John Thomas: It is.
Abhijit Thakare: Yeah.
Uday Kumar Javangula: So I'll just give a quick background of it.
Uday Kumar Javangula: So we have built this capture which is a enterprise knowledge platform with a capabilities where it will help all the organizations or different organizations to streamline their unstructured data to a structured format.
Uday Kumar Javangula: With different data links we'll be having right to make sure that the knowledge is being utilized properly and well utilized throughout the system.
Uday Kumar Javangula: So we take care about the core functionality. So knowledge management which contains the content management including.
Uday Kumar Javangula: Features like authoring right.
Uday Kumar Javangula: Writing a content and also associating the metadata and also we take the basic features of entire content management on top of it. We have added the A features and enterprise search capabilities here where it will help the organizations to streamline their knowledge into the knowledge creation knowledge consum.
Uday Kumar Javangula: And knowledge availability as well.
Uday Kumar Javangula: So it's basically an API driven platform, so it is not tightly coupled with the UI.
Uday Kumar Javangula: You can integrate with anyone of your existing third party like it like as Oracle or a Salesforce. Any other third party through consuming this APIs so they can consume all the search related functions and AI capabilities on top of it.
Uday Kumar Javangula: So that is a base idea.
Uday Kumar Javangula: And internally it calls. It can call any kind of data, whether it is your Microsoft Office document types or any text files. Any Jason files XML.
Uday Kumar Javangula: So it can crawl all the data and make it structure oriented point of view. And also if you have any data in your sales force, other third party integrations also it supports so which is easier for.
Uday Kumar Javangula: To tackle all the different data of the organization to one single source of truth.
Uday Kumar Javangula: Which creates an unified repository in capture users with different user groups or different visibility.
Uday Kumar Javangula: They can log into the system and they can fire a query by using a keyword search or any kind of AI or NLP search.
Uday Kumar Javangula: Also, they can do so.
Uday Kumar Javangula: We made the search easier here by adding the AI, because most of the the keywords such as lapsed already so people are not interested on keyword search.
Uday Kumar Javangula: They wanted to do some kind of AI based on.
Uday Kumar Javangula: Models that we have, right and.
Uday Kumar Javangula: These are the features that currently we have which contains all set of integrations. I'll show you that shortly how the integration works and AA capabilities to.
Uday Kumar Javangula: Mainly, I'll focus on AI because I think any questions somebody's talking.
Bhupendra Modi: So one quick thing, Uday.
Bhupendra Modi: So when you say it is able to search all the documents et cetera, it's not realtime, right?
Bhupendra Modi: All the documents you will pass you will get them indexed and then it will be stored in your database, right?
Uday Kumar Javangula: Yeah, it's a real time indexing.
Uday Kumar Javangula: I'll. So it's a real time indexing.
Uday Kumar Javangula: Suppose if you upload a document or if you sync your data source to capture. So in real time the data will be indexed and make it available for end user search consumption.
Bhupendra Modi: Mm hmm.
Uday Kumar Javangula: So we index this data into background views. All kind of indexing mechanism through open search and all. So it will be in real time in few seconds that document will be indexed and you can search a query against it.
Bhupendra Modi: OK.
Uday Kumar Javangula: And system supports all the deep search as well like regular keyword search on top of a deep search. If you upload any zip file which contains the multiple data so system can now auto extract all the things and do the auto meta tagging into the repository you can.
Uday Kumar Javangula: Search against it using.
Uday Kumar Javangula: Either am or a general keyword search.
Uday Kumar Javangula: It's a real time and it supports system supports.
Uday Kumar Javangula: It's multilingual.
Uday Kumar Javangula: So to make sure that the system we have to work the platform as a global platform.
Uday Kumar Javangula: So system supports all the kind of available 43 languages which we can view the with that language as well as you can write the content and we also have the translation feature where we can translate.
Uday Kumar Javangula: One English document to multiple set of destination locations, whether it is Japanese, French, etcetera.
Bhupendra Modi: So how do you translate?
Bhupendra Modi: Do you use Google Translate APIs or some particular APIs or?
Uday Kumar Javangula: We have integrations, one is AWS Google.
Uday Kumar Javangula: Deple inline bridge.
Bhupendra Modi: OK, OK.
Uday Kumar Javangula: So these are the four different sources services we are using which are integrated. So based on the customer need we can enable them and we can also choose specific channel if you want to translate 1 channel to using AWS. If you feel Google translates well you can ***.
