1. Core Platform
The base of EmpFinesse™ Suite

2. Business Context Overview Business Scenario Proposed Resolution
EmpFinesse™ Core Platform is the heart of the entire EmpFinesse™ Suite. All or most of the utilities offered by EmpFinesse™ is held on top of the core platform architecture.
Business Scenario
Enterprise wants to have departmental NLP / NLQ / AI engines, which can be from same or diverse technology, surfacing their responses to user requests through single conversational interface
Enterprise AI Load goes beyond the maximum capacity of respective AI engines’ efficiency resulting in pathetic accuracy in response
Power Users want to drive new Q&A, intent-entity injection in AI brains by themselves to reduce dependency on technical resources and eventually reducing the mean time to resolve for any need or issue
Power Users wish to integrate AI responses with backend systems also to respond with personalized live data
A Legacy Conversational Engine wants to be aligned with a global Conversation based systems in enterprise in minimal effort towards rework
Proposed Resolution
Solution should be able to manage and load multiple AI brains in parallel to let them contribute with their intelligence to respond to a user request through a single conversational interface
Multi-brain solution would be having a scoring engine evaluating the accuracy of responses from respective candidate brains based on predefined rules (e.g. confidence score of respective brain against the user request utterance)
Web Interface based Power User console would be available to provision and configure new or existing brains from same or disparate technology background enabling them to be on full control of the AI and conversational assistant ecosystem
Backend integration, wherever applicable would be through an API Gateway consuming the relevant end points to supply data or execute actions as per the request from the conversation flow
Solution would be completely Natural Language aware and flexibility to accept machine intelligence, content classification, analytics, data lake or data validation interfaces from intranet or internet to fulfill the ask of user request

3. Key Features
Hosts a multi-brain ecosystem welcoming brains from any technology platform (MS LUIS, MS QnAMaker and Google DialogFlow are up till now) and functional type (query, action, search, push and dialog) on demand basis with facility of configuring and provisioning from web interface
Includes rule based (optional) scoring module to evaluate the confidence shown by competing candidate brains from multi-brain ecosystem ensuring the appropriate response from appropriate brain
Governs the brain ecosystem through a three (3) level design – AI platform selection for intent patterns in context, distribution of intents across brains on selected platform(s), intra-platform and intra-brain optimal organization of intents, entities, key-phrase etc.
Complete brain transaction auditing for history reference as well as utilization in re-learning mechanism through partial automation
Offers associated brain ecosystem features like data classification on any criteria, caching, feedback collection, cascading conversations, personalization etc.
Surfacing the brain communications over conversational / virtual assistant (BOT) from any technology platform (e.g. MS BOT, Azure BOT) and any interaction mode (text / voice)
Enabling the BOT with capability of communicating with various BOT ecosystem facilities like multi-channel, auto detected multi-lingual, embedded content, two-factor authentication and authorization etc.
Integrating with native backend systems in complex orchestration scenarios through recipe and cartridge ecosystem built on disparate technologies (MS Logic App, Workato, MS Flow etc.) exposed via designated API gateway

4. Technology Perspective
Architecture
Software and Tools
MS Azure Tables
MS Azure Web Apps
MS Azure Web APIs
MS Azure Active Directory
MS Azure Redis Cache
MS BOT / Azure BOT
MS LUIS, MS QnAMaker, Google DialogFlow
MS Logic App / Workato
MS Azure Machine Learning
MS Azure Language Service
XML / JSON
Dot Net 4.5
Output Format
Administration is through Web Interface while normal users can use it over virtual assistants.

5. Value Delivered
Flexibility of choosing any technology for virtual assistant, brain and orchestration engine or even a combination of multiple technologies
Removal of any constraint with respect to scaling of machine intelligence, as one can scale out to infinite using core platform infrastructure of EmpFinesse™
Richness in in-built features at all (BOT, Brain and Orchestration) layers makes implementation enriched with several elegant functions automatically
Openness to interfaces beyond the platform boundary i.e. handshaking with validation engine, rule engine, backend data sources etc. increases acceptability

6. Snapshots

7. Thank You