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A Triniti.ai Guide

This tutorial walks you through the Triniti.ai features, functionalities including managing your account, process of building and maintaining a workspace from scratch. It also provides you the best practices in designing a workspace. It will also guide you through various channel setup, handling FAQs, Smalltalks, and more.

Manage Your Account

Account Setup

Create New Triniti.ai Account

To experience our Triniti.ai platform & our feature you will be needed to have a Triniti.ai account. It is a simple step of creating a new account on Triniti.ai. You can register to Triniti.ai by following these steps.

Note:

*** Once you fill in the form, carefully go through the Terms of Services & Privacy policy. By clicking on ‘Create Account’, you are giving us an acknowledgment that you have successfully read our Terms of Services & Privacy policy.

create_account01

create_account02

login

Congratulations! You have created your triniti.ai account successfully.
We are so happy to welcome you on board. Eagerly looking forward to collaborating and grow along with you!

Login to Your Triniti.ai Account

To access all the features and experince the Triniti.ai platform you will have to login with the account which you have created earlier. You can login to Triniti.ai platform by following these steps:

login

Recover Your Triniti.ai Account

If you have forgot your password or your account has been locked then you can recover your account by following these steps:

create_account03
recover_account02 recover_account03

recover_account04
recover_account05 recover_account06

Now you can login with your new password to Triniti.ai portal.

Create Your First Workspace

Workspace, as the name indicates, is a space that triniti.ai provides you for you to do the bot building process. It is similar to a digital folder that you will be provided with, upon purchase to start with building your bot.
Before you could start, make sure you have a Triniti.ai account. If you do not have an account, click on Create an account
This section describes how to create and try out your first triniti.ai workspace.

alt_text

Note:

Depending on the plan you choose, the costs may vary. Please look into the pricing section for details.

Manage Workspaces

Managing the workspace is a better practice to make your bot more interactive and enabling more user interactable features so that users can easily interact with the bot and get their respective responses to the query. The bot will perform any action based on your workspace configuration to give a response to the user's query. You can manage the settings of the workspace that how your bot should react, analyze & give response, also you can manage messages for some scenarios where bot can send the same messages to the user if don't find any related response for the user's query, more important you can share your workspace with user's as well like your colleagues, etc.

Managing Settings

This section will help you to configure your bot to be more interactive to the user and give the appropriate response instantly. In settings, you can set the AI, Knowledgebase, security-related settings and also can enable the customer support fallback so that the user can directly interact with a human agent. You can manage the settings of your bot as per your requirement by going to the "Workspace Settings".

1. CREDENTIALS (You can use your workspace credentials wherever it is required)

2. AI

Settings
Description
Auto suggest limit It will show the suggestions on keypress, you can set the suggestions limitmax to 5.
Minimum Smalltalk Confidence It will set the Smalltalk confidence.

3. BOT

Settings
Description
Audit anonymous user You can also audit the anonymous user by enabling this setting.
Display welcome message You can choose the option to display or not to display the welcomemessage.
User session timeout The bot session timeout can be set by this option.

4. COGNITIVEQNA

Settings
Description
Default Fallback It will give suggestions on fallback.
Minimum knowledgebase confidence The confidence for your knowledge base, can set from thissetting.
Similar suggestion It will show the suggestion after the response, similar to the previousutterance.

5. CUSTOMER SUPPORT

Settings
Description
Chat Agent Provider Users can chat with the human agent and it can be enabled by this setting. You can either select Zendesk or Livechat.
Customer support fallback To enable chat with a human agent you will have to enable this setting.
Number of failed comprehension After how many failed responses the human agent chat should be triggered, can be done from this setting.
Provider license key To enable the human fallback you will have to enter the Licence key of the respected chat agent provider.

6. SECURITY

Settings
Description
Allowed domain This feature is for security purpose, you can enable only those domain(s) which you want to be used for your workspace.

manage_settings

Managing Messages

In the messages section, you can configure the messages to show on the bot for some scenarios that bot cannot handle from their end. So you can configure some messages to show on the bot for these kinds of cases. Suppose if your bot has some issue that is not able to answer the user's query then you can configure a message(like "We are experiencing some difficulty in processing. Could you please try again later?") to show on the bot in those scenarios. You can manage the messages that will be shown on the bot for that respected case by following these steps:

1. BOT

Settings
Description
Multi Query Recognition If the user will ask multiple queries at once then the bot will respond based on the message that is set here.
Multi Query Continuation After finishing the response of one query from multiple queries the bot will send the message that is set in this section.
Default Server Error The default server error message will be shown if the bot is not able to understand the query or not able to connect to the server.
Feedback Response You can set the message for the feedback as well.
Unable to classify The bot will reply with this message if the bot is not able to classify the query.
Default answer if utterance not matched The bot will send this response if the utterance doesn't match in the knowledgebase.

2. CUSTOMER SUPPORT

Settings
Description
Live Agent Unavailable The bot will reply to this message if not any human agent is available.
Live Agent Default Error For live agent the default error message can be set here.
Live Agent Signoff If the human agent has signed off then the bot will send this response to the user.
ext to end the chat with live agent You can set a text that if the user will type that text the chat will end with a live agent.
User Live Agent Handoff The message will be shown if the human agent is about to start the chat.
Live Agent Decline The bot will send this response when the human agent will disconnect the chat.

manage_messages

Managing Templates

The templates are the combination of text with some components or some graphical pictures. You can add a template for your bot to show on bot launch or fallback. It will be more attractive for the user instead of normal text. Suppose you are showing a welcome template on bot launch in the textual format "Hi! I am your Smart Assistant! What shall we look at first?", so instead of normal text you can add a picture or any different component to make the same welcome message more attractive. In this section, you can manage the "Welcome Template" & "Live Agent Template".

1. Welcome Template:

It's a template that will be shown on the bot launch to the user by greetung them. You can configure a welcome template for your bot by adding the components to make your bot looks cool. Normally your bot will greet the customer with a welcome message in textual format but you can add the card, image or button along with the text to greet the customer.

welcome_template

2. Live Agent Template:

The template which will be shown on fallback (If the bot is not able to respond to the query). When the bot will not be able to understand the user's query then the bot will give some options like Chat with a live support agent or Continue to chat with me, etc. You can also configure a template as per your choice & requirement so if the bot will not able to classify the query will show the live agent template to take the user's choice.

live_agent_template

You can configure the template as per your requirement by adding any component from the following template components:

To know more about template configuration, please refer Configuring the templates.

Managing Users

You can add your colleagues or anyone with whom you want to share your workspace from the "manage users" section in the workspace settings by adding their Email Id & granting the access as Member, Admin or Viewer. You can also change or revoke access after adding for those people whenever you want.

manage_users

Deleting Workspace

Workspace owners can delete the unnecessary or not required workspace anytime. It will delete everything the workspace including knowledgebase & bot from other users also with whom that workspace is shared
NOTE: Only workspace owner can delete the workspace. The Member or Viewer of the workspace can not delete the workspace.

delete_workspace

Manage FAQs

The FAQs (Frequently Asked Questions) are the customer-specific questions that might be asked by the users. FAQa are usually about the business oriented product offerings. FAQs are usually interrogative in nature.You can add FAQs to your workspace as per your requirements so that bot will give a response of those FAQs which user will ask to your bot.
A FAQ will consist of the User Asks (that user may ask) and a Bot response (that your bot will respond for that particular utterance).

Adding New FAQs

You can add the FAQs either manually by entering the question(user asks), answer(bot response) or import a CSV file of your FAQs. You can also add the variants for the FAQs, by predicting how customers can ask the same question in another way. It will be helpful for the bot to give a proper response for the same intention.

import_faqs

Importing FAQs

We are supporting three types of import to add the FAQs in your knowledgebase.

