ML/AI is expected to disrupt every industry profoundly. However there are several bottlenecks in achieving the full potential. Gyrus, with its experience in this space, attempts to make the transition to use ML/AI in businesses smooth, remunerating and rewarding. Gyrus built several building block technologies to make this transition seamless. The usual bumps in the road to implement ML/AI stems from various areas such as data residing in several silos, incomplete data, asynchronous data logging, annotation of data, yielding poor quality datasets are addressed with proven techniques. The ML/AI algorithms are targeted to specific environment with metrics of accuracy, precision and recall pertaining to the use-case. Usually the models are not enterprise ready missing in three major areas. And our flow addresses it by having specific support models checking for Bias in the results, having explain-ability models counter-parts and employing Differential Privacy so as to not leak the data. Finally to deploy, Model maintenance is a challenge, as version control of code will not suffice. The datasets, algorithm implementation, parameters used need to be version controlled to be able to reproduce results.