Bhupendra Modi: OK.
Uday Kumar Javangula: That specific service to a particular channel, that flexibility is there in the system.
Bhupendra Modi: And you said keyword search in the AI search. So how do you differentiate between keyword and AI searchments if you can give some real time example of AI search just to understand it better.
Uday Kumar Javangula: Yes. So yeah.
Uday Kumar Javangula: So keywords such as mostly matches with your document.
Bhupendra Modi: Keyboard is a standard.
Uday Kumar Javangula: Any kind of feeds keyword is standard, but when it comes to AI, what we do is we evaluate the what is the intent intent of this intent and entity of the user query will extract all the information Internet entity extraction by using our custom a logic a AL.
Bhupendra Modi: Yeah, keyboard is fine.
Uday Kumar Javangula: Right. Then we'll fire a query against the repository.
Uday Kumar Javangula: We'll process all the required data.
Uday Kumar Javangula: Which is matching example. We use the rag approach here on top of AI which related documents and it generates and generated a response from that list of documents or A1 document.
Uday Kumar Javangula: I'll show you that shortly.
Bhupendra Modi: Sure, sure. Sure. Yep.
Uday Kumar Javangula: Different open.
Uday Kumar Javangula: So system supports voice search as well.
Uday Kumar Javangula: You can do without typing.
Uday Kumar Javangula: We can do the voice search so that is a key features of it. We also have a chatbot for AI as well as we built a service Connect feature, a service connect module. That module which covers for using for any customer support portal kind of thing a chat.
Uday Kumar Javangula: Will be chatbot will be available for taking the request from end user.
Uday Kumar Javangula: And it will automatically create a service request into the service Connect module and you can track end to end similar to a Salesforce case management. We also have a service Connect case management.
Uday Kumar Javangula: Then we can track from the creation of the ticket 2 until it is closure what happened to the ticket and notify using custom roles business rules we can configure it.
Uday Kumar Javangula: And system has a pre built as I mentioned we have integration.
Uday Kumar Javangula: So here are the few of the things that I've mentioned here.
Uday Kumar Javangula: There are more, but we can crawl your teams data or Slack a SharePoint or any of your databases.
Uday Kumar Javangula: Even we crawled a single database to analyze the predictive analytics on top of the AI data.
Uday Kumar Javangula: Siebel also can be crawled and database can be crawled here into the system. We make it as a from database.
Uday Kumar Javangula: Table structure to.
Uday Kumar Javangula: Capture specific structure documents and we can fire a query against it and to fetch the information.
Uday Kumar Javangula: It's a plug and play. We can just include the integration point to capture, then the synchronization will happen in the background in real time.
Bhupendra Modi: When you say sales force in a semal data, so are you only talking about the knowledge data or in particular which type of data? When we are saying?
Uday Kumar Javangula: Can crawl.
Bhupendra Modi: From both of these systems.
Uday Kumar Javangula: Yeah, yeah.
Uday Kumar Javangula: So suppose if it is Salesforce, we can crawl not just the knowledge.
Uday Kumar Javangula: Knowledge is just a small part of it, right?
Uday Kumar Javangula: But we can crawl the objects if you're in syncing the sales force, you can choose you want to crawl the cases or opportunities accounts. We can crawl all the data on top of the knowledge base.
Bhupendra Modi: Mm hmm.
Bhupendra Modi: OK, OK.
Uday Kumar Javangula: We do the data mapping Bhupendra here. When we are crawling any Oracle service cloud or something we do the data mapping.
Uday Kumar Javangula: What is a source data mapping to the capture?
Uday Kumar Javangula: Then the synchronization will happen.
Bhupendra Modi: Hello.
Bhupendra Modi: OK.
Uday Kumar Javangula: So capture has a multiple modules, one is authoring module to create the content and authoring module itself have data integrations and everything where we can integrate the existing data to the capture repository and also we can crawl existing data as well as write the native data using content.
Uday Kumar Javangula: Management system and we have capture search which is.
Uday Kumar Javangula: A consumption for end user point of view to search against all the data that has been crawled into the system.
Uday Kumar Javangula: Followed by we have administration to monitor and maintain the entire platform and the visibility point of view, roles and responsibilities. And we have a capsule administration which is user management and capture dictionary to fine tune the search. If users are complaining that they're not able to get the.