You can add FAQs in your knowledge base by following these steps:

  1. Goto your workspace
  2. Click on 'Import' icon
  3. Import the Faqs:

import_faqs

Exporting FAQs

If you want to use the same knowledgebase containign all the FAQs that you have added earlier in another workspace or want to keep those FAQs, then you can export those FAQs by following these steps:

  1. Goto your workspace
  2. Click on 'Export' icon in knowledgebase
    It will download a CSV file containing id, questions, answers, products, etc columns.

export_faqs

Deleting FAQs

If you want to remove some unneccessary FAQs or duplicate FAQs, so you can remove those FAQs from your knowledgebase. You can delete a particular, multiple or all FAQs by selecting the FAQs which you want to delete by following these steps:

  1. Goto your workspace
  2. Select the FAQs which you want to delete
  3. Click on 'Delete option' or click on the 'Delete' option of a particular FAQ.

delete_faqs

How to search for the FAQs in knowledgebase?

Searching for a particular FAQ is a very hectic job, but we have made your job easy by enabling this feature to search for the FAQs very quickly. You can search for FAQs in the knowledgebase with various search options:

Search Options
Description
All It will search the FAQ among all the FAQs in your knowledge base.
By Questions With this option you can search for the FAQ by questions in the knowledgebase.
By Answers It will help you to find the FAQ by its answer in the knowledgebase.
By Categories If you have added categories to your FAQs then you can search for those FAQs by selecting categories option in the knowledgebase.
By Keyphrases If you want to search the FAQs based on key phrases that you have added in your knowledge base, then you can go with this option.
Without keyphrases If you want to search the FAQs that don't have any key phrases in your knowledge base, then you can go select this option.
With Workflows In your knowledge base if you have configured workflows in any of the FAQ then to search those FAQs you can try this option.
With Templates For some of the FAQs you have added templates and you want to search those FAQs then you can search with this option in your knowledge base.
Duplicate Questions You can search for the duplicate questions in you knwoledgebase by selecting this option.

search_faqs

Formatting FAQ Responses

The response will be rendered on the bot normally in a textual format. You can add a text response in your FAQs by entering the responses in the bot Response section. If you are not selecting any template so by default is Text.
You can format the FAQs response in three ways:

  1. Text
  2. Templates
  3. Workflow

Configuring the templates

Template editor supports formatting the FAQ response from the bot to render a good look and feel to your bot responses so that the conversations will be more interactive & user-friendly.
You can format the responses as Text, Card, Image, Carousel, List, Button, Video & Custom, etc by following these steps:

1. Text Template

It will show the responses in textual format, if you want to show some responses in the text following with some components then you can configure text template by following these steps:

text_template

2. Button Template

If you want your user to get the response by clicking on a button or suggested option, so you can configure a button template by following these steps:

Note:

1. Postback: Postback is the request body which the server gets once the user clicks on any postback type button or quick reply. The bot expects it to be of JSON type with either 'intent' or 'type' and 'data'. The postback data will be the intent_name/intent_id or a FAQ question that you want to show the user on clicking this button. You will have to add the intent_name/intent_id or FAQ in JSON format as shown below. You can add postback data in two ways with a valid postback:

With FAQ
With Intent
{
    "data": {
        "FAQ":"<ANY TRAINED FAQ UTTERANCE>"
    },
    "type":"MORE_FAQ"
}
{
    "data": {
        "options": "no"
    },
        "intent": "faq_id/intent_id"
}

Where;

2. External URL: External URL is the website URL on which you want to redirect the user on clicking this button. It will open the URL in a new tab.
3. Web URL: Web URL will open the entered website URL in the bot itself.

button_template

3. Card Template

If you want to show some cards in your response with text or other components, then you can configure a card template by following these steps:

card_template

4. Image Template

If you want to show images in your responses or for a particular utterance then you can add the image template in your response by following these steps:

image_template

5. Carousel Template

Carousel is a kind of list component in which a slider slides the images horizontally, where you can give the multiple options listing in the carousel to choose from the various options. You can configure the carousel template by following these steps:

carousel_template

6. List Template

If you want to show various options to choose from, then you can add those various options in a list. You can configure the list by following these steps:

list_template

7. Video Template

Even you can add a video for the response to show the user for any particular utterances by following these steps:

video_template

Managing Workflow

Workflow helps to define step by step conversation journeys. The intents and entities derivation is mandatory to identify the correct response to the user, but all the needed information may not be available all the time, during these cases workflow can be configured to prompt the user for more input that is needed to respond correctly.

In a workflow, each entity is handled by a node. A node will have at least a prompt and a connection. A prompt is to ask for user input and connection to link to another node. In a typical workflow that handles n entities, there would be n+2 nodes (One node per entity with a start and a cancel node).

In the workflow, we expect user inputs in a sequence, but by design, a workflow can handle any entities in any order or all in a single statement. Out of the box, workflow supports out of context scenarios.
Ex: If a user asks, How to apply for a debit card?, the defined workflow can ask for the various card selection like Rupay Card, Master Card, Visa Card, etc.
To know more please refer manage fulfillment via workflows section

Configure a workflow:

Generally we configure workflows to communicate from both end i.e; user & bot (User will ask any query then the bot will ask for some input from the user). But in some cases we don't want the user to enter any input to respond to their queries, so we are supporting two types of workflows:

1. Enquiry Type Workflow:

In the Enquiry type of workflows, we give the options to users to select for the response instead of asking for any input by typing from the user.
Ex; If the user asks to the bot "I want to know about credit card", then the bot will show the various options related to the user's query like "Vanilla Credit Card, Student Credit Card, Balance Transfer Credit Card, Rewards Credit Card etc.". So the user can select one of these options for which they want to know.

To configure a Enquiry type workflow please follow these steps:

  1. Goto your workspace
  2. Click on 'Add a Question'
  3. Enter the question
  4. Click on 'Format Response' in 'Answer' section
  5. Select 'Workflow'
  6. Select 'Workflow Type' as 'Enquiry'
  7. Configure the workflow by adding the nodes, responses, validation & connection
  8. Click on apply (On each node after configuring to save node configurations.)
  9. Click on Save (to save workflow.)

Enquiry_workflow

2. Transactional Type Workflow:

In transaction workflows, the conversation happens from both sides to give the proper response. The bot will also ask the user for some inputs to give the appropriate response for the user's query. It will be used for any lead generation process or any transaction-based task.
Ex;
If the user asks to the bot "Apply for a credit card",
then the bot will ask for the required details from the user to enter like "choose a card, enter name, enter email, etc."

