Frequently Asked Questions

Airia is an enterprise AI platform that unifies orchestration, security, and governance in a single solution. It enables organizations to build, deploy, and govern AI agents and automated workflows at scale — without stitching together multiple disconnected tools. Airia is purpose-built for enterprises that require speed, control, and accountability across their entire AI operation.

Unlike point solutions that address only one dimension of enterprise AI, Airia delivers orchestration, security, and governance in a single unified platform — eliminating the tool sprawl and integration burden that comes with piecing together separate products. This gives enterprises one command center for the full AI lifecycle, from building agents to governing their behavior at scale.

Airia is trusted by enterprises across manufacturing, education, media, telecommunications, and global business operations — with customers including ArcelorMittal, Northwestern University, Mars, BuzzFeed, and 8×8. The platform is designed for organizations that need to deploy AI at enterprise scale while meeting strict security, compliance, and governance requirements.

No. Airia is built to democratize AI across the enterprise, offering no-code, low-code, and pro-code options for building and deploying AI agents and workflows. Both business users and developers can design, test, and launch AI automation without relying on specialized AI expertise or dedicated data science teams.

Airia solves the fragmentation problem in enterprise AI — where organizations are forced to assemble a patchwork of tools for building, securing, and governing AI. Airia replaces that complexity with a single platform that handles all three pillars together, so enterprises can deploy AI faster, reduce operational risk, and maintain accountability as AI adoption scales.

Yes. Airia includes an Integration Framework with out-of-the-box connectors and Model Context Protocol (MCP) support that connects AI agents to SaaS platforms, databases, CRMs, and internal enterprise systems. This allows AI workflows to operate with real-time organizational context — rather than isolated datasets — enabling more accurate, timely, and relevant AI-driven actions.

Yes. Airia is designed as a unified platform that eliminates the need for standalone AI security products or separate governance tools. Security and governance capabilities are embedded directly into the orchestration layer, meaning protection and compliance are enforced at the point of action — not added on as an afterthought.

Airia provides purpose-built value across enterprise functions: IT teams use it to protect infrastructure and eliminate vulnerabilities; operations teams use it to automate workflows and reduce downtime; business leaders benefit from faster time-to-value with built-in compliance; and AI developers and data scientists can experiment, iterate, and deploy in secure, sandboxed environments. The platform is designed to support cross-functional AI adoption without creating new silos.

AI orchestration is the coordination of AI agents, tools, data sources, and workflows to automate complex business processes end-to-end. For enterprises, orchestration is critical because it eliminates manual bottlenecks, connects disparate systems, and enables AI agents to take coordinated action across multiple platforms — turning individual AI capabilities into scalable, measurable operational outcomes.

Airia’s Agent Builder lets teams design AI agents by defining logic, actions, and data flows using drag-and-drop no-code tools, low-code configurations, or full pro-code development. Both business users and developers can build agents that automate tasks, connect to enterprise data, and respond dynamically to changing business conditions — without starting from scratch each time.

Airia’s orchestration layer includes out-of-the-box connectors for widely used enterprise platforms including Salesforce, Microsoft OneDrive, and Slack, as well as a broader MCP-powered integration framework for databases, CRMs, and internal systems. These connectors bring live enterprise data directly to AI agents, ensuring every workflow operates on accurate, up-to-date context.

Yes. Airia’s Prototyping Studio provides a secure, sandboxed environment where teams can test prompts, LLM parameters, and agent workflows before deployment. This allows enterprises to validate performance, debug issues, and confirm readiness — without exposing production systems or users to experimental configurations.

Airia includes dedicated Cost Optimization tools that allow enterprises to set model budgets, enforce usage quotas per team or project, and monitor AI consumption in real time. This prevents runaway costs as AI usage scales across the organization, giving finance and IT leaders predictable, granular visibility into AI expenditure.

Yes. Airia’s orchestration tools are explicitly designed for both technical and non-technical users. The no-code and low-code interfaces allow business teams to design and deploy AI agents and workflows without engineering support — enabling faster time-to-value and broader AI adoption across the enterprise without creating bottlenecks in IT or development queues.

