Every CloudRaker capability and automation runs on the same foundation for files, data, knowledge, AI, integrations, governance, and control. Nothing to deploy. Nothing to assemble.
A new workflow should not begin with a platform project. Most enterprise automation stalls before it ships, because the process everyone agreed on turns into a list nobody planned to build: file infrastructure, a database, a knowledge base, vector search, a RAG pipeline, model infrastructure, permissions, integrations, monitoring, usage tracking, governance, auditability.
Inside CloudRaker, those foundations already exist. They are not modules to select or services to wire together. They are the operating foundation the platform runs on, and everything you build inherits them the moment it exists.
Teams spend their time on the business process they want to automate not on the infrastructure required to operate it.
Capabilities are what CloudRaker can do. Automation is how the work gets done. CloudRaker OS is the infrastructure already built underneath both.
Give every workflow the right business context. CloudRaker manages documents, structured data, business objects, relationships, and enterprise knowledge together, so AI and automation work from what your organization knows, not from whatever fits in a prompt.
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Standardize once. Reuse everywhere. Approved templates and capability configurations live in one place, so the same standard applies across workflows, agents, APIs, and teams. Update it once and every caller inherits the change.
Connect work across your enterprise. CloudRaker automation does not operate in isolation: it executes work and moves it through the applications, external systems, and browser-based processes where the business already operates.
The right intelligence for every task. Model execution is built in and matched to the work being done, instead of forcing every step of every workflow onto the same model, the same latency, and the same cost.
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More automation should not mean less control. Know who can access what, what is running, what happened, what it cost, and where the data went, from infrastructure rather than a process someone has to follow.
Fifteen built-in services. Not one of them is a separate purchase, a separate deployment, or a separate project.
Not every step deserves the same model. Classifying a document type, reading a dense indemnity clause, and reformatting a date are different problems with different costs. Inference is built into CloudRaker and managed according to the task being performed instead of forcing every workflow to depend on one model and one level of intelligence.
Probabilistic AI, deterministic logic, and human control. AI reads the messy input. Rules run the steps that must return the same answer every time. People approve what accountability requires a person to approve. All three execute inside the same run.
Inference optimization is what makes usage-based economics work. Cost tracks the value of the task, not the size of the model someone happened to select.
CloudRaker does not simply store your files. It builds the context AI and automation need to work with enterprise information, and every retrieval respects the permissions of the caller.
As enterprises automate more work, four questions get harder to answer. CloudRaker answers them with infrastructure rather than with a process someone has to remember to follow.
Control which people, agents, and automations can reach each file, dataset, capability, saved action, workflow, and connected system. Policies attach to the work itself, so an agent inherits exactly the access you granted, and nothing beyond it.
See how workflows execute, where exceptions occur, and how the platform is performing. Every run keeps its inputs, its steps, the model output, the rule that fired, and the person who approved it.
Understand consumption across capabilities, workflows, models, and infrastructure. See what a process costs on real volume before you scale it across the business.
Data residency, retention, and anonymization are platform settings, not per-project engineering. Run history, approvals, and source references stay attached to the output for audit, regulator, and client review.
Every CloudRaker capability and workflow gets the enterprise foundation it needs from day one.