To configure a transactional workflow please follow these steps:

  1. Goto your workspace
  2. Click on 'Add a Question'
  3. Enter the question
  4. Click on 'Format Response' in 'Answer' section
  5. Select 'Workflow'
  6. Select 'Workflow Type' as 'Transactional'
  7. Configure the workflow by adding the nodes, responses, validation & connection
  8. Click on apply (On each node after configuring to save node configurations.)
  9. Click on Save (to save workflow.)

transactional_workflow

Note:

Definition
Validation
Connection
definition validation connection

Bot response for both the workflows

The bot will respond to the added query as per the configured workflow as shown below:

Enquiry Type Workflow
Transactional Workflow
Enquiry_workflow transaction_worflow

Redirecting FAQs to Workflows

If you want your user to redirect on a workflow response like on an inquiry, lead generation or raise service request flow from a faq.
Eg; User is asking queries on the bot and for some question you want the user to raise a service request then you can redirect the user on the service request workflow where the user will give all the required information as configured in that workflow.
You can configure this functionality by referring to these steps:

  1. Goto your workspace
  2. Click on 'Add a Question'
  3. Enter the question
  4. Click on 'Format Response' in 'Answer' section
  5. Select 'Template Editor'
  6. Select 'Button template' & add button heading
  7. Click on 'Add Button'
  8. Enter the Question/workflow name in the button name
  9. Enter the intentId(faqId of the workflow) in the intent value under postback payload
  10. Click on save

redirect_to_workflow

Setting up Channel Specific Responses

We have a feature to add channel-specific responses. If you want a different kind of or indifferent way of response for specific channels then you can do that also on this platform.
Eg;
If you want to show the "This is web SDK response" for a question but for the same question you want to show the response "This is the Facebook response" for Facebook users then you can do that easily by following these steps:

  1. Goto your workspace
  2. Add a question
  3. Add a response for default web SDK
  4. Choose a channel from channels list on the top right corner of the response section (The channels list will show all the enabled channels)
  5. Enter the response for the particular channel
  6. Click on Add (Save the faq)

The same question will show channel-specific responses as per the responses set for specific channels.

channel_specific_response

Styling FAQ Responses

If you want to make your customers pay more attention to the bot responses then you can style your responses in a way so that the responses can be more readable, easily understandable and more attractive. So that you can engage the customers on your bot to interact and spend little more time on the bot. So we have an option for the FAQ response styling, with the help of this guided tutorial you can style your responses and make more attractive.

You can use standard HTML attributes to style your bot responses.

HTML format
Preview
Redirect the user on dialer pad/calling
on clicking the given phone number.
Eg;
You can call us on the given number
<a href="tel:your_phone_number">
    your_phone_number
</a>
On clicking the phone number it will take
the user on the dialer and ask to call on
the given number		 bold_italic
- Bold Text:
<strong>
    This is a response in bold
</strong>
or
<b>
    This is a response in bold
</b>

- Italic Text:
<em>
    This is an Italic response
</em>

- Text with underscore:
<u>
    This is a response with underscore
</u>

- Strikethrough Text:
This is a
<s>response</s>
with strikethrough text		 bold_italic
- Font color:
<span style="color: rgb(226, 80, 65);">
    This is a response with red color
</span>
or
<font color=”red”>
    This is a response with red color
</font>


- Font size:
<font size="4">
    This is a response with font size 4
</font>		 font_style
- Link/Hyperlink (word specific):
You can bind a link/url in a word using <a href> </a>.
Eg;
New to triniti.ai
<a href="https://developer.triniti.ai"
target="_blank">
    Create an account
</a> for free

- Link/Hyperlink (default):
Just paste the URL or link,
our AI engine will bind the url in a text 'here'.
Eg;
To know more about Triniti.ai
please click https://triniti.ai/

Note:
There should be sinlge space before
& after the url/link.		 link
- Text alignment(center):
<p style="text-align: center;">
    This response at the center
</p>

- Text alignment(right):
<p style="text-align: right;">
    This is right side
</p>

- Text alignment(left):
<p style="text-align: left;">
    This is left side
</p>

- Blockquote:
<blockquote>
    <p>This is a quoted response</p>
</blockquote>		 text_alignment
- Unordered List (Default Bullet points):
<ul>
    <li>One</li>
    <li>Two</li>
    <li>Three</li>
</ul>

- Unordered List (Circle points):
<ul style="list-style-type: circle;">
    <li>One</li>
    <li>Two</li>
    <li>Three</li>
</ul>

- Unordered List (Disk points):
<ul style="list-style-type: disc;">
    <li>One</li>
    <li>Two</li>
    <li>Three</li>
</ul>

- Unordered List (Square points):
<ul style="list-style-type: square;">
    <li>One</li>
    <li>Two</li>
    <li>Three</li>
</ul>		 unordered_list
- Ordered List (Default Numbers):
<ol>
    <li>One</li>
    <li>Two</li>
    <li>Three</li>
</ol>

- Ordered List (Uppercase Alphabets):
<ol style="list-style-type: upper-alpha;">
    <li>One</li>
    <li>Two</li>
    <li>Three</li>
</ol>

- Ordered List (Lowercase Alphabets):
<ol style="list-style-type: lower-alpha;">
    <li>One</li>
    <li>Two</li>
    <li>Three</li>
</ol>

- Ordered List (Uppercase Roman):
<ol style="list-style-type: upper-roman;">
    <li>One</li>
    <li>Two</li>
    <li>Three</li>
</ol>

- Ordered List (Lowercase Roman):
<ol style="list-style-type: lower-roman;">
    <li>One</li>
    <li>Two</li>
    <li>Three</li>
</ol>		 ordered_list
- Image:
If you want to add an image between your responses
like some icon (eg; info icon) then you can use this HTML format
otherwise you can use template editor for the images.
Eg;
This is a response with an
info(<img alt="info_icon"
src="https://img.icons8.com/metro/2x/info.png"
width="50" height="50">) icon.		 image
- Table(With border):
<table border=1>
    <tbody>
        <tr>
            <th>serial number</th>
            <th>description</th>
        </tr>
         <tr>
            <td>1.</td>
            <td>One </td>
        </tr>
        <tr>
            <td>2.</td>
            <td>Two</td>
        </tr>
        <tr>
            <td>3.</td>
            <td>Three</td>
        </tr>
    </tbody>
</table>

table_with_border
- Table(Without border):
<table>
    <tbody>
        <tr>
            <th>serial number</th>
            <th>description</th>
        </tr>
        <tr>
            <td>1.</td>
            <td>On </td>
        </tr>
        <tr>
            <td>2.</td>
            <td>Two</td>
        </tr>
        <tr>
            <td>3.</td>
            <td>Three</td>
        </tr>
    </tbody>
</table>		 table_without_border

Fine Tuning FAQs

If you have any unclassified utterances that are not in your knowledgebase or the unanswered question by bot to the user then you can add those utterances in your knowledgebase or update the question so that bot can give a proper response by following these steps:
1. Add New FAQ:
You can add those utterances as a new FAQ if those FAQs are not relevant to the anyone of the utterance present in your knowledge base.

2. Existing FAQ:
If the utterance is relevant to an utterance that is present in your knowledgebase which has the same response, then you can add that utterance in the existing FAQ by following these steps:

reports

Manage Keyphrases

Keyphrases are significant phrases in a FAQ like a product e.g credit card or an action e.g apply. CognitiveQnA uses keyphrases to improve the accuracy of responses and tags them automatically during the training phase.
A Keyphrase is a word/phrase that must be present in the user's query to consider and predict a question as potential candidate for deriving the desired response. Example “How to pay by credit card” can have a “credit card” as the key phrase.
Since users may type the key phrase in different ways you may also want to declare “credit crd”, “CC” as variants to these Keyphrases so they are all treated equally.
Keyphrases are used to narrow down the potential candidates against which the CognitiveQnA module runs matches. Quality Key phrases are important for accurate responses of the bot.

Kephrases Guidelines

  1. Post adding/modifying/deleting keyphrases, Training AI data is required.
  2. Maximum three words are permitted under keyphrases where the single space(s) should be replaced _ (underscore).
  3. Root value of the keyphrase should not contain the single or multiple space rather should be replaced with _ (underscore).
    Note: By default the Cognitive Q&A module generates Keyphrases automatically. But it is important that you review these for best performance.

