Objective assurance for every model you build, buy, or inherit.

Monitaur is the model governance platform for technical teams responsible for AI in production. It validates models before deployment and at runtime, applies model risk management discipline to predictive, generative, and agentic systems, and produces the evidence that validation actually occurred. Built for the model development lifecycle, not retrofitted from MLOps tooling.

Book a 30-minute technical walkthrough. See the validation workflows, drift and bias detection, and runtime monitoring across your model portfolio.

Documentation is not validation. A risk register is not oversight.

Model risk does not live in a spreadsheet. It lives in how a model behaves under inputs it was not trained on, how it drifts after deployment, and how it acts when chained into a multi-step system. Answering questions in a risk register does not test any of that.

Effective model governance runs validation that tests model behavior, applies controls proportional to model risk, and measures the residual risk that remains. The difference between organizing and governing is whether the validation actually executes.

Model risk management, extended to AI and ML.

Traditional model risk management was built for static, deterministic models. AI and ML systems are probabilistic, learn from changing data, and drift silently. Generative and agentic systems add multi-step behavior and read-and-write access to production systems. Monitaur extends established model risk discipline to all of them, with validation, monitoring, and evidence designed for how these models actually operate.

Model risk management, extended to AI and ML.

Stress test, red team, and validate before deployment.

Govern model risk across the full development lifecycle.

Monitaur applies a model risk governance framework across the AI model development lifecycle, from intake and validation through deployment, monitoring, and retirement. Model risk policy, ownership, lineage, and segregation of duties are tracked in one system of record. MLOps governance and model risk management operate as one workflow, not two disconnected stacks.

Monitor drift, detect bias, and keep models explainable.

After deployment, Monitaur monitors models in production for drift, degradation, and bias, with monitoring designed around each model's purpose rather than generic thresholds. Explainability and bias detection keep model behavior transparent to validators, auditors, and risk owners, including for generative systems where outputs are not deterministic.

From policy to proof: governance across the model development lifecycle

Monitaur delivers technical governance across the complete model development lifecycle, from intake to retirement.

AI inventory & catalog

One system of record for every model in production, including predictive, generative, agentic, and third-party models, with ownership, lineage, and risk classification.

Model explainability and bias detection

Methods for explaining model decisions and detecting bias before deployment and during operation, applied to deterministic and probabilistic systems alike.

Risk assessment & management

Risk tiering and structured assessment that scale controls to each model's risk, from low-risk internal tools to high-risk regulated decisions.

Automated policy compliance

Control libraries mapped to model risk and AI frameworks, including SR 11-7, NIST AI RMF, ISO 42001, and the EU AI Act, evidenced automatically.

Pre-deployment assurance

Validation against synthetic and adversarial test cases through APIs and libraries, run before models go live.

Runtime validation & continuous monitoring

Contextual monitoring of model behavior in production, with validation and performance signals tied to each model's intended purpose.

One platform. Every model type. From development to runtime. AI policy defines risk. Controls mitigate it.

A field guide to governing agentic and probabilistic AI

Accelerating Enterprise AI Returns is a strategic guide with a technical core, covering risk tiering, contextual monitoring, and the access controls agentic systems require, alongside the operational case for dedicated governance.

What's inside:

Why multi-step and agentic systems are fragile, and how risk tiering scales oversight to match
Contextual monitoring that distinguishes real drift from normal business behavior
The read-and-write access controls that autonomous agents demand

Enterprise-proven. Globally recognized.

Regulated enterprises reach full governance implementation in under 90 days, grow governed AI projects 8x, and realize millions in efficiency gains. Governance becomes the engine of AI growth, measured where it counts.

Fortune 200 financial services company

Scaled AI projects while reducing risk exposure

Insurance carrier

Full governance implementation and measurable ROI in under 90 days

Recognized by industry analysts

Gartner Market Guide for AI Governance Platforms: Representative Vendor

The Forrester Wave™: AI Governance Solutions, Q3 2025: Customer Favorite

Chartis AI Governance, 2025: Category Leader

FinTech Global InsurTech100 for 2025

Start with a technical walkthrough, not a commitment.

The fastest way to evaluate a model governance platform is to see it run. Bring a model, a validation question, or an agentic use case. A short technical walkthrough will show how Monitaur validates behavior, monitors drift, and evidences controls across the lifecycle.

Monitaur is the only customer favorite, category leader, and visionary in the market, according to analysts. Schedule time with the team that's leading the way in AI governance.