Uday Kumar Javangula: Proper results for their keywords or search terms we can find in the results by using a dictionary.
Uday Kumar Javangula: We're calling it as a captured dictionary platform.
Uday Kumar Javangula: These are the four Plat 4 modules we have on top of it.
Uday Kumar Javangula: Service connect is a is a specific to customer support module.
Uday Kumar Javangula: So if you look into each authoring, it has a multi that captures a multiple use cases. It can be opted for oh, not just in specific industry.
Uday Kumar Javangula: So it wherever the knowledge is there where users are can search on top of the knowledge or gain the knowledge without losing that information. In organization capture suits that are use case whether it is manufacturing or retail supply chain, whatever.
Uday Kumar Javangula: It is so there are multiple features in ordering like we can maintain all the versioning.
Uday Kumar Javangula: I'll show you that. And we maintaining platform.
Uday Kumar Javangula: So this platform is not specific to.
Uday Kumar Javangula: AWS or something? It's a platform agnostic.
Uday Kumar Javangula: You can deploy this artifacts into OCI, Oracle OCI or GCP of Google Azure, Microsoft and AWS as well.
Uday Kumar Javangula: And we can gain the analytics on top of it.
Uday Kumar Javangula: How the users are being interacted with it?
Uday Kumar Javangula: Search. As I mentioned, there are multiple search, so I'll I'll take a pause here. Then I can go back and show the demo if any questions.
Uday Kumar Javangula: K.
Uday Kumar Javangula: No questions at all. Let me.
Uday Kumar Javangula: So basically I'll show the search 1st.
Uday Kumar Javangula: So this is a search platform where end user search can happen here based on.
Uday Kumar Javangula: Using this search, as I mentioned, all the keyword search and everything can be.
Uday Kumar Javangula: From here like.
Uday Kumar Javangula: Kind of. It provides the auto prompts whatever data that we have in the system. So where it can crawl.
Uday Kumar Javangula: As I mentioned, we can crawl multiple sources, right?
Uday Kumar Javangula: We need to fetch all the information from different data sources.
Uday Kumar Javangula: So here you can see the data is being retrieved here as a search results.
Uday Kumar Javangula: This data belong to multiple data sources, either from A/C bill or from a web or from a Salesforce, or if I do any specific term which has more data from different data lake.
Uday Kumar Javangula: So you can see that data is being.
Uday Kumar Javangula: The system is available in multiple locations right from data import forums.
Uday Kumar Javangula: Google Drive captures a native thing slack and Web and other things, teams and all.
Bhupendra Modi: So like the first one is the Salesforce right this cloud?
Uday Kumar Javangula: Yes, this is OneDrive. This is OneDrive, this is OneDrive.
Bhupendra Modi: 01 right one right, OK.
Uday Kumar Javangula: So we do have sales force as well.
Uday Kumar Javangula: I'll show you that.
Uday Kumar Javangula: This is a Salesforce data so we are providing extensive search mechanism here based on specific source data you want to use, you can just use a call type, sales force or any kind of thing so that we get the same data available.
Bhupendra Modi: So.
Bhupendra Modi: So so when I see content is SF case it is the service request and maybe Vistara bridges the service request name right?
Uday Kumar Javangula: Right, right, right.
Bhupendra Modi: And when I click on hyperlink will I go to the service request or?
Uday Kumar Javangula: Pace.
Uday Kumar Javangula: Actually we redirect to the sales force instance if you already logged in.
Bhupendra Modi: OK.
Bhupendra Modi: Yeah. Perfect. Perfect.
Bhupendra Modi: Wherever, whatever is the record you are opening it OK.
Uday Kumar Javangula: Yeah.
Uday Kumar Javangula: So it gives the experience that we're not losing the data. So experience we're not losing as soon as we open it. If you already logged in, it will navigate to that case easily.
Uday Kumar Javangula: We can see the data more.
Bhupendra Modi: So my point is, you know all these Oracle and Salesforce application they themselves give keyword search pretty well.
Bhupendra Modi: So so if I have to search any case or any particular object in the system.
Bhupendra Modi: They their keyword search is already being given, but with AI search can we think about some use cases where it can give more value?
Uday Kumar Javangula: Right, correct.
Uday Kumar Javangula: The weekend, yes.
Bhupendra Modi: That's what I am thinking.