Adding Keyphrases

You can either add the keyphrases manually by entering the key phrase & the respective synonyms or import a CSV file of keyphrases.
Manually adding the keyphrases:
To add keyphrases, one has to follow below steps:

keyphrase

keyphrase

Importing Keyphrases

You can even import the CSV file of keyphrases to add into your knowledge base by following these steps:

keyphrase

import_keyphrases

Exporting the keyphrases

If you want to use the same keyphrases in another workspace or want to keep the saved keyphrases then you can export the keyphrases by following these steps:

keyphrase

export_keyphrases

Deleting Keyphrases

If you don't want some of the keyphrases that you have saved or the keyphrases generated by our AI engine then you can remove those keyphrases by following these steps:

  1. Goto your workspace
  2. Click to manage keyphrases
  3. Click on the delete icon of the respective keyphrase that you want to delete
  4. Click on save

keyphrase

Cleaning up keyphrases

Cleanup is the process where the keyphrases that you have added will be removed from your knowledgebase and also from our database. When you train your knowledgebase then our AI engine will again generate the keyphrases from your knowledgebase automatically.
You can perform the clean up operation on your keyphrases by following these steps:

keyphrase

cleanup_keyphrases

Adding Blocked Keyphrases

If your organization is not supporting some of the features or services and you want to let your users know that you don't have those types of services or features then you can add some key phrases from those kinds of queries in blocked keyphrases. Blocked keyphrases feature allows you to add the keyphrases for which you don't want to show the error message and you can show some message like "Currently we are not supporting this service".
You can add blocked keyphrases by following these steps:

keyphrase

add_blocked_keyphrases

So if any user tries to ask a query that contains the added blocked keyphrase then the bot will show the added message respective to that blocked keyphrase.
Ex; If a bank doesn't support credit card or doesn't have credit card service then you can add "credit card" in blocked keyphrases with a message that will be shown like "Currently we are not supporting this service" to the user when the bot will find this keyword in their queries.

blocked_response

Importing Blocked Keyphrases

You can even import the CSV file of blocked keyphrases containing message & keyphrase columns to add into your knowledge base by following these steps:

keyphrase

import_blocked_keyphrases

Exporting Blocked Keyphrases

If you want to use the same blocked keyphrases in another workspace or want to keep the saved blocked keyphrases then you can export the blocked keyphrases by following these steps:

keyphrase

export_blocked_keyphrases

Deleting Blocked Keyphrases

If you want to remove some of the blocked keyphrases that you have saved then you can remove those blocked keyphrases by following these steps:

keyphrase

delete_blocked_keyphrases

Manage Small Talk

Small talks are the friendly conversations and informal type of discourse that does not cover any functional topics of conversation or any transactions. SmallTalk data can include questions like "Hi", "Who are you" and "What is your name".
Small Talk is stored in two separate categories namely; Base and Custom. Base Small Talks are provided as part of the System. Custom Small Talks are those which are created by the business users for enriching the customer experience based on the self-learning reports.

Small Talk Guidelines

  1. All the greetings, bot capability questions, and bot introduction questions possibly are asked by the users should be thought of
  2. Customer-specific Small Talks should be provided by the business user along with the appropriate neutral answers
  3. Small Talk works based on the lookup, thus all different variations to a query should be included and mapped with appropriate answers
  4. Smilies can be used in the answer for which Unicode mapping should be provided in the bot configuration.

Adding Small Talk

You can add the smalltalks manually by providing the smalltalk as 'User asks' & the respected response as 'Bot Answers'. or you can import a CSV file containing atleast 3 columns (category, questions, answers, etc).

Manually add small talks:
You can add the small talks manually by following these steps:

add_smalltalk

Importing Small Talk

If you have the file of smalltalk or want to import from a file then you can import the CSV file of smalltalk which should contain atleast 3 columns(category, questions, answers, etc.) by following these steps:

import_smallTalk

Exporting Small Talk

If you want to use the same smalltalks in another workspace or wnat to keep the smalltalks then you can export the smalltak by following these steps:

export_smallTalk

Deleting Small Talk

If you don't want to keep some of the smalltalks then you can remove those smalltalks, you can delete a particular, multiple or all smalltalks by following these steps:

delete_smalltalk

Manage Acronyms

Acronyms are custom ways to provide an alias to certain words to improve the vocabulary of the bot. An acronym is a pronounceable word/abbreviation formed from the first letter (or first few letters) of each word in a phrase or title. Because in general, the "WHO" can signify many objects, like; a. world health organization; b. it can be the question word.

The variations which need to be corrected has to be one word only. As currently Conversational AI only supports one word (input variant – i.e. bill payment) to many words (root word/phrase – i.e. bill payment) and not the other way around (i.e. bil pyment (input variants) cannot be mapped to root word/phrase bill payment.
Three types of corrections happen at the spellcheck level:-

  1. Acronym to abbreviation
  2. Map the regular expression words with the same value to stop the auto-correction
  3. Spell typo to correct word mapping
    Ex;
    • balance=balnc,blance (spell typo correction)
    • Amit=Amit, amit (regular expression)
    • account=ac.,a/c, acc, ac (acronym)

Acronyms Usage:

Acronyms file is useful to correct the three types of single token-based corrections before user input gets processed by any other modules. This helps to prepare the training data without creating variations at the typo errors or acronyms based variations. In return, such variations will become redundant.

Adding acronyms

You can add acronyms manually by adding the word & the respected acronyms, you can add multiple acronyms for the single word by following these steps:

  1. Goto your workspace
  2. Navigate to Acronyms
  3. Click on ADD NEW ACRONYMS
  4. Add the word in Words section
  5. Add respective acronyms for that word in Acronyms section
  6. Click on SAVE

add_acronyms

Importing Acronyms

You can even import a CSV file of acronyms containing words name & acronyms columns by following these steps:

  1. Goto your workspace
  2. Navigate to Acronyms
  3. Click on import icon
  4. Select a CSV file of the acronyms containing name & acronyms columns.
  5. Click on Save

import_acronyms

Exporting Acronyms

If you want to use same acronyms that you have added earlier, so you can export those acronyms by following these steps:

  1. Goto your workspace
  2. Navigate to Acronyms
  3. Click on export icon
    It will download a CSV file containing name & acronyms columns.

export_acronyms

Delete acronyms

If you don't want to keep some of the acronyms in your worksapce then you can remove those acronyms by following these stpe:

  1. Goto your workspace
  2. Navigate to Acronyms
  3. Click on delete icon of the particular acronyms
  4. Click on save

delete_acronyms

Manage Fulfillment via Workflows

Workflow Editor

The Workflow Editor as the name suggests is a GUI to create/edit a new/existing workflow.
Workbook
A workbook is where you will design the complete flow for a particular intent. Each workbook can have multiple nodes to define the flow. The figure below shows a new workbook only with a Start node and the toolbar.

<workbook image>

Toolbar
Toolbar exists in the top right corner of the workflow editor, which comprise of

Icon
Description
<fulscreen image> Full Screen: To make the editor occupy full screen or go back to the standard window.
<download image> Download: To downloads the AiFlow file. The AiFlow file can be imported for other workflow or in the other workspace.
<upload image> Upload: This can be used in case a pre-designed AiFlow file is available and needs to be uploaded to show a workflow in the editor.
<debug image> Debug Workflow: To view the source code of the AiFlow file and JSON workflow for the debugging purpose.
<save image> Save Workflow: To save the workflow.

Node
Node is one step of the process to complete a flow. Each node is attached to one entity. Moreover, every node has three responsibilities.

  1. Ask input from the user
  2. Verify inputs
  3. A route to the next node

Each node except the start node has four buttons on the right-hand side upper corner to open Definition, Validation, Connection and Delete. For the start node, there is no Validation.

