1. A system for intelligent sales engagement, comprising: a pre-integrated graph module comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device; a machine learning module comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device; and an optimization module comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device; wherein, the pre-integrated graph module: (a) monitors and captures events from source systems and constructs an event graph of multichannel interactions and attributes including firm demographics and sales rep attributes; (b) automatically reduces the graph to the significant state transitions, effectively reverse engineering the sales process from available actual event data; (c) runs in modes where reducing the graph happens either periodically or continuously; and (d) supports different pre-defined topologies of funnel, decision circle and journey; wherein the machine learning module: (e) trains a family of predictive machine-learning models (ex. Distributed Random Forest, Deep Learning) for any transition of interest (or for all transitions) of the reduced graph and performs validation of the accuracy (AUC) of each predictive machine learning model; (f) chooses 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) accepts case determined numbers of input attributes of different types on each state transitions; (h) runs with either full state history with attributes, Markov approximation or hidden Markov model or a hybrid mode; and (i) supports a hidden Markov model to represent the hidden “intent” state of the contact or lead; wherein the optimization module: (j) creates a set of visualizations showing the various resulting performance metrics including conversion rate, representative utilization percentage, and total value in the pipeline; (k) uses the trained predictive models as input to an automated optimization phase which recommends specific actions (interactions) to take to optimize the business outcome of prospects flowing through the reduced graph subject to constraints; (l) supports optimization under uncertainty; (m) schedules interactions between agents and prospects to maximize an objective; and (n) configures, 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.