ensure that your AI systems act responsibly, deliver consistent results, and comply with safety and privacy standards.
With SuperviseAI, you gain clear visibility into every action your AI takes — so you can trust its performance, correct mistakes instantly, and sleep well knowing your AI agents are always under watch.
Common Evaluators
Out-of-the-box:
Common evaluators out-of-the-box include safety and toxicity checks, PII/PHI leakage and redaction verification, prompt-injection and jailbreak resistance, hallucination and grounding (RAG) assessments, action guardrails for allow/deny/approval, brand, sentiment, and suitability analysis for UGC or ads, as well as bias, harassment, denial, and frustration detection.
Where SuperviseAI Shines
Financial Services
Employee copilot and customer-facing chatbot supervision with audit-ready artifacts.
Healthcare
Monitoring and evaluating AI output for personalized treatment plans and care coordination.
Public Sector & Regulated Industries
Continuous, policy-mapped evaluations with HITL oversight.
UGC Platforms & Advertisers
Policy violation decisions with appeals to reduce over-blocking.
Agentic Startups & Entreprise Teams
Quality monitoring (accuracy, consistency, safety), drift alerts, and improvement loops as you scale agents.
What SuperviseAI Does
1. Always-On Quality & Risk Monitoring
Continuous, policy-driven evaluations across your critical agent flows (FAQ, search, summarization, ticket reply, actions/tools). Early drift alerts notify you so you can minimize regressions impact on customers.
2. LLM-as-a-Judge, Reconciled with Humans
Policy-driven classifiers judge the quality measures that are most important to your business, like completeness, tone, bias, and safety / no-harm. All quality decisions include human-readable, step-by-step rationales for transparency.
3. Human-in-the-Loop Adjudication
SuperviseAI routes disagreements and high-risk items to expert reviewers (your employees or outsourced to TrustLab or a 3rd party) with calibration, κ/IRR tracking, and dispute resolution workflows—then syncs final verdicts back to agent developers.
4. Reinforcement Learning and Reporting
Policy mapping, scorecards and datasets are self-serve accessible and exportable so you know how your AI agent’s quality is measuring up, agent developers know how to improve quality, and your compliance/oversight reports are always current.
Test & Integrate SuperviseAI in Minutes
1. Connect your agents via API/MCP
2. Select quality evaluation suites
3. Observe LLM judge disagreements
4. Adjudicate with exper reviewers
5. Leverage judge+human feedback
6. See proof of quality improvement
Our Differentiation -
Certified AI Agent Quality
Know what your AI agent does - with certainty
Our roots in nuanced, subjective, and high-stakes judgments (beyond “routine” labeling) give you safety-grade accuracy—with less human review because we target the right samples.
Visualize AI agent health in dashboards
Gain instant clarity into how your AI agents perform, behave, and improve — with live metrics that turn invisible risks into actionable insights.
Auditor-ready from day one
Versioned suites, evidence bundles, and clear error case analysis without months of bespoke work.
Automate AI agent learning processes
Continuously optimize your AI agents with closed-loop feedback and reinforcement learning that scales quality without manual retraining.
Trust through Evidence - Metrics that Matter
Quality & Safety
• Safety Pass Rate
• Leakage Index
• Grounding Score (Factuality@K)
Reliability & Performance
• Jailbreak Resistance
• Action Error Rate
• Approval SLA
Human Review Quality
• IRR (κ)
• Dispute overturn %
• Time-to-adjudication (TTA)
Monitoring & Compliance
• Drift
• Regression alerts
• Release-gate status
Key Capabilities
• Policy-as-Code Engine: Versioned policies, taxonomies, and thresholds with change history and stakeholder sign-off.
• Evaluator Orchestrator: Batch/CI runs, scheduled jobs, sampling plans, drift detection, and “LLM-judge vs. human” reconciliation.
• HITL Workbench: Calibration sets, κ/IRR, dispute handling, annotator QA metrics, bias dashboards.
• Data Provenance Ledger: Source & consent tracking, dataset datasheets, acceptance criteria, and immutable logs.
• Optimization Loops: Preference data capture, reinforcement learning hooks to raise scores, not just measure them.
Plan Options
Pilot
2 Evaluation Suites
1 Environment
25k Evals/Mo
Core Reports
Check Our Performance based on a Free Pilot
Pro
5 Evaluation Suites
3 Environments
100k Evals/Mo
HITL Workbench
Evidence Bundles
Enterprise
Custom Evaluation Suites
Unlimited Environments
Enhanced SLAs
Dedicated Support
Ready to de-risk your AI
agents and prove quality?
Frequently Asked Questions
Your agents are moving fast.
Make sure they're moving safely and measurably.
© Copyright 2025
Trust and Safety Laboratory Inc.
Palo Alto, CA
