Deeploy

About Deeploy

Deeploy is an enterprise-grade AI Governance platform designed to provide organizations with centralized oversight, compliance, and monitoring for all their AI systems. It acts as the essential governance infrastructure for modern AI stacks, enabling businesses to scale their AI initiatives confidently while mitigating associated risks. The platform is built for organizations running AI at scale, from large enterprises to regulated industries, who need to maintain control over a fragmented landscape of machine learning models, generative AI applications, and embedded AI systems. Deeploy's core value proposition lies in transforming AI from an unmanaged "jungle" into a controlled, transparent, and compliant asset. It directly addresses critical challenges such as the lack of a central AI inventory, difficulties in complying with regulations like the EU AI Act, and the absence of real-time monitoring and audit trails. By offering flexible onboarding, actionable control frameworks, and real-time explainability, Deeploy helps organizations build trust in their AI, prevent incidents, and prove compliance efficiently.

Features of Deeploy

AI Discovery and Onboarding

This feature provides complete visibility across an organization's entire AI landscape. It allows teams to discover, onboard, and manage every AI system from a single, unified interface. Deeploy connects seamlessly to any existing MLOps or GenAI platform, eliminating blind spots without requiring costly and complex migrations. This centralized registry is the foundational step for governance, offering flexible onboarding options that bring all AI assets under management, from legacy models to the latest large language model (LLM) applications.

Control Frameworks

Deeploy simplifies regulatory compliance by offering guided workflows through established and custom control frameworks. Organizations can choose from default frameworks like ISO 42001 and the NIST AI RMF or build their own tailored policies. The platform enables teams to classify AI system risk levels in minutes and establishes clear accountability through structured approval processes. This turns complex regulatory requirements into a manageable, systematic process, helping organizations navigate the AI Act and other standards with confidence.

Control Implementation

This feature translates high-level governance policies into enforceable, engineer-friendly controls. It ensures that every AI system automatically receives the correct, actionable requirements without manual overhead. Deeploy accelerates compliance by up to 90% using pre-built templates and automatically collecting evidence. Furthermore, it employs AI-powered assessments to handle repetitive compliance tasks, making governance practical and something development teams can actually follow and integrate into their workflow.

Real-Time Monitoring

Deeploy offers proactive monitoring to prevent AI incidents before they impact users or create compliance risks. It tracks AI performance in real-time, providing instant alerts for issues like model drift, performance degradation, or output anomalies. The platform includes capabilities for adding tracing and guardrails to protect LLM outputs. This continuous oversight allows organizations to identify and rectify errors before end-users encounter them, ensuring reliable and safe AI operations in production.

Use Cases of Deeploy

Achieving EU AI Act Compliance

Organizations operating in or selling to the European market can use Deeploy to systematically meet the stringent requirements of the EU AI Act. The platform's control frameworks, risk classification tools, and automated evidence collection streamline the process of documenting conformity, conducting risk assessments, and ensuring human oversight. This structured approach turns a complex regulatory challenge into a manageable operational procedure.

Centralizing Oversight for Fragmented AI Portfolios

Companies with AI systems scattered across different teams, vendors, and embedded platforms use Deeploy to gain a single source of truth. The discovery and onboarding feature creates a centralized inventory, providing leadership with complete visibility and control. This eliminates blind spots, reduces shadow IT, and enables consistent governance policies to be applied across all AI assets, from financial forecasting models to customer service chatbots.

Implementing Human-in-the-Loop for High-Stakes AI

In sensitive sectors like healthcare, finance, or mental health services, Deeploy facilitates crucial human oversight. The platform's explainability features and feedback loops allow experts to understand AI reasoning and intervene when necessary. This enables the safe deployment of AI in critical applications, such as clinical support tools, by ensuring that AI decisions are transparent and can be validated or overridden by human professionals.

Scaling MLOps with Governance-by-Design

AI and data science teams use Deeploy to embed governance directly into their MLOps lifecycle. By integrating governance controls, monitoring, and audit trails from the initial deployment phase, teams can scale their production models faster without sacrificing control or creating technical debt. This results in faster, compliant deployments and provides non-technical stakeholders with the transparency needed to trust and approve AI-driven processes.

Frequently Asked Questions

What types of AI systems can Deeploy manage?

Deeploy is designed as a platform-agnostic governance layer. It can manage a wide variety of AI systems, including traditional machine learning models deployed via platforms like MLflow or Sagemaker, generative AI applications built on OpenAI or Anthropic APIs, and AI embedded within third-party vendor software or internal applications. The flexible onboarding process is built to connect with and govern any AI asset in your organization's portfolio.

How does Deeploy help with AI explainability?

Deeploy provides built-in explainability tools that help users understand how AI models arrive at their predictions or outputs. This is crucial for debugging, building trust, and meeting regulatory requirements for transparency. The platform generates explanations that are accessible to both technical and non-technical stakeholders, enabling data scientists to validate model behavior and business users to understand the rationale behind automated decisions.

Can we customize governance frameworks to match our internal policies?

Yes, absolutely. While Deeploy offers out-of-the-box templates for major standards like ISO 42001 and the NIST AI RMF, it also provides full flexibility to build custom control frameworks. Organizations can define their own policies, risk categories, control requirements, and approval workflows to align perfectly with their unique internal governance, risk, and compliance (GRC) standards and industry-specific needs.

How does the real-time monitoring feature work?

Deeploy's monitoring continuously tracks the performance and behavior of AI systems in production. It uses predefined metrics and anomaly detection to spot issues like data drift (where live data diverges from training data), concept drift, or sudden drops in accuracy. When a potential issue is detected, the system triggers instant alerts to designated teams, allowing them to investigate and intervene before the problem affects business operations or end-users.