- Creating Fine Digital Experiences for the Employees

Since EmpFinesse™ is having its core platform supporting five kinds of brain which can have as many instances as the enterprise wants implemented on any supported technology. EmpFinesse™ also has a Brain Factory having a bundle of functional brains available ready-made. While we are handling such a scalable situation around handling brains, it is essential to decide on brain platform for a precise purpose, design the brains externally and internally appropriately, design the rules and connections around those brains and finally monitor effectiveness or performance of those brains on a regular basis. Unless all such actions are done through a well governed tool or toolset, it would be very difficult to maintain the accuracy of the aforementioned information. Hence Brain Analyzer is a tool which support this management of Brain(s) seamlessly end-to-end in EmpFinesse.

Enterprise users are habituated to fill in long forms to create or update records. Same users fill in a set of advanced search fields to retrieve a complex query or dataset. But if all these could be done just using a common english like phrases and system would have understand the intents automatically, no nicer solution could be offered. Artificial Intelligence has the power to make this happen in reality.

When early or pre-implementation adoption becomes a need, simulation of post-implementation scenario becomes essential. And to place that simulated situation on top of real ambience to make it realistic, Augmented Reality is the way to go. Virtualizing the forthcoming situation on the real or physical set up of the enterprise is the key for this.

Biometric Computing is getting popular everyday to use human physiology into computing more and more in order to drive seamless operations without taking help from any device. Common biometric computing systems includes facial recognition, voice recognition, iris recognition, skin texture recognition, fingerprint scanning and finger vein identification. In the area of authentication, profiling etc. biometric computing is getting increased importance in enterprise space as well.

Facial recognition is a biometric method of identifying an individual by comparing live capture or digital image data with the stored record for that person. Facial recognition systems are commonly used for security purposes and are increasingly being used in a variety of other applications. Most current facial recognition systems work with numeric codes called faceprints. Such systems identify as many nodal points possible on a human face. Facial recognition systems based on faceprints can quickly and accurately identify target individuals when the conditions are favorable. Reliability of this technique depends on availability of light, angle of face etc. Authentication based on Facial Pattern Matching is becoming popular, mainly for enterprise system, recently. Live photo of entrant gets captured and validated against already stored secured profile photo by the special face API algorithms to issue Authentication Token to user upon validation. Knowing about individual just from a picture of the person is something very interesting to make the onboarding process of an enteprise more simple and easy. This technique also would help to build more relevant Persona in enterprise context for an employee.

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Machine Learning works on several Alogorithms and accuracy of projection / prediction through a Machine Learning engine primarily depends on choosing the right algorithm for a stated problem. And often manual choice of such algorithms raises questions, especially when the output performance is not great. There comes the need of using an automated algorithm mapper which can take inputs on the problem statement in predefined format and analyze internally to decide on the right machine learning algorithm fit for the problem mentioned. This would increase the perfection in the Data Science driven output out of EmpFinesse™.