ASELECTOR
Artificial Intelligence
Artificial Intelligence
Futuristic customer care
Auto decisioning
Transformation

A KNOWLEDGE MANAGEMENT PLATFORM DESIGNED FOR DIGITAL TRANSFORMATION

Smarter Decisions

Aselector Knowledge Management platform is a hierarchical artificial intelligence platform for customer self help, decision support and knowledge management.

Knowledge management helps staff follow the standard operating procedure, ensures consistency for customers, reduces the burden on expert attrition, makes visual thinking tangible, and manages effectively large volumes of information to help employees serve their clients better and faster.

Help your voice and back office agents reduce transaction time and provide the right resolution - every time. Knowledge management leads to competitive advantage and adds a real client value.

Workflow and Command Center


Queue based transactions with automated load balancing.


Reduce Manual ticket creation :Transactions get auto created and allocated to work queues based on outcome of previous transactions.
Real time command center : All queues can be monitored centrally through a real time command center that can refresh every second or at a pre-defined frequency.
Real time automated load balancing :The platform can intelligently change user skills to align them to the most optimal transactions real time based on ageing and targets across all queues.

AI concepts implemented

Digital Knowledge Platform

Decision Management

Our Logic Engine inserts AI rules and logic into the decision making process. Fine tuned for enterprise applications - improves decision making by assisting in or performing automated decision-making. The automated decisions are used to support the process, not as the driving or dominating force

Neural networks

Artificial neural networks (ANNs) / Deep Neural networks (Deep learning -also known as deep structured learning, hierarchical learning or deep machine learning) use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.

Theory of evolution

Continuous evolution is built into the application. The application enables continuous refinement by accepting and incorporating changes in the business environment. It is capable of evolving (with physical review and remapping) based on actual searches, feedback and queries.

AUTOBOT


Analyze and pre-plan automatic choice selection for specific scenario's. AUTOBOT automatically takes the decisions at the right time.


Predictive decision making: Parse and interpret the information from supplied case number - e.g. card type from card number
Explicit decision making: Move step based on available information from API / data stored in ASelector.
Time based decisioning: Change decisions automatically based on time of the day, day of week, month etc.
Device / browser based decisioning: Example - different action for iPhone Vs Android
variable based decisioning: Variables supplied explicitly in the query string can dictate decisions.
Cookie and user timezone based decision making: Example - if the user has visited before, IPv6 lookup for geography.

User centric Decision Tree

All relevant information is available to the user in real time on fingertips. No searching for required information or asking for help.

Decision making is a cognitive process resulting in the selection of a course of action among several alternative scenarios. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences.
The unlimited hierarchical structure is utilized to map theoretically infinite situations and provide the best solution for each and every unique situation.

Hierarchical process map takes the agent to the best and fastest resolution in real time. Get unprecendented level of analytics and information on detailed work splits and effort on each node to help refine your customer service strategy.

Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely succeed. A decision tree is normally supported by a probability model to arrive at a decision most favourable in probability terms.

Contact us


Aselector Technologies

  324 Vardhaman Dee Cee Plaza, Sector 11, Dwarka.New Delhi

  Phone: +91 9811822188

  Email: ashu@datapotential.com

Data Potential Online surveys is a part of Aselector Technologies




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