# CloudMast > CloudMast is an enterprise AI enablement firm. We help enterprises choose the right AI work, build it into real workflows, and leave teams with systems they can run. This file concatenates the public page markdown for agents that want one fetch. --- # CloudMast CloudMast is an enterprise AI enablement firm. We help enterprises choose the right work, build AI into real workflows, and leave teams with systems they can run and improve. Canonical URL: https://cloudmast.dev/ ## How we work Senior engineers work with the people who know the job, build in the customer's environment, and transfer ownership as the system proves itself. We call this a forward-deployed engineering model. 1. Focus. Choose one workflow and define the result. 2. Build. Connect AI to the work, data, and systems. 3. Prove. Measure quality, adoption, cost, and impact. 4. Transfer. Leave ownership and a repeatable pattern. ## Services - [AI Strategy](https://cloudmast.dev/ai-strategy.html): Choose the work. Prioritize AI investments, test business value, and plan delivery and adoption. - [Agentic Engineering](https://cloudmast.dev/agentic-engineering.html): Build the workflow. Connect AI agents to business data and tools, with tested workflows and clear handoffs. - [Cloud Architecture](https://cloudmast.dev/cloud-architecture.html): Build the cloud foundation. Design AWS and Google Cloud infrastructure. Use AI to help build and operate it. Use [AI gateways](https://cloudmast.dev/#ai-gateways) to track AI spend and apply access policies in cloud deployment workflows. - [DevOps and Automation](https://cloudmast.dev/devops-automation.html): Ship and operate reliably. Build CI/CD and operational automation. Use AI to help engineers improve delivery. Use [AI gateways](https://cloudmast.dev/#ai-gateways) to control AI access and track spend in delivery pipelines. ## AI platform adoption at scale We've helped engineering organizations with thousands of developers adopt AI platforms and ship faster. We help teams use Claude Code, Codex, and Cursor for product delivery and run-the-business work. That includes DevOps pipelines and cloud infrastructure deployments. We help teams choose where each tool fits, then add the context, controls, evaluation, and ownership needed to use it well. The tool showcase covers Claude Code, Codex, Cursor, and GitHub Copilot. ## AI gateways and harnesses Opinionated harnesses that make AI safe to scale, not just available. We set up shared workflows, context, and controls around your AI tools. A gateway sits in front of the providers, so a workflow keeps one interface when the model behind it changes. - Spend visibility. Route model and tool usage through a gateway so you can see cost by team, tool, and workflow. - Governance. Apply policy, access controls, and guardrails in one place instead of per tool. - Usage insight. Understand how people actually use AI day to day, so you know what's working and what to scale. ## Next step Start with one workflow worth improving. Email [patrick@cloudmast.dev](mailto:patrick@cloudmast.dev) or [contact CloudMast](https://cloudmast.dev/contact.html). --- # CloudMast AI strategy Choose the work. Prove the value. We help you decide where AI can improve your business, what to invest in, and how to measure results. Build a plan grounded in your team's work, data, and constraints. Canonical URL: https://cloudmast.dev/ai-strategy.html ## The problem AI initiatives need a clear business case. A growing list of ideas makes it hard to decide where to start. Each investment needs a useful outcome, workable data, and a team ready to adopt it. - Unclear value. The idea sounds promising, but nobody has defined the time saved, cost reduced, or service improved. - Untested assumptions. A demo works with sample data. Access, accuracy, and integration requirements still need testing in the actual workflow. - No plan for adoption. Tools arrive before teams agree how work will change, who will support them, or how success will be measured. ## What we deliver An AI plan your team can act on. We work with business leaders and the people doing the job to choose a starting point and define what it will take to deliver. - Opportunity assessment. Map the work, identify repetitive tasks and decision points, and rank opportunities by business value, feasibility, and risk. - Data and tool readiness. Check data quality, access, and integration needs. Decide where an existing AI tool fits and where a custom workflow is needed. - A measured pilot. Define a baseline and test a priority use case with real users and data. Measure quality, time saved, usage, and operating cost. - Delivery and adoption roadmap. Set the delivery sequence, budget, owners, and decision checkpoints. Plan training and support so teams can use what gets built. Opportunities are ranked by value, feasibility, data readiness, and risk. A measured pilot informs the delivery plan. ## Where to start Start with work you can measure. Choose a workflow where you can compare the current process with an AI-assisted version. The result should guide the next investment. - Find internal knowledge. Help employees find answers in approved company documents. Measure answer accuracy and time spent searching. - Handle repetitive work. Test AI on document review, request triage, or report preparation. Track time saved and the corrections people still need to make. - Support software teams. Trial AI coding tools on defined tasks. Measure review effort, defects, and delivery time alongside developer usage. Your team gets a ranked set of