Uday Kumar Javangula: Yeah.
Uday Kumar Javangula: This is a keyword, right?
Uday Kumar Javangula: Suppose if you get this information about VPN and all right, since we indexed all this data.
Uday Kumar Javangula: SF, KS or other objects, right? Objects data is already there in the system. Apart from this keyword search we also have the.
Uday Kumar Javangula: Chatbot here where I'll show you here how it works, yeah.
Bhupendra Modi: So can can you do one thing?
Bhupendra Modi: Can you search VPN learning in the Salesforce? Yeah, yeah.
Bhupendra Modi: Same OK in the Salesforce data or OK.
Bhupendra Modi: Let's do the directory.
Bhupendra Modi: P and learning can you search only in within Salesforce?
Uday Kumar Javangula: Yeah, just filter it like we have the filters that was on top of the results, right?
Bhupendra Modi: K.
Uday Kumar Javangula: We got multiple results here, so we have these filters. We can filter it by using definite specific data.
Bhupendra Modi: Wait, wait.
Uday Kumar Javangula: So here this.
Bhupendra Modi: K.
Bhupendra Modi: So it's able to understand probably training and learning is same maybe right?
Bhupendra Modi: OK.
Uday Kumar Javangula: Because we have the dictionary right where it will identify the synonyms pre built synonyms and it does.
Bhupendra Modi: Mm hmm.
Uday Kumar Javangula: Synonym search and stop word.
Bhupendra Modi: So so this is this is searching based on synonyms or it is the AI search.
Uday Kumar Javangula: This is not a search, it's keyword purely, yeah, available.
Bhupendra Modi: Not as it keyword. Plus synonyms are got it.
Uday Kumar Javangula: Yeah, correct. In this search term, whatever we are doing that is specific to keyword and synonym search.
Bhupendra Modi: OK.
Uday Kumar Javangula: So I'll show you that. I mean this is a general thing where we crawl the data and filter it.
Uday Kumar Javangula: We have multiple features here where you can filter the based on the data type or data Lake.
Uday Kumar Javangula: Or post filters.
Uday Kumar Javangula: Also there you can have all the specific categories associated to that articles and filter it so that.
Uday Kumar Javangula: Can gain their specific knowledge from this categories and all.
Uday Kumar Javangula: Show the AA search how it works.
Kiran Rajendran: Say on question. So for this data, how are you are structuring the data in database. So from each source you are putting in a different tables or in same table how you structured the data.
Uday Kumar Javangula: So basically we're not using any database here for storage, but we use no SQL Open search platform where it will we create index of each channel we create we maintain we call it as channel so that integration type and channel association we follow structure capture structure where it.
Kiran Rajendran: Mm hmm.
Kiran Rajendran: OK.
Kiran Rajendran: Mm hmm.
Uday Kumar Javangula: Will convert if it is Salesforce Oracle service request right?
Uday Kumar Javangula: So we convert that into capture or entered structure and index it.
Kiran Rajendran: Yeah, but that indexing is happening within the index index database right within AWS.
Uday Kumar Javangula: Right. No, not in Arab list we are.
Uday Kumar Javangula: We are using open search which is open source file.
Kiran Rajendran: Yeah, that open search is but deployed aware in AWS or just which server it is deployed.
Uday Kumar Javangula: Deployment is in AWS current.
Kiran Rajendran: OK.
Kiran Rajendran: Yeah, that's my question. So basically.
Kiran Rajendran: Any source of data we are pulling the data from the open search database, right?
Kiran Rajendran: So the open search database, you already have a mechanism to pull the content from all these sources and convert into the indexed data, right?
Uday Kumar Javangula: When we sync the data so it will index the data to the repository so it is available for end user search. Because in between we have a multiple layers right to preprocess the data OK convert that data to specific index on top of it.
Kiran Rajendran: So that's.
Uday Kumar Javangula: This synonyms are stop words.
Uday Kumar Javangula: Other improvements will be there when the keywords such as that.
Kiran Rajendran: Makes sense?
Kiran Rajendran: Makes sense, OK.
Kiran Rajendran: So one more question.
Kiran Rajendran: This sync is happening from the capture or from each data sources.
Kiran Rajendran: What is the direction of the sync?
Uday Kumar Javangula: Capture, capture, capture will listen to the changes.
Uday Kumar Javangula: Assume that you sync the Salesforce account or Oracle service cloud account, right?
