1. A system for intelligent sales engagement, comprising: a pre-integrated graph server comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein the programming instructions, when operating on the processor, cause the processor to: (a) monitor and capture events from source systems and constructs an event graph of multichannel interactions and attributes including firm demographics and sales rep attributes; (b) automatically reduce the graph to a plurality of state transitions; (c) run in modes where reducing the graph happens either periodically or continuously; and (d) support different pre-defined topologies of funnel, decision circle and journey; a machine learning server comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein the programming instructions, when operating on the processor, cause the processor to: (e) train a family of predictive machine-learning models for any transition of interest of the reduced graph and performs validation of the accuracy of each predictive machine learning model; (f) choose different model types for different transitions based on model with highest accuracy, model then estimates the conditional probability of the transition from the starting to ending state potentially including all known input attributes at the starting state; (g) accept case determined numbers of input attributes of different types on each state transitions; an optimization server comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein the programming instructions, when operating on the processor, cause the processor to: (h) create a set of visualizations showing the various resulting performance metrics including conversion rate, representative utilization percentage, and total value in the pipeline; (i) use the trained predictive models as input to an automated optimization phase which recommends specific actions to take to optimize the business outcome of prospects flowing through the reduced graph subject to constraints; (j) support optimization under uncertainty; (k) schedule interactions between agents and prospects to maximize an objective; and (l) configure, in addition to existing model optimization, optimization experiments that are executed and is then able to run experiments, analyze the results and self-learn giving rise to increased utility.