how it works

The Control Tower for All Enterprise AI

See every AI solution, control every risk, prove every result. ModelOp automates end-to-end AI lifecycle management and governance for visibility into all internal and vendor AI, faster time-to-market with trust by design, and continuous control over costs and usage.

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How ModelOp Streamlines Each Step in the AI lifecycle

ModelOp provides self-service governance with role-based workflows that
automate every step of the end-to-end AI lifecycle by integrating with and orchestrating your enterprise’s many processes and systems related to AI innovation and governance so you can bring AI to market faster.

01

Submit a New AI Use Case

  • Standardize AI use case intake and registration
  • Initiate the end-to-end AI lifecycle record
  • Automatically ensure business, risk, and portfolio reviews are conducted
02

Assess Risk of Use Case Based on Policy

  • Codify risk assessments for every AI use case
  • Auto-generate the risk tier for each use case
  • Auto-generate initial controls based on risk
03

Implement the AI Solution

  • Track and manage the vendor or internal solution details
  • Submit candidate AI solution through approval workflows to enforce reviews and policies
  • Ensure the solution submission is verified and documented
04

Conduct Testing

  • Continuosly run automated tests such as bias, drift, performance, and more
  • Continuously track risks and collect evidence to stay audit-ready
  • Automatically generate documentation and model cards
05

Review & Deploy

  • Quickly request independent reviews
  • Automaticially trigger notifications and alerts
  • Consistenly enforce policies for approving production deployment
  • Track and manage production deployments
06

Continuously Monitor & Review

  • Monitor all use cases in production from a single system of record
  • Easily conduct regular reviews
  • Quickly perform annual attestations
  • Automatically update policies and processes
modelop introductory demo

See How ModelOp Brings AI to Market Faster and at Scale

This 6-minute introductory demo shows how ModelOp’s AI lifecycle management and governance platform establishes visibility into all AI, gets AI into production faster with enforceable policies, controls costs, and integrates with existing enterprise systems to orchestrate governance.

Transform responsible ai

Operationalize AI Lifecycle Management and Governance

ModelOp provides the technical and operational backbone to implement responsible AI and balance its costs, risks, and value.

Without ModelOp

With ModelOp

Fragmented and invisible AI

Leaders don’t know what AI is being used, where it’s being used, who’s using it, and what the risks are.

Single AI system of record

Full visibility into the usage and risk of all AI across the enterprise from a single source of truth.

Siloed teams and manual processes

Manual operations across dozens of teams, systems,  processes delay AI solutions by months or more.

Interoperability and orchestration

United collaboration, integrated systems, and continuous governance that accelerate AI time-to-market.

Inconsistent policy enforcement

Policies are inconsistently applied and enforced across different teams, business units, and geographies.

Automatic, consistent enforcement

100% assurance that all policies—internal and regulatory—are enforced across the entire enterprise.

No cost, value, or risk tracking

Limited ability to track the cost, benefits, and risks of AI use cases and align them to business objectives.

Continuous insights

Automated tracking and reporting on KPIs to identify bottlenecks, measure ROI, and inform business decisions.

FAQs about ModelOp and AI Lifecycle Management & Governance

1
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What is AI lifecycle Management and why is it important?

2
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Why is it important to manage AI as a portfolio of investments?

3
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How is ModelOp different from AI Development and MLOps tools?

4
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How is ModelOp different from Data Management and Data Governance platforms?

5
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How is ModelOp different from Governance, Risk, and Control (GRC) platforms?

6
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How is ModelOp different from IT Service Management (ITSM) systems?

7
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How is ModelOp different from AI Observability platforms?

8
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How is ModelOp different from IBM watsonx.governnace?

9
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How is ModelOp different from ServiceNow?

10
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How is ModelOp different from Databricks?

11
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How is ModelOp different from Collibra?

12
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How is ModelOp different from Credo.ai?