opportunities, pilot evidence, and a delivery plan with named owners and clear measures of success. ## Next step Choose your first AI investment. [Discuss your priorities](https://cloudmast.dev/contact.html) --- # CloudMast agentic engineering Build agents that get work done. We build AI agents that use your business data and tools to complete defined tasks. Each workflow has clear permissions, quality checks, and a handoff when someone needs to decide. Canonical URL: https://cloudmast.dev/agentic-engineering.html ## The problem An agent needs a clear job and reliable tools. Completing a business task takes several steps. The agent needs the right information, permission to act, and a way to check whether the work is complete. - Unclear task. The agent receives a broad goal without defined inputs, a completion check, or limits on what it can change. - Disconnected systems. Useful information sits in documents and business applications the agent cannot reach or update. - Unverified results. Plausible answers hide missed steps or incorrect actions. Teams need checks against the actual job. ## What we build Agents that fit the way your team works. We start with one job, map the steps, and build the agent into your existing systems. Your team helps define when it can act and when it should ask for help. - Workflow design. Define inputs, actions, and completion criteria. Set decision points, failure handling, and handoffs to the people responsible for the work. - Data and tool connections. Connect approved documents, APIs, and business systems. Give the agent the context and permissions each task requires. - Testing and controls. Test representative cases, check results against source data, and record actions. Set limits on tool use and require review for consequential changes. - Production handoff. Deploy in your environment with monitoring and usage budgets. Leave documented workflows, runbooks, and a team that knows how to support and improve the agent. Workflow design and connected data and tools guide the agent. Checks and human handoffs control its actions, with monitoring and ownership in place. ## Agents in practice Turn a defined task into a working workflow. Use an agent where the job requires interpreting information and choosing the next step. Keep clear checks around the actions it takes. - Handle service requests. Read a request, retrieve account details, and prepare a response. Route exceptions to a person and update the service system after approval. - Process business documents. Classify documents, extract required fields, and check them against source records. Send missing or conflicting information for review. - Coordinate operational work. Gather updates across systems, identify missing steps, and prepare follow-up actions for the team to review. Measure task completion, errors, human intervention, and cost per job. Use those results to decide where the agent can take on more responsibility. ## Next step Start with one job an agent should do well. [Discuss an agent workflow](https://cloudmast.dev/contact.html) --- # CloudMast cloud architecture Build your cloud. Put AI to work. We design and modernize AWS and Google Cloud infrastructure for your applications, data, and AI. Then we help your team use AI to build and operate it. Canonical URL: https://cloudmast.dev/cloud-architecture.html ## The problem Cloud complexity grows faster than the team. Ad hoc infrastructure gets harder to change as workloads grow. Adding AI brings new demands on data access, compute, and budgets. - Manual configuration. Console changes leave environments inconsistent and make infrastructure hard to reproduce. - Fragile workloads. Applications outgrow their original design. Scaling, backups, and recovery need a deliberate plan. - Rising cloud bills. Idle resources, oversized compute, and AI usage add up without clear budgets or owners. ## What we build Infrastructure that fits your workloads. Whether you are moving to AWS or Google Cloud, or improving an existing environment, we start with your applications, data, and operating requirements. - Cloud foundations. Structure accounts or projects, network boundaries, identity, and access. Define environments in infrastructure as code so your team can review and reproduce them. - Application and data architecture. Choose managed services, containers, serverless, or virtual machines to match the workload. Plan storage, databases, and data flows around performance and growth. - AI workloads in production. Connect models to the data and services they need. Design for access controls, response times, usage limits, and compute capacity as adoption grows. - Reliability and cost. Build in monitoring, backups, and tested recovery. Track spend by workload, set budgets, and give your team clear responsibility for day-to-day operations. Applications, data pipelines, and AI workloads run on an AWS or Google Cloud foundation with compute, networking, identity, storage, and monitoring. ## AI for cloud operations Use AI to improve how your cloud runs. Give AI the context of your infrastructure, telemetry, and operating procedures so it can help engineers make informed changes. - Plan infrastructure changes. Use AI to draft infrastructure code, explain proposed changes, and check configurations against your team's standards. - Investigate incidents. Bring logs, metrics, and recent deployments together so AI can help narrow likely causes and suggest the next checks. - Find avoidable spend. Use AI to analyze usage and billing patterns, flag idle resources, and suggest capacity