KPS partner Cognigy
Conversational automation solutions through the use of cognitive bots
COGNIGY.AI is the leading enterprise conversational automation platform for building advanced, integrated conversational automation solutions through the use of cognitive bots. As a highly-flexible converstional automation platform COGNIGY.AI is implemented to build advances, integrated enterprise virtual agents for customer and employee service on chat and voice channels.
Cognigy is supported by Nordic Makers, a group of knowledgeable investors such as Lars Fløe Nielsen, former product strategist and co-founder of Sitecore.
Why are Natural Language Interfaces better?
- Messaging is a primary app: Users download fewer and fewer apps and primarily spend their time in no more than five apps. Messaging apps are at the top of the list. (Forrester)
- Consumer message business: Facebook reports that 20 billion messages are exchanged between customers and businesses every month on the Messenger platform.
- Messaging is a differentiator: 56% of surveyed consumers indicate they are more likely to do business with companies they can message directly.
Low Code Conversational Flow Editor
Real-time preview
Enterprise-Level Operations
- SAAS & ON-PREMISE - Cognigy.AI runs on Cognigy's SaaS cloud or on your own infrastructure as an on-premise deployment.
- AUTOMATED TESTING - Perform automated regression testing of your conversations to ensure business objectives are met after changes have been made.
- UNLIMITED SCALABILITY - Cognigy.AI's containerized micro-services architecture provides unlimited scaling and built-in resilience to handle peak traffic loads.
- BUILT-IN LOGGING - Access application and system logs from within Cognigy.AI and integrate with your existing logging tools.
Differentiation
Enterprise-Level SaaS Platform
- Support of several end-points OOTB
- Support of several NLU/NLP (Cognigy, Microsoft, Google)
- Support of several backend systems OOTB
- Cross-endpoint dialog flows (true omnichannel)
- Easy to use dialog-flow editor
- Machine learning
Scalable cloud solution (Microsoft Azure), can be deployed on-premise if required
Ideal approach
Phase 1: Implement an informational bot that addresses common support queries and deploy on your website, Messenger or SMS.
Common requirements include:
- Addresscommonqueries
- Deploy on website, messengerorSMS.
- Measure, lean, iterate
Phase 2: Expand your capabilities by addressing the authenticated user, providing a Conversational interface to enterprise systems. Here, your business objectives are to keep customers in self-service mode by providing both information and action in a single interface and to measure call center contact rates for users exposed to AI vs. those using existing channels.
Common requirements include:
- Identify users
- Integrate back-end systems.
- Expose information and action
Phase 3: Your goals in Phase Three are to gain operational efficiency by standardizing on a platform, standardize activities for identifying and implementing use cases and integrate into Enterprise governance requirements.
Common requirements include:
- Enable teams across department and function.
- Define conversational standards and policies.
- Establish Conversational AI as a first-class channel along with web, mobile and email.
