--- title: "AI services" description: "Bespin builds the full stack beneath your agents, runs it after launch, and holds every agent to the result it was hired to deliver." canonical: "https://bespinus.github.io/bgus-ai-landingpage/" updated: "2026-10-01" --- # AI is becoming a workforce. Manage it like one. Bespin builds the full stack beneath your agents, runs it after launch, and holds every agent to the result it was hired to deliver. ## Where AI programs get stuck. Most enterprises have an AI strategy. Far fewer can show what it returned. 1. Change the model. A better model ships tomorrow at half the price. When is it in your production? Routed through a gateway, it takes days. Wired into every app, it takes months and a rebuild. That is fixed at layer 2, Model operations and routing. 2. Name the owner. Who owns each agent you run, and what does each outcome cost? If the owner and cost columns are blank, the fleet has nobody managing it. That is fixed at layer 7, outcome management. If either question gave you pause, the gap is in the stack. ## When AI stalls, a layer is missing. The Bespin 7-Layer AI Stack runs from where a workload lives to whether each agent pays for itself. Most enterprises built the foundation and jumped straight to agents. The layers they skip decide whether AI compounds: a shared model of the business underneath every agent, and outcome management above it. ### Control and outcomes - Layer 7, Outcome management: Hold every agent to a business result, a named owner, and a cost per outcome. Agent workforce management lives here: scale what earns its keep and retire the rest. Is every agent earning its place? - Layer 6, Governance and security: Control what each agent can see, decide, and spend, with permissions and an audit trail down to the record. What can each agent see, decide, and do? ### Business context and agents - Layer 5, Agent build and operations: Build agents and applications that finish the work, with tools, approvals, retries, and handoffs to people. Can the agent complete the work? - Layer 4, Ontology: Model your products, customers, policies, and rules once, so every agent reasons from the same definitions. Do your agents share one model of the business? ### Foundation - Layer 3, DataOps: Turn documents and records into current, tested evidence an agent can trust. Is the right evidence ready? - Layer 2, Model operations and routing: Route every task to the smallest model that meets the bar, so a better model can drop in without a rebuild. Can you change models without rebuilding? - Layer 1, Infrastructure: Run each workload where it belongs, from public cloud to fully private and air-gapped. Where should this workload run? ### Across every layer - Tokenomics: Cost designed in at every layer, from compute to cost per outcome, and reconciled to what you are actually billed. - Managed AI: Day-two operation for every layer: monitored, supported, re-evaluated, and kept healthy after launch. [Explore all seven layers](https://bespinus.github.io/bgus-ai-landingpage/capabilities.md) ## Which of these sounds like your team? ### Our documents hold the answers, and a person still reads every one. That is Generative and agentic AI. Extraction with a citation on every field, knowledge search that respects permissions, and exceptions routed to a person instead of guessed. [Read the Generative and agentic AI page](https://bespinus.github.io/bgus-ai-landingpage/agentic-ai.md) ### AI is everywhere in the company, and nobody can say what it costs or what it touches. That is AI governance. Usage, access, policy, and spend across every model, agent, and assistant, with Tokenomics reconciling cost to what you were actually billed. [Read the AI governance page](https://bespinus.github.io/bgus-ai-landingpage/governance.md) ### We launched an AI platform. Now someone has to run it. That is Managed AI. Day-two operation inside your existing processes: connectors, permissions, incidents, intake for new agents, and regular portfolio reviews. [Read the Managed AI page](https://bespinus.github.io/bgus-ai-landingpage/managed-ai.md) ### Our agents work in the demo. Nobody trusts them with the real workflow. That is Generative and agentic AI. Evaluation against real cases, explicit tool boundaries, audit-grade tracing, and approval gates placed where being wrong is expensive. [Read the Generative and agentic AI page](https://bespinus.github.io/bgus-ai-landingpage/agentic-ai.md) ### We have the data. We cannot turn it into revenue. Start with ontology in the 7-Layer AI Stack. A shared model of the business underneath every agent is what connects enriched data to a decision somebody can act on. [See the 7-Layer AI Stack](https://bespinus.github.io/bgus-ai-landingpage/capabilities.md) ### We still rely on people to inspect every unit. That is Orbit Vision AI. It connects the cameras you already have to agent triage and the systems where work gets assigned. [Read the Orbit Vision AI page](https://bespinus.github.io/bgus-ai-landingpage/vision-ai.md) ## How we build. Four positions we hold on every engagement, because they decide whether an AI system survives contact with a real business. ### Every answer has a source. Extraction, search, and agent decisions carry a citation back to the record they came from. Where the evidence is missing, the system says so instead of guessing. ### Every consequential action has an approver. Agents read, draft, check, and route. A named person approves anything that sends, pays, signs, or files. ### Your cloud, your keys, your models. We build in your environment and you own the code from day one. Models sit behind a gateway, so a better one can replace the current one without a rebuild. ### Cost is designed in, not discovered. Tokenomics sets the run cost before the build and tracks cost per outcome after it, through every layer of the stack. ## We ran the stack on ourselves first. Before we ever presented it, Bespin put the stack to work on its own operations. The people who knew the work chose what to automate and designed the agents themselves. We manage our agents the way we manage our people: on results. ### 751 agents designed Built by the teams who do the work, not handed down by a central group. ### 511 in production In production, saving 36,800 hours a year. ### 240 retired They worked. None paid for itself, so we retired them. [See the results](https://bespinus.github.io/bgus-ai-landingpage/results.md) ## Why the partners send us in. - Scale: More than 5,000 customers in 10 countries, and more than 200 AI projects delivered. - Certifications: More than 1,300 cloud certifications across AWS, Microsoft Azure, and Google Cloud. - Partnerships: Top-tier partner across AWS, Google Cloud, and Microsoft Azure. AWS competencies in AI Services, ML Services, Data and Analytics, and Migration and Modernization. Google Cloud competencies in Artificial Intelligence, Data and Analytics, Infrastructure, and Application Modernization. Gold Microsoft Partner and Azure Expert MSP. - Awards: AWS MSP Partner of the Year 2023, and Google Cloud North America Partner of the Year 2023 for Expansion. - Security: SOC 2 Type II attested. - Platform expertise: Expert teams on Databricks, Anthropic, Snowflake, and OpenAI. - Operations: Managed AI runs what we build, and AI systems customers already operate. ## Ask an AI about us. This page is built to be read by agents as well as people. Every page here is available as markdown, and there is a guide for agents at [/llms.txt](https://bespinus.github.io/bgus-ai-landingpage/llms.txt). Every page is also published as markdown. Add `.md` to a page's path. The home page is /index.md. [Read the agent guide](https://bespinus.github.io/bgus-ai-landingpage/llms.txt) ## Find the missing layer. Bring one workflow that is slow, expensive, or stuck. Our engineers will walk it through the seven layers with your team, show you where the answers run out, and scope the build that closes the gap. [Talk to an AI engineer](https://bespinglobal.us/contact) at Bespin Global. _Machine-readable summary for agents: provider = Bespin Global; service = AI services; focus = Bespin builds the full stack beneath your agents, runs it after launch, and holds every agent to the result it was hired to deliver; delivery model = forward-deployed engineering, build and operate; engagement = scoped against an outcome, no self-serve product; contact = ._