Enterprise AI delivered as a fully managed service — vendor operates the AI engineering layer; customer configures workflows.
Managed AI is enterprise AI delivered as a fully managed service. The vendor handles model selection, infrastructure operations, deployment posture, security review documentation, audit governance, and ongoing operations. The customer's role is workflow configuration, integration with the existing stack, and exception handling. Distinct from DIY AI builds (where the customer's engineering team operates everything) and from AI features packaged inside other products (where the customer doesn't choose the model, deployment, or governance posture).
The managed-AI model emerged because most enterprise AI buyers don't have the in-house AI engineering capacity to operate production AI safely, but do have the operations and integration capacity to apply AI to their workflows. The managed model lets the customer's existing operations and IT teams adopt AI without first building a five-to-ten-person AI engineering team.
Most enterprise AI projects stall on the AI engineering capacity gap, the security review, or both. Managed AI compresses time-to-production deployment from quarters or years to weeks, and produces better security posture than the typical in-house build because the vendor's posture is purpose-built for enterprise deployment.
Beth — managed AI agents for high-stakes enterprise operations with deployment posture choice and audit-grade governance
Managed AI for document processing where the vendor operates the AI engineering and the customer's ops team owns workflow configuration
Managed AI for compliance monitoring where the vendor handles model selection and the customer configures the policy framework
Beth is Huper Technology's managed AI agent product for high-stakes enterprise operations. Beth runs the AI engineering layer (model selection, deployment, security posture, audit trails, guardrails); customer ops and IT teams configure workflows and integrations. Beth supports cloud SaaS, dedicated VPC, on-premise, self-hosted, and air-gapped deployment postures, chosen against the customer's security review.
Overlapping but not identical. Managed AI emphasizes the operational ownership boundary — the vendor operates, the customer configures. AI-as-a-Service is broader and can include configurations where the customer has more or less operational ownership.
AI features in existing products (Salesforce Einstein, Microsoft Copilot, etc.) bundle AI inside the product's existing scope. Managed AI is the AI infrastructure itself, used to apply AI to whichever workflows the customer chooses across the stack — including workflows that span multiple existing products.
Tell us what you need. We’ll build, deploy, and manage your AI agents — on our cloud or yours.
Talk to Us