Icon
Description
definition Definition: To give your node name, enter message to show on that step.
validation Validation: To validate the user input.
connection Connection: To have conditional branching to another node.
delete Delete: To delete the particular node.

Definition Tab

This category contains the following keys:

Keys
Description
Node Name Name of the node.
Node Description A brief description of what the node does.
Entities The entity to be handled by the node.
Prompt Messages to send to the user to ask for the entities.

See Prompts for more information.

Validation Tab

This category contains the keys participating in the validation of user inputs:

Keys
Description
Validation Type It defines the type of validation of the user inputs e.g. regex validation, camel route validation, etc.
Login Session Required If checked, the key secured in JSON is set to true, meaning that the user will have to be logged in as part of validation for this node.
End flow if validation fails If checked, the flow will end altogether in case the validation of the current node fails.
Error Prompt This static error text message is displayed if the user fails the validation.
Update Prompt This static text message is displayed if the user updates the value.

See Handling Validations for more information.

Connection Tab

This section can be configured to have conditional branching to another node and is reflected in the script section of the input definition in JSON.

Definition Tab
Validation Tab
Connection Tab
definition_tab validation_tab connection_tab

Defining a Workflow

Workflow helps to define conversation journeys. The intent and entity might be enough information to identify the correct response, or the workflow might ask the user for more input that is needed to respond correctly. For example, if a user asks, What is the status of flight?, defined workflow can ask for the flight number.

In a workflow, each entity is handled by a node. A node will have at least a prompt and a connection. A prompt is to ask for user input and connection to link to another node. In a typical workflow that handles n entities, there would be n+2 nodes (One node per entity with a start and a cancel node). Even though while designing a workflow we expect user inputs in a sequence, but by design, a workflow can handle any entities in any order or all in a single statement. It out of the box supports out of context scenarios or updates and many other features. See Features to check the list of workflow features.

Sequence of Execution

The workflow like any other bot conversation starts with an utterance made by the user, followed by Triniti's intent identification and consequently followed by start/init node of the workflow.
Every node has the jobs of slot-filling (getting the entity value from the user), validating user input and moving on to the next node.

As one might imagine, to fulfill above process, each node has a "prompt" to ask a user for an utterance (to get the entity it is expecting), a "validation" process to validate the entry and finally a "connection" to jump to another node to further the flow.
Hence, the Sequence of implementation involving User and Workflow is:

<sequence image>

Sample Scenario:-

  1. User says: I want to book a flight from Delhi to Singapore.
  2. Since the workflow has been configured as the fulfillment of the intent identified here (let's say "txn-bookflight"), the init (start) node is called. The connection of this node is executed, and the next linked node is called. (Hence the start node has only "connection")
  3. The node connected to the start node gets called by the "connection" part of the start node. Let's call this node X for easier understanding. For this node (and all nodes from hereon) first prompt is executed.
  4. Prompt has the responsibility of prompting the user to enter an utterance to resolve and expected entity for the current node (node X).
  5. Once the user enters the utterance, validation of the X node is executed to validate the entity value entered by the user as part of the text.
  6. On successful validation, the control moves on to connection of node X, where a logical decision is made to know which node to branch on from the current node (X).
  7. On successful execution of connection, the underlying framework has now resolved the node to be branched onto. Let's call this node X+1.

Now steps 4 through 7 will be sequentially executed for each node until the user ends the flow or there is some failure in any of the above steps or the flow itself ends successfully.
Find the detailed description of Prompts, Validations and Connections in the following text.

Prompts - User Input

Prompt defines how to ask for the required information or reply to the user. You can define it in the following ways:

Send Message

If you want to respond with a text message, choose Send Message options and add a message in Message Content text box. A message can include workflow variables using curly braces. For example, if you have name as a variable in the workflow context, to use it in the message, your message would look like Hi {name} , how can I help you?

send_message

Send Template

Sometimes it’s a better user experience to ask or show information using templates. For that you have the option to choose from:

To use one of the templates as prompt, choose Send Template in prompt and click ‘Create Template’ button. That will show you a dialog like in the figure below. See Templates for complete list with details.

send_template

Call Webhook for Prompt

You can define a webhook to return a dynamic response. Webhook defined will get a request with the event workflow_webhook_pipeline. See Webhooks for more information.

Handling Validations

If you want to validate user input before proceeding to next node, you have option to do that using one of the following:

Validation using Regex

For a typical node which expects a single simple entity like mobile number or email etc, you can use a regex to validate the input. For example, if you expect only Gmail email addresses as an input, you can use define a regex like ^[A-Z0-9._%+-]+@gmail\\.com$. Any valid Java regex will work.

Validation using Groovy Script

You can define validation code in Groovy script. Request and workflow variables are exposed along with the Webhook request object as wRequest (Refer WorkflowRequest class in GitHub). Groovy validation script needs to return status (success/error) with other optional fields like workflow_variables and global_variables as JSON response.

Sample Groovy Script

import groovy.json.JsonSlurper
import java.text.SimpleDateFormat
import java.text.DateFormat
import java.util.Date
import java.text.ParseException

if (sys_date != null) {
    DateFormat df = new SimpleDateFormat("dd-MM-yyyy");
    Date now = new Date()
    try {
        Date date = df.parse(sys_date);
        if (date < now) {
            return new JsonSlurper().parseText(' {"status":"error"}')
        } else {
            return new JsonSlurper().parseText(' {"status":"success", "workflow_variables": {"travel_date": "' + date.format('yyyy-MM-dd') + '"}}')
        }
    } catch (ParseException pe) {
        return new JsonSlurper().parseText(' {"status":"error"}')
    }
} else {
    return new JsonSlurper().parseText(' {"status":"error"}')
}

See Scripting via Groovy for more information.

Validation using JavaScript

Like Groovy script, you can also define validation code in JavaScript. Request and workflow variables are exposed along with the Webhook request object as wRequest (Refer WorkflowRequest class in GitHub). Validation script needs to return status (success/error) with other optional fields like workflow_variables and global_variables as JSON response.

Sample Javascript

function test() {
    if (sys_amount == '7000' && wRequest.bot.languageCode == 'en') {
        return {
            'status': 'success',
            'workflow_variables': {
                'travel_year': '2019'
            }
        };
    } else {
        return {
            'status': 'error'
        };
    }
}
test();

See Scripting via JavaScript for more information.

Response expected from Groovy and JavaScript Validation Scripts:

{
  "messages": [...],
  "render": "<WEBVIEW|BOT>",
  "keyboard_state": "<ALPHA|NUM|NONE|HIDE|PWD>",
  "status": "<success|error|2faPending|2faSuccess|2faFailure|pending|loginPending>",
  "expected_entities": [],
  "workflow_variables": {
    "entity_1": "value_1",
    "entity_2": "value_2"
  },
  "global_variables": {
    "entity_3": "value_3",
    "entity_4": "value_4"
  }
}

Validation using Webhook

For complex cases, you can define a webhook to validate user input. Webhook defined will get a request with the event wf_validation or wf_u_validation. See Webhooks for more information.

