# Bespin AI, from Bespin Global > Bespin Global builds and runs the full AI stack underneath enterprise agents, organized as the Bespin 7-Layer AI Stack: infrastructure, model operations and routing, DataOps, ontology, agent build and operations, governance and security, and outcome management. Every agent is held to a named owner and a cost per outcome. Tokenomics and Managed AI run across every layer. Solutions are Orbit Vision AI, AI governance, Generative and agentic AI, and Managed AI. Forward-deployed engineers work inside customer environments. Bespin serves more than 5,000 customers in 10 countries, has delivered more than 200 AI projects, and is a top-tier partner across AWS, Google Cloud, and Microsoft Azure. Every page below links to its markdown version. The same pages are on the web without the `.md` suffix, for example https://bespinus.github.io/bgus-ai-landingpage/vision-ai/. ## Solutions - [Orbit Vision AI](https://bespinus.github.io/bgus-ai-landingpage/vision-ai.md): computer vision at the edge with agent triage that turns detections into assigned work. - [AI governance](https://bespinus.github.io/bgus-ai-landingpage/governance.md): usage discovery, agent and connector intake, policy and approval controls, and Tokenomics for cost reconciled to actual billing. - [Generative and agentic AI](https://bespinus.github.io/bgus-ai-landingpage/agentic-ai.md): document automation, cited knowledge search, workflow agents, and bespoke agentic applications, with evaluation, approvals, and traces. - [Managed AI](https://bespinus.github.io/bgus-ai-landingpage/managed-ai.md): day-two operation for any AI system, including monitoring, incident response, re-evaluation, cost control, and agent portfolio reviews. ## Method and proof - [Bespin AI services](https://bespinus.github.io/bgus-ai-landingpage/index.md): the home page, with the stack, the four solutions, and Bespin's own agent program. - [Bespin 7-Layer AI Stack](https://bespinus.github.io/bgus-ai-landingpage/capabilities.md): the seven layers and the operating model, with Tokenomics and Managed AI across every layer, and the capability register. - [Results](https://bespinus.github.io/bgus-ai-landingpage/results.md): Bespin's own agent program, customer results by sector, and use-case patterns. ## When to bring in Bespin - [Generative and agentic AI](https://bespinus.github.io/bgus-ai-landingpage/agentic-ai.md): when a team says "Our documents hold the answers, and a person still reads every one." - [AI governance](https://bespinus.github.io/bgus-ai-landingpage/governance.md): when a team says "AI is everywhere in the company, and nobody can say what it costs or what it touches." - [Managed AI](https://bespinus.github.io/bgus-ai-landingpage/managed-ai.md): when a team says "We launched an AI platform. Now someone has to run it." - [Generative and agentic AI](https://bespinus.github.io/bgus-ai-landingpage/agentic-ai.md): when a team says "Our agents work in the demo. Nobody trusts them with the real workflow." - [Start with ontology in the 7-Layer AI Stack](https://bespinus.github.io/bgus-ai-landingpage/capabilities.md): when a team says "We have the data. We cannot turn it into revenue." - [Orbit Vision AI](https://bespinus.github.io/bgus-ai-landingpage/vision-ai.md): when a team says "We still rely on people to inspect every unit." ## Use-case patterns - [Replacing a SaaS stack with an agentic deal desk](https://bespinus.github.io/bgus-ai-landingpage/use-cases/agentic-deal-desk.md): Generative and agentic AI. One agentic backbone holds deal state, and agents do the work that used to be a seat in a SaaS tool: assembling the quote, checking it against policy, chasing the approval, writing the record back. - [AI sprawl across a dozen assistants, and no answer for the CISO](https://bespinus.github.io/bgus-ai-landingpage/use-cases/ai-sprawl-ciso.md): AI governance. Discovery first, because the real list is always longer than the one people hand you. - [Years of expertise, searchable with the source attached](https://bespinus.github.io/bgus-ai-landingpage/use-cases/cited-knowledge-base.md): Generative and agentic AI. Sources are inventoried and indexed with their permissions intact, and a shared model of the business defines the things people actually ask about. - [Documents into the system of record, without anyone keying the values](https://bespinus.github.io/bgus-ai-landingpage/use-cases/document-automation-system-of-record.md): Generative and agentic AI. Retrieval agents collect each document from its source, through an API where one exists and a browser only where it does not. - [From enriched data to a qualified pipeline, with a person on every handoff](https://bespinus.github.io/bgus-ai-landingpage/use-cases/enriched-data-to-pipeline.md): Generative and agentic AI. A shared model of accounts, people, offerings, and fit sits underneath every agent. - [Forklift and pedestrian proximity safety on an active floor](https://bespinus.github.io/bgus-ai-landingpage/use-cases/forklift-pedestrian-safety.md): Orbit Vision AI. The cameras already covering the aisles get a proximity model watching for a vehicle and a person in the same space. - [Keeping an enterprise AI platform healthy after launch](https://bespinus.github.io/bgus-ai-landingpage/use-cases/managed-enterprise-ai-platform.md): Managed AI. Managed AI takes over day-two operation inside the existing change and approval process: connector and permission upkeep, index freshness, incident response, and a risk-tiered intake for every new agent and connector. - [A multi-agent assistant over nationwide public records](https://bespinus.github.io/bgus-ai-landingpage/use-cases/multi-agent-public-records.md): Generative and agentic AI. An acquisition pipeline normalizes records from hundreds of sources into one queryable layer. - [Continuous defect detection across a multi-site plant network](https://bespinus.github.io/bgus-ai-landingpage/use-cases/multi-site-defect-inspection.md): Orbit Vision AI. One platform across every use case and every site, rather than a product per defect. - [Giving teams the standards their coding agents will actually follow](https://bespinus.github.io/bgus-ai-landingpage/use-cases/standards-coding-agents-follow.md): Generative and agentic AI. Stop policing the tooling and arm it instead. - [When unmanaged AI spend becomes an audit finding](https://bespinus.github.io/bgus-ai-landingpage/use-cases/unmanaged-ai-spend-audit-finding.md): AI governance. Pull usage and spend from every model gateway, cloud, and endpoint in parallel, then reconcile it against the invoice rather than published rates. ## How to engage Every engagement is scoped, staffed, and priced against an outcome the customer can verify. There is no self-serve product or public rate card. Work starts with a working session with a Bespin engineer, builds one business process through the missing layers, then runs under Managed AI. [Talk to an AI engineer](https://bespinglobal.us/contact). ## Company - Partnerships: Top-tier partner across AWS, Google Cloud, and Microsoft Azure. - Scale: More than 5,000 customers in 10 countries, more than 200 AI projects delivered, and more than 1,300 cloud certifications. - Operations: Managed AI runs what Bespin builds, and AI systems customers already operate. - Corporate site: [bespinglobal.us](https://bespinglobal.us/) ## Optional - [Every page in one file](https://bespinus.github.io/bgus-ai-landingpage/llms-full.txt): the complete markdown corpus. - [Sitemap](https://bespinus.github.io/bgus-ai-landingpage/sitemap.xml): every HTML page and its markdown version.