Airia’s security architecture is embedded natively into the platform across six integrated capabilities: Security Posture Management for continuous visibility and risk detection; Agent Constraints to control data and tool access; a Routing Engine for operational reliability; Agent Red Teaming to simulate adversarial attacks; AI Discovery for full agent inventory; and Responsible AI Guardrails for ethical, bias-free outcomes. Together, these protect AI workflows from threats, data exposure, and compliance failures from day one.

Airia supports built-in alignment with GDPR, HIPAA, FERPA, COPPA, PCI DSS, SOC 2 (AICPA), ISO standards, and the EU AI Act through automated policy enforcement, data protection controls, and compliance reporting. Enterprises can generate audit-ready reports, monitor compliance posture in real time, and reduce the manual overhead of regulatory documentation.

AI Security Posture Management (AI-SPM) is the continuous practice of monitoring, inventorying, and hardening the security posture of AI systems and agents across an enterprise. Airia’s Security Posture Management capability provides centralized visibility into all AI agent activity, flags risks and anomalies in real time, prevents AI sprawl, and enables security teams to manage their entire AI attack surface from a single platform.

Airia protects sensitive data through encryption, masking, and role-based access controls that are embedded directly into every AI workflow. Agent Constraints enforce granular rules about what data each agent can read, write, or transmit — and the Routing Engine ensures requests flow through policy-compliant paths. These layered controls eliminate the risk of unauthorized data exposure whether AI interactions occur within internal systems or across external tools.

Agent red teaming is the process of simulating adversarial attacks against deployed AI agents to uncover vulnerabilities before they can be exploited in production. Airia’s Agent Red Teaming capability enables security teams to run realistic attack scenarios, stress-test agent behaviors, and identify weaknesses proactively — shifting enterprise AI security from reactive incident response to continuous, offensive-minded threat hardening.

Airia’s Responsible AI Guardrails capability embeds automated bias detection, fairness checks, and ethical decision-making controls directly into AI workflows. This ensures that AI-generated outputs are reviewed against enterprise ethical standards before acting — giving organizations the confidence to scale AI without creating liability exposure from discriminatory or non-transparent AI decisions.

AI governance is the framework of policies, controls, and processes that ensure AI systems operate transparently, ethically, and in compliance with applicable regulations. Enterprises need AI governance today because the pace of AI deployment has outpaced accountability structures — creating regulatory exposure (EU AI Act, ISO 42001), reputational risk from biased outputs, and operational blind spots when AI agents act autonomously at scale.

Airia’s Governance module includes automated compliance reporting, risk-based classification of AI assets, human-in-the-loop controls, and complete audit trails designed to support alignment with the EU AI Act, ISO 42001, GDPR, HIPAA, and NIST Risk Management Framework. Enterprises can tag agents by risk tier, generate regulator-ready reports on demand, and maintain a full, timestamped record of every AI decision and model deployment.

An AI inventory is a centralized, authoritative catalog of every AI agent, model, dataset, and workflow operating within an enterprise. Airia’s AI Inventory Management capability delivers a unified view of all AI assets across the organization — enabling governance and risk teams to understand what AI is deployed, where it is running, who owns it, and whether each asset falls within acceptable risk parameters.

Yes. Airia’s governance framework includes configurable human-in-the-loop approval workflows that require authorized human review before sensitive or high-impact AI actions are executed. This enforces accountability for consequential decisions, supports regulatory requirements for human oversight, and builds organizational trust in AI operations — particularly in regulated industries.

Airia’s Risk Classification capability allows organizations to tag AI agents, data sources, and workflows with risk levels based on organizational policies or regulatory thresholds such as EU AI Act risk tiers. Combined with the real-time Governance Dashboard and full audit and observability tooling, enterprises get continuous, proactive risk detection — not just point-in-time compliance snapshots taken at audit time.

Yes. Airia’s Compliance Reporting capability allows enterprises to generate audit-ready reports across major regulatory frameworks including GDPR, HIPAA, ISO 42001, and NIST RMF in minutes — not days. Reports are built from the platform’s live audit trail and observability data, ensuring they accurately reflect actual AI system behavior rather than manually assembled documentation.