AI Compliance Vendors

ModelOp

ModelOp, Inc.

Enterprise AI lifecycle management and governance platform

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Last verified April 23, 2026

Quick facts: ModelOp is an AI compliance vendor founded in 2018 and headquartered in Chicago, United States. The vendor publicly documents coverage for EU AI Act, NIST AI RMF, and ISO/IEC 42001. Pricing is available on request. Profile last verified April 23, 2026, with every claim traceable to a cited public source.

About ModelOp

ModelOp provides a centralized platform for managing the full AI lifecycle, from intake to retirement, for ML, GenAI, Agentic AI, and vendor models. It offers a single system of record for AI inventory, automates policy enforcement and workflows, enables continuous monitoring for risks like bias and drift, and generates audit-ready reports. Targeted at complex regulated enterprises, it integrates with existing systems to accelerate AI deployment while ensuring compliance and control across teams. Distinct from MLOps tools or GRC systems, ModelOp orchestrates governance end-to-end, supporting internal and third-party AI at scale.

Featured in

ModelOp is ranked in the following independent collection.

Frameworks supported

Regulations and voluntary standards ModelOp documents support for on their own materials. Chip shading reflects the strength of the claim, not an independent audit.

EU Artificial Intelligence Act

Regulation · EU · in force

Full
NIST AI Risk Management Framework

Voluntary standard · US · voluntary

Full
ISO/IEC 42001:2023 AI Management System

Voluntary standard · Global · voluntary

Full

ModelOp features

Capabilities ModelOp markets publicly. Inclusion means the feature is documented on the vendor's site — not that it's best-in-class. Last verified April 23, 2026.

AI Model Inventory

Centralized registry of all AI/ML models in use across the organization, with ownership, lifecycle stage, and risk classification.

Policy Management

Authoring, versioning, and distribution of AI usage policies mapped to regulations.

Risk Assessment Workflow

Guided workflows for completing AI impact assessments, risk scoring, and approval routing.

Bias & Fairness Testing

Automated statistical testing for disparate impact across protected attributes, with audit-ready reports.

Explainability

SHAP, LIME, counterfactual, and feature-importance explanations for model decisions.

Model Monitoring

Production monitoring for performance, drift, data quality, and fairness regressions.

Audit Evidence Collection

Automated collection, hashing, and retention of evidence (model cards, test results, approvals) for audit.

Integrations

Documented by ModelOp in public product materials.

  • AWS SageMaker
  • Azure ML
  • Google Vertex AI
  • Databricks
  • Snowflake
  • MLflow
  • Jira
  • ServiceNow
  • OpenAI API

ModelOp pricing

No public pricing listed; contact sales for enterprise quotes.

Third-party reviews

Publicly cited ratings from independent review platforms. We don't collect reviews ourselves and we don't weight these in our rankings. Low review counts (≤2) are noted so you can decide how much signal to take from them.

G2

6 reviews

4.9 / 5View on G2

Frequently asked

What is ModelOp?+

ModelOp is a AI governance platform headquartered in Chicago, United States, founded in 2018. Profile last verified April 23, 2026.

How much does ModelOp cost?+

ModelOp does not publish pricing. No public pricing listed; contact sales for enterprise quotes.. Verified April 23, 2026.

Which AI compliance frameworks does ModelOp support?+

ModelOp documents support for EU AI Act, NIST AI RMF, and ISO/IEC 42001 in its public materials. Coverage strength varies — see the framework chips above.

What does ModelOp integrate with?+

ModelOp publicly documents integrations with AWS SageMaker, Azure ML, Google Vertex AI, and Databricks, and 5 more. See the integrations list above for the full set.

Who is ModelOp for?+

ModelOp markets to Financial Services, Healthcare, and Insurance teams and other regulated industries. Match it against your specific framework and integration requirements before committing.

Sources

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