Connections

Based on the current input and other previous inputs, you can instruct what to do next. For that, you have the following options to define the connections between the nodes. For example, at node A you asked for user's age and now based on that you want to take a call whether to allow him to book the tickets or not. These kinds of rules you can define in the connections. When even you create a new node from an existing node, an entry is added in the connection of that node. You can define the conditions there, or if there is only a single connected node, it's automatically added as the default next node. To define these connections you have these options:

Connection Builder

Connection Builder is a GUI to define simple routing. It's the best option to go while defining a mockup of the flow or for simple use cases where routing is only based on entities or just a default routing. Whenever you create a new node from any existing node that new node entry is added to the parent node connection builder as default routing. If multiple nodes are added from a node, then you need to define the conditions to route to each node and can keep one as the default fallback node.

connection_builder

Groovy Script

You can use a Groovy script to define routing. The groovy script needs to return the response in JSON format. For example, as per below code snippet, if text is hello go to the node with id world else to the node with id error.

import groovy.json.JsonSlurper
if (text == 'hello' ) {
    return new JsonSlurper().parseText('{"id":"world", "type": "node"}')
} else {
    return new JsonSlurper().parseText('{"id":"error", "type": "node"}')
}

JavaScript

Similar to Groovy, routing can be achieved using Javascript function like:

function test() {
    if (text == 'hello') {
        return {
            "id": "exit",
            "type": "node"
        }
    } else {
        return {
            "id": "error",
            "type": "node"
        }
    }
}
test();

Connection Webhook

You can define a webhook to define a dynamic routing. Webhook defined will get a request with the event wf_connection. See Webhooks for more information. The property of "webhook" as a tool to call API is useful here in case the connection needs extensive coding or in case the developer wants to exercise language discretion.

Scripting via JavaScript
Scripting via Groovy
javascript_connection groovyscript_connection

Templates GUI

Button payload data to be in JSON format with data and intent. Data could be any JSON object.
For example:

{
    "data": {
        "flight-no": "AI381"
    },
    "intent": "txn-bookflight"
}

Features

  1. Workflow Cancellation
  2. Amend inputs
  3. Workflow Context
  4. Global Context
  5. Handling multiple inputs in a single statement
  6. Visualize flow
  7. Partial state save (Coming Soon)
  8. In step login

Debugging Workflows

~Comming Soon~

Workflow FAQs

Q. What can I do if I need to add a static prompt as well as a dynamic template?

A. You can use a combination of Send Message and Call Webhook to show a static text message followed by a dynamic template (or even text) sent from the API implementation. You can also use Call Webhook to do any number of text-template combinations.

Q. When is the text in update and error prompts in the "validation" tab of a node displayed?

A. Error prompt tells the user if for a particular node validates the entity entered by the user as incorrect. Similarly, update prompt is to let the user node if an older entity (the node that has already been executed) gets updated. Update Prompt gives the update message of the entity (node) that has been updated and not of the current node.

Q. What is the postback? What is the format to define that?

A. Postback is the request body which the server gets once the user clicks on any postback type button or quick reply. Morfeus expects it to be of JSON type with either 'intent' or 'type' and 'data'. For example this is a valid postback:

{
    "data": {
        "flight-no": "AI381"
    },
    "intent": "txn-bookflight"
}

Q. How do I point a button to an FAQ?

A. You can create a postback type button with following postback data:

{
    "data": {
        "FAQ": "<<ANY TRAINED FAQ UTTERANCE>>"
    },
    "type": "MORE_FAQ"
}

Manage Fulfillment via Webhooks

A webhook is nothing but an endpoint or an API that can be summoned to fulfill a particular task or in AI terminology, a particular intent. The primary motive to have a webhook as a mode of fulfillment is to call an API that can be written in any programming language and be hosted on a server and be accessible irrespective of the scope of the classes calling it.
We'll go through this feature to see how it can be leveraged to define a fulfillment for your intent.

Defining a Webhook

A webhook is reasonably easy to define. It just requires a URL and a secret key that is used to validate the requester of the API is accessed. Triniti.ai leverages the use of this feature to support vanilla fulfillment via webhooks and as part of other fulfillment as well, namely, Workflow. One can define a webhook as fulfillment for intent and in the same way a webhook can be called for a particular node's implementation within a workflow. Refer Workflows for a better understanding of workflow basics.

Webhook Signature

A Webhook signature has two components, namely,
* Webhook Url : the URL of the webhook/API called to fulfill the intent.
* Webhook Secret Key : a string value that is used to validate the requester by matching the values of the secret key in the signature and the one defined in the API. See Security for more information on how to secure your webhook using security key.

To create a fulfillment via webhook for a particular intent you can click on the Call Webhook button seen at the bottom of the page once you navigate to any intent, along with other fulfillment options.

<Setup webhook fulfillment>

Conversational Workflow Framework

Please refer to the article on workflow for the detailed understanding of dialog flow management in Triniti.ai.

Other than acting as a method of fulfillment for an intent all by itself, webhook can also be leveraged to support certain parts of steps within a workflow. As we know that workflow is essentially a sequential and logical implementation of steps (nodes), it might be required to have multiple logical checks to be made to decide which step (node) to call next. It may not always be the best idea to perform these tasks within the static scripts within a node, or worse in multiple nodes. This is where webhooks come into the picture.

You can define a webhook to implement prompt, validation or connections of a node in a workflow. The definition/signature of the webhook is the same as above, i.e., a URL and a secret key, just the purpose changes.

<Setup webhook in a workflow node>

Webhook Request

Your webhook to receive a POST request from Triniti.AI. For each message from a user, this webhook will be called, depending on webhook is configured for across bot or per intent. This request format is chosen to simplify the response parsing on the service side to handle multiple channels.

A request to comprise of following fields to give you details about the bot, user profile, user request, and NLP. For text requests, request body to have all the enabled fields for NLP, for postback requests you may get few of them.

Generic Webhook Request Format

{
    "id": "mid.ql391eni",
    "event": "wf_validation",
    "user": {
        "id": "11229",
        "profile": {}
    },
    "bot": {
        "id": "1874",
        "channel_type": "W",
        "channel_id": "1874w20420077206",
        "developer_mode": true,
        "sync": true
    },
    "request": {
        "type": "text",
        "text": "mumbai"
    },
    "nlp": {
        "version": "v1",
        "data": {
            "processedMessage": "mumbai",
            "intent": {
                "name": "txn-bookflight",
                "confidence": 100.0
            },
            "entities": {
                "intentModifier": [{
                    "name": "intentModifier",
                    "value": null,
                    "modifiers": []
                }],
                "source": [{
                    "name": "source",
                    "value": "mumbai",
                    "modifiers": null
                }]
            },
            "debug": [{
                "faq-subtopic-confidence": 0.0,
                "faq-topic-confidence": 0.0
            }],
            "semantics": [{
                "sentence-type": "instruction",
                "event-tense": "present",
                "semantic-parse": "location:DESCRIPTION[]"
            }]
        }
    },
    "workflow": {
        "additionalParams": {
        },
        "workflowVariables": {
            "modifier_intentModifier": "",
            "modifier_destination": ""
        },
        "globalVariables": null,
        "requestVariables": {
            "intentModifier": "null",
            "source": "mumbai"
        },
        "nodeId": "Source",
        "workflowId": "bf9f3713-7921-4927-8a40-5876b1012543"
    }
  }

Request Body

Property Type Description
user Object User object. User details acquired from that particular channel
time String Timestamp of the request
request Object Request object. User Request Details
nlp Object NLP object. Natural Language Processing information about the request
id String Unique ID for each request
event String Event Type
bot Object Bot object. Bot details
workflow Object Workflow object. Only for requests made from workflow.

