Deeploy

About Deeploy

The journey to scaling AI is often fraught with unseen risks and regulatory complexity. What begins as a promising pilot can quickly descend into a fragmented landscape of unmonitored models and compliance blind spots. Deeploy is the essential guide on this journey, providing the governance infrastructure that responsible AI scaling demands. It is a comprehensive AI Governance Software platform designed for organizations that are moving beyond experimentation to deploying AI at scale. Deeploy centralizes oversight, compliance, and monitoring for all AI systems, transforming a chaotic "jungle of AI" into a controlled, trustworthy environment. It serves risk, compliance, and AI leadership teams who need to ensure their AI initiatives are transparent, compliant with regulations like the EU AI Act, and aligned with business values. The core value proposition is clear: take control. With flexible onboarding, real-time explainability, human feedback loops, and immutable audit trails, Deeploy empowers organizations to manage risk proactively and scale AI faster, without ever losing control or the trust of their customers and regulators.

Features of Deeploy

AI Discovery and Onboarding

Embark on your governance journey with complete visibility. This feature allows you to discover, onboard, and manage every AI system across your organization from a single, unified interface. Connect seamlessly to any MLOps or GenAI platform to eliminate blind spots, creating a centralized AI inventory without the pain of migrating your existing stack. It’s the foundational step that turns unknown risks into managed assets.

Control Frameworks

Navigate the complex regulatory landscape with a trusted map. Deeploy offers guided workflows using default frameworks like ISO 42001 and NIST AI RMF, or allows you to build custom ones tailored to your needs. You can classify AI system risk levels in minutes and establish clear accountability with structured approval processes. This turns the daunting task of compliance into a straightforward, manageable journey.

Control Implementation

This is where governance moves from policy to practice. Deeploy translates high-level frameworks into clear, actionable requirements for engineering teams. It accelerates compliance by up to 90% through pre-built templates and automatically collected evidence. By automating repetitive assessment work, it ensures governance is something engineers can actually follow, embedding it directly into the development lifecycle.

Real-Time Monitoring

Maintain vigilance on your AI journey, preventing incidents before they derail your progress. Monitor AI performance in real-time to detect model drift, performance drops, and anomalies before they impact end-users. The platform also enables you to add tracing and guardrails to protect LLM outputs, ensuring your AI systems operate safely and as intended, 24/7.

Use Cases of Deeploy

Achieving EU AI Act Compliance

For organizations operating in or selling to the European market, Deeploy provides the structured path to compliance. It helps classify high-risk AI systems, implement necessary controls, automatically gather evidence, and maintain the required documentation and audit trails. This turns a regulatory hurdle into a streamlined process, building trust with regulators.

Centralizing Oversight for Fragmented AI Estates

Large enterprises often find AI scattered across different teams, vendors, and embedded systems. Deeploy acts as the central command center, bringing all these systems into a single registry. This provides leadership with the oversight needed to understand what AI is running, where it is, and what risks it carries, transforming chaos into control.

Enabling Safe LLM and GenAI Deployment

Generative AI introduces novel risks around output quality, safety, and appropriateness. Deeploy’s real-time monitoring and guardrails allow teams to deploy LLMs with confidence. By tracing outputs and enabling human feedback loops, organizations can scale generative AI applications while ensuring they remain safe, reliable, and aligned with brand values.

Accelerating AI Model Deployment with Governance

Data science teams can move faster when governance is baked into the process. Deeploy integrates oversight directly into the MLOps pipeline, allowing models to be deployed quickly while automatically satisfying compliance checks and monitoring requirements. This removes the traditional friction between innovation and risk management, speeding time-to-value.

Frequently Asked Questions

How does Deeploy handle AI systems from different vendors and platforms?

Deeploy is built for flexibility and is designed to connect with any MLOps or Generative AI platform. Through its flexible onboarding process, you can integrate and govern AI models whether they are built in-house, hosted on major cloud providers, or sourced from third-party vendors. This creates a unified governance layer without forcing migration or vendor lock-in.

Can we customize Deeploy to fit our internal policies, not just external regulations?

Absolutely. While Deeploy comes with pre-built control frameworks for major standards like ISO 42001 and the NIST AI RMF, it is fully customizable. You can build, tailor, and implement your own internal governance frameworks and control sets to match your organization's specific risk appetite, ethical guidelines, and operational procedures.

What kind of real-time monitoring and alerts does Deeploy provide?

Deeploy monitors key performance indicators like accuracy, drift, data quality, and latency. It provides instant alerts when these metrics deviate from expected baselines or when anomalies are detected. For LLMs, it can monitor for prompt injections, toxic output, or data leakage, allowing teams to intervene before issues affect users or cause compliance breaches.

How does Deeploy facilitate human oversight and feedback?

Human-in-the-loop is a core principle. Deeploy provides tools for experts to review AI decisions, offer feedback, and override outcomes when necessary. This feedback is integrated directly into the model's lifecycle for continuous improvement. The platform’s explainability features also make model reasoning understandable, enabling meaningful and informed human oversight.