Most failed AI deployments are not model failures. They are context failures. A system that can write well but does not know your policies, customers, or decision criteria will produce confident output that is wrong for the business.
MSTRMND treats context as infrastructure: persistent memory, governed access, and routing rules that ensure the right information is available before work begins.
When context precedes execution, operators spend less time correcting outputs and more time on judgment that actually matters.