Adeptiv AI is an enterprise AI governance platform built to help organizations discover, classify, and manage their AI inventory while automating compliance across more than 30 regulatory frameworks. In this Adeptiv AI review, we examine how the platform handles AI risk management, model monitoring, and audit readiness for regulated industries like banking, healthcare, and HR. The tool targets enterprises running dozens or hundreds of AI models that need centralized governance without building custom compliance workflows from scratch.
Overview
Adeptiv AI operates in the fast-growing AI governance market, where organizations face mounting pressure from regulations like the EU AI Act, ISO 42001, and NIST AI RMF. The platform positions itself as a full-lifecycle governance solution: from initial AI inventory discovery through ongoing production monitoring and audit reporting.
The primary audience is mid-to-large enterprises in regulated sectors -- banking and financial services (BFSI), healthcare, and HR -- where AI deployments carry significant compliance and reputational risk. Unlike broader data governance tools such as Collibra or Alation, Adeptiv AI focuses specifically on AI-related governance rather than general data cataloging.
The platform supports mapping to 30+ regulations simultaneously, which is a notable differentiator. Most competitors either cover a handful of frameworks or require manual mapping. Adeptiv AI also offers flexible deployment across SaaS, private cloud, and on-premises environments, addressing a common enterprise requirement for data residency control.
Key Features and Architecture
Adeptiv AI structures its governance capabilities around five core pillars: discovery, compliance, risk, monitoring, and reporting.
AI Inventory Discovery and Lifecycle Management. The platform auto-discovers AI assets across an organization's infrastructure, cataloging models, datasets, and use cases. Each AI use case gets tracked through its full lifecycle -- from development through deployment and retirement. This eliminates the common problem of shadow AI, where teams deploy models without central visibility.
Compliance Automation. Adeptiv AI maps AI use cases to 30+ regulatory frameworks including the EU AI Act, ISO 42001, and NIST AI RMF. The system auto-suggests controls and evidence requirements for each regulation, reducing the manual effort of compliance mapping. Policy management features let governance teams draft tailored AI policies directly within the platform.
Risk Scoring and Management. The platform auto-detects risk levels for each AI use case based on regulatory classification (e.g., high-risk under EU AI Act Article 6). Risk assessments feed into a centralized dashboard where teams can track mitigation actions and control implementations.
Real-Time Monitoring and Bias Detection. Production models are monitored for drift and bias using integrated fairness testing via SHAP and LIME explainability methods. This goes beyond simple performance monitoring -- teams get alerts when model behavior shifts in ways that could trigger regulatory concerns.
Enterprise Integrations and Access Control. The platform connects to common data and ML infrastructure through REST API integrations with Snowflake, Databricks, MLflow, GitHub, S3, and identity providers like Okta. RBAC controls allow granular permission management across governance, compliance, and data science teams. Deployment options span SaaS, private cloud, and on-premises installations.
Ideal Use Cases
Regulated financial institutions managing 20+ AI models across lending, fraud detection, and customer scoring will benefit most. These organizations face simultaneous compliance demands from the EU AI Act, local banking regulators, and internal risk frameworks.
Healthcare organizations using AI for diagnostics, patient triage, or claims processing need the bias detection and audit trail capabilities. SHAP and LIME integration provides the explainability that healthcare regulators increasingly demand.
Enterprise AI governance teams tasked with creating a centralized AI registry across business units will find the auto-discovery and lifecycle management features directly useful.
Not the right fit for: startups with only 1-2 AI models in production -- the governance overhead will exceed the value. Teams seeking open-source solutions should look at OpenMetadata or DataHub instead. Organizations without dedicated AI governance staff will struggle to operationalize the platform effectively.
Pricing and Licensing
Adeptiv AI uses an enterprise pricing model with no publicly listed dollar amounts. All paid tiers require contacting sales for a quote.
The platform offers a 30-day free trial with 1 user seat and 2 AI use cases, providing full feature access on SaaS deployment. This is enough to evaluate the core workflow but too limited to test at enterprise scale.
Three paid tiers are available: Starter supports up to 10 users and 20 AI use cases on SaaS deployment. Private Cloud Enterprise allows custom user counts and AI use cases on private cloud infrastructure. On-Premises Enterprise provides unlimited users with custom AI use case limits on self-hosted infrastructure.
Pricing scales based on user seats and the number of AI use cases managed. Annual prepayment offers a 15-20% discount. SaaS plans carry no setup fees, while private cloud and on-premises deployments may include implementation costs. The lack of published pricing makes it difficult to compare directly with competitors, though this is standard for enterprise AI governance tools.
Pros and Cons
Pros:
- 30+ regulation mapping in one platform -- covers EU AI Act, ISO 42001, NIST AI RMF, and sector-specific frameworks simultaneously, saving teams from managing separate compliance workflows for each regulation
- Auto-discovery of AI inventory eliminates shadow AI risk by scanning infrastructure for unregistered models and datasets, which is critical for organizations with decentralized data science teams
- SHAP and LIME integration for fairness testing provides model explainability that goes beyond basic monitoring, directly supporting regulatory requirements for algorithmic transparency
- Flexible deployment options across SaaS, private cloud, and on-premises address strict data residency requirements common in banking and healthcare
- Audit-ready reporting generates compliance documentation that maps directly to regulatory requirements, reducing the manual effort of preparing for external audits
Cons:
- No published pricing makes budget planning difficult -- enterprises must go through sales conversations before understanding cost implications, and there is no self-serve option for smaller teams
- Limited free trial scope at 1 user and 2 AI use cases is too constrained to meaningfully evaluate governance workflows that typically involve cross-functional teams and dozens of models
- Narrow focus on AI governance means organizations also needing broader data quality or data catalog capabilities will need a separate tool like Collibra or DataHub alongside Adeptiv AI
- Enterprise-only positioning excludes mid-market companies and growing startups that need governance tooling but cannot justify enterprise procurement cycles
Alternatives and How It Compares
Collibra is the better choice when you need unified governance across both traditional data assets and AI models. Collibra covers data quality, lineage, and catalog functionality that Adeptiv AI does not, making it stronger for organizations where AI governance is one part of a broader data governance initiative.
Alation fits teams that prioritize data discovery and collaboration alongside governance. Starting at $16,500 per year, Alation provides more pricing transparency than Adeptiv AI, though its AI governance features are less specialized.
DataHub is the right pick for engineering-led teams that want open-source flexibility and community-driven development. DataHub handles metadata management and basic governance without licensing costs, but lacks the regulatory compliance automation that Adeptiv AI provides.
EarlyCore targets a different slice of AI security -- runtime protection for AI agents rather than governance and compliance. Choose EarlyCore when agent-level security is the priority; choose Adeptiv AI when regulatory compliance and audit readiness are the primary concerns.
Adeptiv AI stands out when the core requirement is automated compliance mapping across multiple AI-specific regulations with production monitoring. It falls short if the organization needs general data governance or prefers open-source tooling.