Beth is managed infrastructure — your team configures the workflows, Huper handles model selection, deployment, governance, and ongoing operations.
Building an internal AI engineering team takes 12–24 months and $2–10M in fully-loaded comp before the first production workload is live. Beth (in build) is the managed-infrastructure alternative: customer teams configure the workflows in plain language; Huper handles model selection, deployment posture, governance documentation, ongoing operations, and the security review documentation the procurement team needs.
An internal AI engineering team capable of deploying production-grade enterprise AI typically requires 5–10 specialists (AI engineers, MLOps, security, data, governance) at $300K–$500K fully loaded each. That's $2–5M in annual run-rate before any production deployment, plus 12–24 months of building. Beth's managed model decouples production-grade enterprise AI deployment from the build-the-team requirement.
Beth handles the AI engineering layer (model selection, infrastructure, MLOps, security posture, audit trails) as managed infrastructure. The customer's operations and IT teams configure workflows, integrations, and exception-handling logic — typically without an internal AI engineering function at all. The customer retains ownership of business logic, integrations, and decisions; Huper handles the AI plumbing.
Operations and IT leads identify the workflows for managed-agent deployment using Beth's configuration interface — natural-language rules, visual integration flows, defined escalation logic.
Huper selects appropriate models per workflow, configures deployment posture per security review, deploys agents inside the customer's perimeter.
Once deployed, customer ops and IT teams adjust workflow rules, expand to new workflows, and handle escalations — without needing internal AI expertise.
Customer-side roles managing Beth are typically operations leads, IT integration engineers, and (for higher-stakes deployments) the security and governance team. They don't need AI engineering expertise; they need to know their workflows, their integration architecture, and their security policy. Huper handles the AI engineering side as managed service.
Huper handles model selection per workflow during deployment and re-evaluates as model capabilities change. Customers can specify preferences (model lineage, model provider, model size) but don't need to choose specific models.
Yes — many enterprise customers use Beth as the production deployment layer while building internal AI engineering capability for custom or bespoke workloads. The two coexist; Beth handles the standard enterprise workflows, internal teams handle the bespoke ones.
Tell us what you need. We\u2019ll build, deploy, and manage the AI agents to fix it.
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