Thirdeye Data offers comprehensive data engineering and data science services, aiming to transform large volumes of data into actionable insights for businesses. The document also highlights the use of chatbots, powered by AI and IBM Watson, to enhance customer engagement across various sectors. Additionally, it outlines the steps necessary for creating successful chatbots, including defining intents and entities, and emphasizes the significance of dialog in improving user interactions.

1. DATA ANSWERS
ThirdEye Data
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2. ThirdEye Data
About Us
Data Engineering
Worldwide Delivery Capability
DATA ANSWERS
ThirdEye answers your data
questions and offers
actionable insights, real-world
experiences and strategic
recommendations.
A Silicon Valley based one-
stop-shop for Data
Engineering and Data Science
services & products.
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Data Sciences
BigDataCloud Community

3. Marquee Customers

4. Data Science Services

5. Data Sciences
Data Science Services from a
Big Data perspective.
We implement end-to-end
Big Data solutions that fully
integrates Data Sciences
seamlessly into one.
ThirdEye Data
About Us
Data Engineering
Worldwide Delivery Capability
Data Sciences
Eyera - IoT Solution
BigDataCloud Community

6. Leveraging latest trends in data sciences, we enable
businesses to churn through petabytes of data to
transform information into knowledge which results in
additional revenue generation.
■ Microsoft
■ Nokia Networks
■ Pier 1 Imports
■ FreedomPop
Data Sciences
References Available on Request.

7. Conversational ChatBot using
IBM Watson Conversation Service

8. ■ Leveraging the power of Artificial Intelligence, Chatbots are essentially
virtual robots that are transforming the way companies interact and engage
with their customers.
■ Chatbots are rapidly gaining user traction and being used across domains
and use cases. They are getting up-close and personal to their human
counterparts, to a point at which its sometimes difficult to tell the
difference!
■ Using Chatbots, businesses are now able to engage consumers in more
ways than ever before:
1. Content Consumption
2. Customer Service
3. Transactional Engagements
Transforms Business Models

9. Business Applications
Use Cases
■ Order/Transactions
Management
■ Customer Support
■ Technical Support
■ Concierge Services
■ Financial Consulting
■ Virtual Assistant
Sectors
■ Retail
■ Telecom & ISP
■ Banking & Financial
■ Medical Services

10. How Chatbots Work
Chatbots are created for a specific purpose.
■ Rule Based:
Function on pre-defined set of options and answers
questions which are within its designed parameters.
■ Artificial Intelligence Based:
Function on more sophisticated Data Sciences related
techniques like:
- Natural Language Processing
- Sentiment Analysis and
- Deep Learning

11. Steps to creating successful Chatbots
1. Identify the problem to be solved and understand the
use cases
2. Choose the channel for your Bot, Facebook
Messenger, Skype, Slack, etc.
3. Choose the service you will use to build your Chatbot:
Azure Bot Service, IBM Watson, Motion.ai, etc.
4. Emulate conversations and train Chatbot accordingly.
5. Test
6. Launch

12. LIVE DEMO

13. Tools for creating Chatbots
By using
IBM Watson Conversation
NoSQL DB

14. Prerequisite
1. An IBM Bluemix account to create Watson Conversation Service.
2. A Cloud Foundry application to host the frontend application.
3. A Python Flask application to act as API.

15. Setup Watson Conversation
1. Intents
2. Entities
3. Dialog
An Intent is a purpose or goal expressed in a customer's input, such as answering a question or
processing a bill payment. By recognizing the intent expressed in a customer's input, the
Conversation service can choose the correct dialog flow for responding to it.
An Entity represents a class of object or a data type that is relevant to a user's purpose. By recognizing
the entities that are mentioned in the user's input, the Conversation service can choose the specific
actions to take to fulfill an intent.
The Dialog component of the Conversation service uses the intents and entities that are identified in the
user's input, plus context from the application, to interact with the user and ultimately provide a useful
response. Your dialog is represented graphically as a tree; create a branch to process each intent that
you define.

16. What is Context?
The dialog context is the mechanism for passing information between the dialog and your
application code.
Sample Context:
{
"context": {
"my_dessert_string": "ice-cream",
"toppings_array": ["onion", "olives"],
"age_number": 18,
"complex_object": {
"user_firstname" : "Peter",
"user_lastname" : "Pan",
"has_card" : false
}
}
}

17. SCREENSHOTS

18. Demo – Front Screen 1

19. Demo – Front Screen 2

20. Bluemix Login Screen

21. Select Watson Conversation Service

22. Create Watson Conversation Service

23. Create WorkSpace

24. Define Intents

25. Define Entities

26. Create Dialog Tree

27. Information Sources
1. Watson Conversation Documentation: https://tinyurl.com/lny9eha
2. Python Flask CF App SDK for Bluemix: https://tinyurl.com/m8hqvvo
3. Python SDK for Watson Developer Cloud: https://tinyurl.com/lkuanmy
Disclaimer:
There are several SDKs are available for Watson and Bluemix services. We have just used
Python SDK for our Demo purpose. The same application can be developed using other
languages.

28. QUESTIONS
http://thirdeyecss.com/contact-us/

29. Contact
Dj Das
Founder & CEO
djdas@thirdeyecss.com | 408-431-1487 | @djdas
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