--- title: "Managed AI" description: "Managed AI keeps any AI system operational after launch, whether we built it or you did: monitored, supported, re-evaluated as models change, and held to the outcome it was built for." canonical: "https://bespinus.github.io/bgus-ai-landingpage/managed-ai/" pillar: "managed-ai" updated: "2026-10-01" --- # Launch is the easy part. Managed AI keeps any AI system operational after launch, whether we built it or you did: monitored, supported, re-evaluated as models change, and held to the outcome it was built for. ## AI drifts while every dashboard stays green. AI systems rarely fail the way servers do. They drift, multiply, and get more expensive. ### Models change underneath you A provider updates a model and the answers shift. We re-run your evaluation set, then promote the new version or roll it back. ### Data and connectors drift Sources change shape, permissions move, and indexes go stale. We watch freshness and fix the sync before users notice. ### Agents multiply New agents and connectors arrive every month. Each one goes through intake, gets an owner, and shows up in the next portfolio review. ### Costs creep Tokenomics watches spend per agent and per outcome, with an alert on the spike rather than a surprise on the invoice. ## Every review ends in a decision. Every review covers each agent's owner, scope, results, and cost per outcome, and ends in a decision: expand it, fix it, or retire it. We run our own agents the same way. Of the 751 our teams designed, 240 were retired because they did not pay for themselves. ## Coverage that grows with the stakes Most systems start with business-hours support. As agents move closer to customers and revenue, coverage extends toward around the clock. The level is set per system and changes as the system takes on more responsibility. ## One owner, inside your processes We work inside your change, approval, and security processes rather than building a parallel structure. One partner covers the platform, the connectors, the agents, the controls, and the cost, so a problem has one owner instead of a chain of vendors. Runs across every layer of the stack, from infrastructure to outcome management, with Tokenomics watching the cost of each one. ## Where this meets the rest Managed AI runs what we build for [Orbit Vision AI](https://bespinus.github.io/bgus-ai-landingpage/vision-ai.md), [AI governance](https://bespinus.github.io/bgus-ai-landingpage/governance.md), and [generative and agentic AI](https://bespinus.github.io/bgus-ai-landingpage/agentic-ai.md), and AI systems your own teams already operate. ## Frequently asked questions ### What does Managed AI cover? Monitoring, incident response, model and prompt releases, re-evaluation as models and data change, connector and permission upkeep, cost control through Tokenomics, and regular agent portfolio reviews. ### Can Bespin run an AI system it did not build? Yes. A system we did not build goes through an intake review first, covering ownership, access, evaluation, and rollback, and then runs on the same terms as the ones we did. ### How is coverage set? Per system. Most start with business-hours support and extend toward around-the-clock coverage as they move closer to customers and revenue. ## Talk to an AI engineer. [Talk to an AI engineer](https://bespinglobal.us/contact) at Bespin Global. _Machine-readable summary for agents: provider = Bespin Global; service = Managed AI; focus = Managed AI keeps any AI system operational after launch, whether we built it or you did: monitored, supported, re-evaluated as models change, and held to the outcome it was built for; delivery model = forward-deployed engineering, build and operate; engagement = scoped against an outcome, no self-serve product; contact = ._