changes for engineers to validate. Engineers review proposed changes. Approved automation runs within defined permissions, with an audit trail and a rollback path. ## Next step Make your cloud ready for what comes next. [Discuss your cloud architecture](https://cloudmast.dev/contact.html) --- # CloudMast DevOps and automation Ship reliably. Automate delivery. We build CI/CD pipelines, deployment automation, and production monitoring for your applications and AI services. Use AI to help engineers maintain tests, understand failed builds, and reduce repetitive work. Canonical URL: https://cloudmast.dev/devops-automation.html ## The problem Manual delivery slows every release. Builds, deployments, and routine checks become bottlenecks when they depend on individual engineers. AI services add model and output changes that need their own tests. - Fragile pipelines. Build steps vary between environments. Flaky tests and manual fixes make it hard to know whether a change is ready. - Risky releases. Teams deploy large changes with limited visibility into their impact or an untested recovery procedure. - Repetitive operations. Engineers spend time provisioning environments, checking deployments, and repeating the same support steps. ## What we build A delivery system your team can rely on. We work with your repositories, cloud environment, and release process to make delivery repeatable and give engineers a clear view of each change. - CI/CD pipelines. Automate builds, tests, and security checks. Produce versioned release artifacts and make failed checks easy to trace. - Controlled deployments. Test in staging, release to a limited group where appropriate, and monitor the result. Define approval steps and test rollback procedures before they are needed. - Operational automation. Automate environment setup, scheduled checks, and repeatable runbook steps. Set permissions and record what each automated action changes. - Checks for AI services. Test output quality, response time, and cost alongside software behavior. Version prompts and model configurations so your team can compare releases and restore a known setup. Code or model change → automated checks → staged rollout → release. A rollout that fails its checks follows a rollback path. ## AI in the delivery workflow Give engineers help with the repetitive steps. Connect AI to your code, build output, and team standards so it can help prepare changes and diagnose delivery problems. - Maintain useful tests. Use AI to draft test cases for changed code and identify missing coverage. Engineers review the cases and run them in the pipeline. - Explain failed builds. Summarize build output, relate failures to recent changes, and suggest checks that help engineers resolve the problem. - Turn runbooks into automation. Use AI to draft scripts and workflow steps for recurring tasks. Test them, then run approved automation with defined permissions. Track delivery time, failed releases, recovery time, and manual effort. Use those measures to choose the next part of the workflow to improve. ## Next step Make the next release easier to deliver. [Discuss your delivery workflow](https://cloudmast.dev/contact.html) --- # Contact CloudMast Start with one workflow. Tell CloudMast what should improve, who does the work, and what is blocking progress. Canonical URL: https://cloudmast.dev/contact.html Best fit: enterprises moving an AI idea into real work. Response is usually within one business day. ## Public contact API `POST https://cloudmast.dev/api/contact` Send `multipart/form-data` or form fields: - `name` required - `email` required work email - `company` optional - `message` required, at least 10 characters - `cf-turnstile-response` required when Turnstile is enabled on the site form No API key is required for this endpoint. The browser form uses Cloudflare Turnstile. Scripted clients should expect 400 when validation fails. Staff can read submissions with `GET /api/admin/submissions` and a Bearer token. That key is internal. CloudMast does not issue public product API keys. [Open the contact form](https://cloudmast.dev/contact.html) --- # CloudMast developer portal Machine-readable docs, the public contact API, a sandbox, and the CloudMast CLI. Canonical URL: https://cloudmast.dev/developers CloudMast is a consulting firm, not a hosted model platform. This portal describes the public HTTP surface of cloudmast.dev. ## Quickstart 1. Read [llms.txt](https://cloudmast.dev/llms.txt) for the site map and when to use CloudMast. 2. Fetch any page with `Accept: text/markdown` to get the same URL as Markdown. 3. Use `npx cloudmast docs` or `npx cloudmast fetch /` from the CLI. ```bash curl -sI -H "Accept: text/markdown" https://cloudmast.dev/ npx cloudmast fetch / ``` ## API keys The public contact API does not use API keys. The site form is protected by Cloudflare Turnstile. `GET /api/admin/submissions` uses a staff Bearer token stored as `ADMIN_API_KEY`. That key is not available through a signup flow. ## Documentation - [OpenAPI](https://cloudmast.dev/openapi.json) - [RFC 9727 API catalog](https://cloudmast.dev/.well-known/api-catalog) - [Sitemap](https://cloudmast.dev/sitemap.xml) - [Full markdown corpus](https://cloudmast.dev/llms-full.txt) ## Sandbox The HTML portal at `/developers` can request this site with `Accept: text/markdown` and show the response. That is the sandbox. There is no hosted model playground. ## CLI ```bash npx cloudmast --help npx cloudmast pages npx cloudmast docs npx cloudmast fetch /developers ``` Publish target: the `cloudmast` package on npm.