User Profile

Property Type Description
id String Channel User ID
profile Object Profile information acquired from the Channel

Bot Details

Property Type Description
id String Triniti AI Bot ID
channel_type String Channel type
channel_id String Channel ID for the Bot
developer_mode Boolean Developer or Live mode
language_code String Bot language code
sync Boolean Channel is sync or async

Natural Language Processing

Property Type Description
version String Triniti API version
body Object NLP body fields depends on Triniti API version

Workflow Object

Property Type Description
workflowId String Unique ID for workflow
nodeId String Unique ID for workflow node
requestVariables Object Local request variables
workflowVariables Object Variables persisted across workflow
globalVariables Object Variables persisted across session
additionalParams Object Some additional data

You can use Webhook Java library to parse the request.

Webhook Response

Webhook response has most of the generic components, but some are specific to its implementation within the workflow.
Following is the expected webhook response structure.
Generic Webhook Response Format

{
    "messages": [{
        "type": "text",
        "content": "<text_message>",
        "quick_replies": [{
            "type": "text",
            "title": "Search",
            "payload": "<POSTBACK_PAYLOAD>",
            "image_url": "http://example.com/img/red.png"
        }, {
            "type": "location"
        }]
    }, {
        "type": "list",
        "content": {
            "list": [{
                "title": "",
                "subtitle": "",
                "image": "",
                "buttons": [{
                    "title": "",
                    "type": "<postback|weburl|>",
                    "webview_type": "<COMPACT,TALL,FULL>",
                    "auth_required": "",
                    "life": "",
                    "payload": "",
                    "postback": "",
                    "intent": "",
                    "extra_payload" :"",
                    "message": ""
                }]
            }],
            "buttons": []
        },
        "quick_replies": []
    }, {
        "type": "button",
        "content": {
            "title": "",
            "buttons": []
        },
        "quick_replies": []
    }, {
        "type": "carousel",
        "content": [{
            "title": "",
            "subtitle": "",
            "image": "",
            "buttons": []
        }],
        "quick_replies": []
    }, {
        "type": "image",
        "content": "",
        "quick_replies": []
    }, {
        "type": "video",
        "content": "",
        "quick_replies": []
    }, {
        "type": "custom",
        "content": {}
    }],
    "render": "<WEBVIEW|BOT>",
    "keyboard_state": "<ALPHA|NUM|NONE|HIDE|PWD>",
    "status": "<SUCCESS|FAILED|TFA_PENDING|TFA_SUCCESS|TFA_FAILURE|PENDING|LOGIN_PENDING>",
    "expected_entities": [],
    "extra_data": [],
    "audit": {
        "sub_intent": "",
        "step": "",
        "transaction_id": "",
        "transaction_type": ""
    }
}

You can use Webhook Java library to form the response.
It has provision to accept responses of multiple types, namely :

Templates

1. quickReplyTextTemplate

Sample :

{
  "button":["Select"],"title":"XXXX 5100"
}

2. imageTemplate

Sample :

{
  "image":"imgs/card.png"
}

3. buttonTemplate

Sample :

{
  "buttons" : [ {
    "title" : "yes"
  } ]
}

4. carouselTemplate

Sample :

[
  {
    "buttons" : [ {
      "title" : "yes"
    }
                ],
    "image" : "https://beebom-redkapmedia.netdna-ssl.com/wp-content/uploads/2016/01/Reverse-Image-Search-Engines-Apps-And-Its-Uses-2016.jpg",
    "title" : "head",
    "subtitle" : "subtitle"
  }
]

5. listTemplate

Sample :

{
  "list": [
    {
      "image": "imgs/card.png",
      "buttons": [
        "button"
      ],
      "subTitle": "XXXX 5100",
      "title": "VISA"
    },
    {
      "image": "imgs/card.png",
      "buttons": [
        "button"
      ],
      "subTitle": "XXXX 5122",
      "title": "VISA"
    },
    {
      "image": "imgs/card.png",
      "buttons": [
        "button"
      ],
      "subTitle": "XXXX 5133",
      "title": "VISA"
    }
  ]
}

6. videoTemplate

Sample :

{
  "video": "https://www.w3schools.com/html/mov_bbb.mp4"
}

7. custom

This type can have user-defined JSON payload to render a user-defined template. The above templates are what is provided to the user by triniti.ai, but the user is free to define his/her own templates/response types and populate them from his/her API.

Other components of response:

As part of Workflow, Webhook responses have some additional fields, namely :

Security

The HTTP request to contain an X-Hub-Signature header which contains the SHA1 signature of the request payload, using the app secret as the key, and prefixed with sha1=. Your webhook endpoint can verify this signature to validate the integrity and origin of the payload.

Please note that the calculation is made on the escaped Unicode version of the payload, with lower case hex digits. For example, the string "äöå" will be escaped to "\u00e4\u00f6\u00e5". The calculation also escapes '/' to '\/', '<' to '\u003C', '%' to '\u0025' and '@' to '\u0040'. If you calculate against the decoded bytes, you will end up with a different signature.

Java sample code available at GitHub.

Manage Fallback to Human Agent

If your bot is not able to understand or classify the user's query then it can't be able to send the proper response to the user. But we have enabled a feature to provide a live human agent for the users to interact with. You can configure a human agent fallback to provide live human agent support, so that if the bot is not able to understand or classify the user's query then it will ask for the human agent support to the user.

We are currently supporting three customer support vendors that enable the live human agent interaction with the user.

  1. Zendesk
  2. LiveChat
  3. FreshChat

Configure Zendesk

Zendesk is an application that enables the users on your bot to chat live with your customer support (Live human agent). We are supporting the Zendesk as a Live agent fallback. If you want your customer to directly interact with the human agent and get their query resolved, then you can configure the Zendesk for your bot. Zendesk allows customer conversations to flow seamlessly across all channels. Zendesk makes better experiences for agents and customers.

Note: You will be needed to choose the 'Enterprise' plan to configure the zendesk with your bot to interact with a human agent.

To configure the zendesk zendesk as a human agent fallback for your bot please follow these steps:

zendesk_get_key

zendesk_get_key

zendesk_triniti

Congratulations! You have configured your bot successfully with Zendesk to interact with your users with a live human agent.

Configure LiveChat

LiveChat is an application that enables the users on your bot to chat live with your customer support (Live human agent). We are supporting the LiveChat as a Live agent fallback. If you want your customer to directly interact with the human agent and get their query resolved, then you can configure the LiveChat for your bot.
This tutorial will take you through the LiveChat configuration for your bot.

To configure your LiveChat, you will be required to have a LiveChat account (Create LiveChat account) & a LiveChat App.
After creating your LiveChat account, Login with your credentials to the LiveChat portal and follow these steps to create a LiveChat App

Create a LiveChat App

Congratulations! You have just created your first LiveChat App.

Configure the LiveChat App

live_chat

live_chat

live_chat

Note: Please verify the API-key & API-secret with your workspace's API-key & API-secret before copying the Webhook Endpoint URL.

URLs to whitelist on the LiveChat App
i. Refresh Token API
https://dev.triniti.ai/triniti-ai-backend/api/workspaces/liveChat/refreshToken

ii. Webhook Endpoint URL
https://router.triniti.ai/social/{API-key}/{API-Secret}/morfeus/v1/chatagent/callback/livechat/webhook*

live_chat

live_chat

live_chat

live_chat

live_chat

Congratulations! You have configured your bot successfully with your LiveChat App to interact with your users with a live human agent.

Configure FreshChat

We are supporting FreshChat also as a human support fallback. Freshchat is a live chat product that helps users to interact with the live human agent for your services/business. You can configure the FreshChat for your bot so that if bot is not able to classify the user's query it will ask the user to chat with the live human agent.

Please make sure that you have selected ' United States' Data Center while creating the account on FreshChat.

