/setup with 60+ products & services. • Stay in the cloud. Improve latency & security. Other advantages User types in Web Interface Machine Learning Detection Call to Dialogflow Store in Data Warehouse Webhooks Return to customer vs.
users type into a chat UI which can be integrated in your apps, websites or social media channels. When user ask to talk with a human, the operator can take over from the chatbot. Detect in real-time the users mood, and let the operator take over from the chatbot. This solution uses Kubernetes containers with Socket.io to enable realtime, bi-directional communication between web clients and servers. Architecture: Dialogflow with human handoff User types in chat UI Chatbot or User replies Operator Dashboard Kubernetes Engine Container Registry Customer Client Kubernetes Engine Back-end Kubernetes Engine Back-end Kubernetes Engine NLP API ML call for sentiment detection Pass user utterance to chatbot Return text response to user User asks to talk with human Negative sentiment let a human take over the conversation Operator types in chat UI User replies Containers images can be stored in the Container Registry Dialogflow Enterprise
User types to custom UI or channel Chatbot replies Dialogflow Enterprise Customer Client JS Angular 5 web front-end Kubernetes Engine Chat Server Dialogflow SDK / socket.io Kubernetes Engine Back-end CRM Python / Django Kubernetes Engine Container Registry Containers images can be stored in the Container Registry Messaging Publisher Pub/Sub Webhook Router Cloud Function Webhook Container Builder Building Dev Pipelines
couple of evenings. Improve your user experience with a little more cloud. • Fast & Easy integration of various cloud products: ◦ Kubernetes Engine, Cloud Builder, Container Registry ◦ Cloud Functions, Pub/Sub, BigQuery ◦ DLP API, NLP API • Cloud IAM, Logs, Traces, Debugging, Error reporting out of the box • Scalable & Complaint: Ready to go live. Conclusion