Start from live operations.
We map the queues, inboxes, handoffs, exception paths, and data gaps that actually decide the outcome.
Built close to the work.
CloudRaker builds RakerOne for enterprise operations where AI has to follow rules, keep context, and leave a trace. The product comes from production systems, not demo scripts.
Better models alone are not going to get AI into the enterprise. The work is the layers around them: turning unstructured interaction into controlled systems, harnessing AI at operational scale, and giving teams the same repeatability and quality bar that engineering already expects.
How we build
Small team. Production systems. Fast feedback.
Turn judgment into software rails.
Reviews, thresholds, citations, and handoffs become explicit controls the system can run and explain.
Ship inside the system of record.
RakerOne improves by running real playbooks with operators, not by producing one-off recommendations.
Bring us the hard operation
Show us the process that has to be right every time.
We will show you what it looks like when RakerOne turns it into controlled, reviewable work.