Note: You will have to upgrade your FreshChat App with the best suitable plan to get the API token & configure the webhook and these services as shown in the following picture.
ie; You will be needed to select 'ESTATE' plan to get the API token & configure the webhooks.

freshchat

You can configure the FreshChat on your bot by following these steps:

freshchat

To get the Client Secret & Provider License Key, please follow these steps:

freshchat

freshchat

freshchat

Note: Please verify the API-key & API-secret with your workspace's API-key & API-secret before copying the agent chat fallback URL.

freshchat

freshchat

Congratulations! you have successfully configured the live FreshChat human agent for your bot.

Manage Analytics

Analyse Dashboard

Analytics will help to know the overall statistics of bot & AI Engine, which gives many details in terms of Users, Accuracy, Channels, Domain, etc. You can monitor the requests and responses of the bot to improve the services and serve the user better. We can check the number of users & new users registering on the bot, number of hits receiving on the bot, the accuracy of the knowledge base on a daily, weekly, monthly basis and for a particular date basis as well.

We can filter the report based on Date (Daily, weekly, Monthly, Yearly) with Domains (FAQ, SmallTalk, etc) & Channels(Web SDK, Facebook, Google Assitant, etc). Using this report, we can do detailed analysis on Accuracy obtaining, No of Logged-in Users, NewUsers, Total Session in range, Feedback percentage and many more.

analyse_dashboard

We can filter the report based on Date (Daily, Weekly, Monthly, Yearly), Channels(WebSDK, Facebook, WhatsApp, etc.), Segments, etc.

Using this report, we can do detailed analysis on Accuracy obtaining, No of Logged-in Users, NewUsers, Total Session in range, Live Chat Redirections, Feedback and many more.

Active Users - Registered users who launched the bot or interacted with the bot during selected date range.

Logged in Users - Unique Users who logged in to the applications during selected date range.

New Users (Registered) - Newly registered users who launched the bot or interacted with the bot during selected date range.

Returning Users (Registered User) - Returning registered Users who launched the bot or interacted with the bot during selected date range but registered before the selected data range.

Total Users (Till Now) - All users including registered, blocked etc.

Logins - Total Login Attempts.

We can also analyze:

You can check:

You can check:

Customer Conversation History

You can monitor the queries that are being asked on the bot by users and can improve the bot based on that monitoring. It will make the job easy to understand the queries that are being asked more and improve the services. If you want to update or add any query you can do that from this page directly.
You can apply a filter for the conversations in the following ways:

Filter
Description
Search Converartion You can search for the particular query by entering the query.
Intent Type You can search on the basis of following intent:
- Ambiguous
- UnSupported
- UnClassified
- FeedBack
Channels You can search the conversations on channels by selecting a particular channel.
Confidence You can also search for the queries based on the confidence of the utterances.

reports

In this report you can analyse the following parameters:

Report Parameters
Description
Date The date of the asked query.
UserId The user id of the interacted user.
Query The utterance that has been asked by the user.
Intent Type of the utterance that user has asked eg; faq, small talk, etc.
Reponse The response was given by the bot to the user's query.
Confidence Confidence of the utterances that bot has given the response.
Location User's location, from where the user has interacted with the bot.

To check the conversation history follow these steps:

analytics

Customer Profile

You can also monitor the Anonymous user's profile, registered user's profile and what they have chatted on the bot and more details like the location of the user, last access/login, messages sent by the user, for how much time the user was active on the bot, what chatting user has done on the bot, the complete information can see in "Customers" section of the "Analytics" page. You can even export the chat for further analysis or as the need for that chat.
Note:
- Anonymous Users: The users who has interacted with the bot without any login or authentication. anonymous_users - Registered Users: The users who has interacted with the bot with some authentication or login. registered_users

You can analyse the following parameters from the customer profile:

Report Parameters
Description
Name Name of the user who has interacted with the bot.
Customer ID User's customer id.
Email ID User's Email Id.
Phone No. User's phone number.
Registered Date The date when the user has registered himself/herself on the bot.
Last Access Date The date of last access from the user to interact with the bot.
Last Access Location The location of the user from where interacted with the bot.
Channel The channel from the user has interacted with the bot.
Status The user status whether user is active, blocked, etc.
Last Login Date The date on which the user has logged in to interact with the bot.
Browser Agent The browser from where the user has interacted with the bot.
Overall Messages The number of messages that have been transferred between user & the bot.
Accuracy The accuracy of the bot to give the response for utterances.
Average Session The time until the user was active on the bot.

customer_profile

reports

To check customer profile follow these steps:

analytics

Conversational Analytics

The Conversational Analytics shows overall statistics of the AI Engine and what is happening on the bot in a graphical manner, which includes all the answered & unanswered utterances including FAQs, Smalltalks, Clicks, Intents, etc. This will contain all the unique counts of the messages based on the category of the utterances (Eg; FAQs, Smalltalks, Intents, etc.), you can expand the total messages to see the category of utterances and their respective unique counts.

In the conversation analytics, you can filter based on the date, channel, response type, and language. It will also show the top total messages.

conversational_analytics

AI Version upgrade process

This tutorial explains the AI upgrade process.

AI_version_upgrade

  1. Goto workspace settings
  2. Click on Upgrade version x.x mlt under credentials section

AI_version_upgrade

AI_version_upgrade
AI_version_upgrade AI_version_upgrade

Batch Testing

If you want to test utterances which are added in the knowledgebase has proper accuracy or will give a proper response, so you can perform a batch testing for your knowledgebase rather than testing individually.

Samples files
You can download these sample files for better understanding:
1. Batch Test Sample File (The file which will be uploaded to perform a batch test)
2. Batch Test Result

Batch Testing process

  1. Navigate to 'Publish' section from side menu
  2. Click on 'Batch Testing'
  3. Upload the created csv file.

batch_test

After uploading the 'faqs.csv', the batch testing will start.

batch_test

batch_test

Manage Billing & Subscription

Setting up Payment Information

Invoice payment can be made through credit card or EFT( Electronic Funds Transfer)

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We accept Master, Visa and Amex type of credit cards.

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Workspace payment mapping. Each workpsace can be configured to used different credit cards.

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Charges

Charges can be configured on either per API or Slab wise. Charges can be configured differently for each plan.

Per API
Based on the total api usage , workspace charges will be calculated.

Slab

Managing Plans

We have the following paid plans whose charges will be differ.

User can upgrade from

But can't downgrade

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Managing Invoices

The Invoice will be generated on 1st of every month for the previous month workspace usage. Be default each workspace allocated a payment method (Primary credit card) and can be changed. Different invoices generated based on different payment methods.
For instance, there are three workspaces (A, B & C). Workspace A & B allocated a payment method say Visa Credit Card and workspace C allocated a payment method say Master Card. There will be two invoices one with Visa Credit Card - includes charges of workspaces A & B and another Master Credit Card for workspace C.

Respective credti card will be charged with the calculated invoice amount who details are in the invoice PDF.
If the payment fails for a credit card, there will be 3 re-try attempts. Even 2rd attempt fails the workpsaces belongs to that invoice gets BLOCKED. Customer can't use those workspcase. Customer needs to make sure the payment arrangement done for the same. Customer have the 'Pay Now' option for the failed invocies which once paid the workspaces status becomes ACTIVE and can be used again.

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Invoice contains the details like total api consumed, domain charges, discount if applicable and taxes and email will be sent to the registed user email id.
The invoice amount will be charged on the same day of invoice generation. If Invoice amount is not paid with 3 attempts, then the workspace will be marked as BLOCKED.
Once the invoice amount is paid through Pay Now option, then workspace status will be changed to ACTIVE.