Kiran Rajendran: Huh.
Uday Kumar Javangula: So it checks for periodically. We have a badge of configuration. Periodically checks the latest data and the delta will be synced to the capture or any modifications are there even if any new modifications are there on a service request, data from Oracle service Cloud.
Kiran Rajendran: OK.
Uday Kumar Javangula: It it captures that.
Kiran Rajendran: Makes sense?
Kiran Rajendran: Got it. Cool. Thank you.
Bhupendra Modi: And it does the polling via the web service, right?
Uday Kumar Javangula: Right, it's an API mention, right?
Bhupendra Modi: API best.
Uday Kumar Javangula: It's an API web services where we can.
Uday Kumar Javangula: Jobs will trigger that AP as and syncs the data open.
Bhupendra Modi: Mm hmm. OK.
Uday Kumar Javangula: So.
Bhupendra Modi: But then it might be a problem if data is used, but for most of the companies it will work.
Bhupendra Modi: So yeah, should be OK.
Uday Kumar Javangula: Yeah, mostly what we do is we'll not grab entire data to the capture.
Uday Kumar Javangula: So there is a these bad jobs will work in a specific interval of time and also in a chunks wise right?
Uday Kumar Javangula: So we need to maintain all the scalability of the platform, right?
Uday Kumar Javangula: Not to utilize consume the more memory and all it does in a chunk space, so it gets the data properly.
Uday Kumar Javangula: So this is a keyword point of view and users can provide all the feedback and all we collect the feedback here whether it is from.
Uday Kumar Javangula: The user can provide the feedback over the search if they are not happy with the search results. What we do is we capture this feedback and also we improve the feedback by creating a specific tasks to the admin or author of this document. If it is a search.
Uday Kumar Javangula: It will notify the admin user if it is specific to one specific document. Then we'll notify that mm hmm.
Uday Kumar Javangula: There will be other for this original order for this document, right?
Uday Kumar Javangula: So we notify them with this below rating and what are the comments provided so that it creates an actionable feedback for the author to improve the content of this document.
Uday Kumar Javangula: And specific to aerial focus on the first one, that content creation, then I'll come back to the search point of view as well do that.
Uday Kumar Javangula: Logging into the authoring module where I'll showcase how the knowledge has been created.
Uday Kumar Javangula: Using AI as well.
Uday Kumar Javangula: It's a knowledge authoring platform where we can configure all the channels and all metadata workflows.
Uday Kumar Javangula: How how does this authoring will happen?
Uday Kumar Javangula: General process. It will happen on top of it. As I mentioned, integrations are there so we can sync multiple data sources into the capture example. If it is a slack data you want to sync so.
Uday Kumar Javangula: I've already synced the other workspaces.
Uday Kumar Javangula: Here we can sync that workspace by going the URL and because this will be provided by your Slack account, right?
Uday Kumar Javangula: The generator token, and we're also providing the visibility so that data that you're syncing should be securely available.
Uday Kumar Javangula: So user groups is a concept where we control the visibility of the data and custom tax.
Uday Kumar Javangula: Just a second.
Uday Kumar Javangula: I assume that I've just logged into one of the apps slack.
Uday Kumar Javangula: Any content that we can create specific to?
Uday Kumar Javangula: Take this content.
Uday Kumar Javangula: This channel is already synced, right?
Uday Kumar Javangula: I'm just posting a message here with this.
Uday Kumar Javangula: In this channel. So if I go back here, system will auto sync but still if you want to do it quickly then you can just refresh.
Uday Kumar Javangula: So it does synchronization of the latest data that has been added into the slack through the integration point and it will index. We can search this using capture search as well.
Uday Kumar Javangula: Same way we can sync multiple data sources. Our data can be crawled here either Salesforce or teams.
Uday Kumar Javangula: Let me go back to search.
Uday Kumar Javangula: See this this I just sync that.
Uday Kumar Javangula: Oracle features whatever that I've just entered in Slack account here.
Uday Kumar Javangula: So data sync will be in real time here.
Kiran Rajendran: Uday on question.
Uday Kumar Javangula: Yeah.
Kiran Rajendran: So in this same result, we saw multiple sources only slack, Salesforce and other on drive and all right assume customer is having a conversation with a agent on the same topic through Slack as well as there was an already a Salesforce ticket or service code ticket creator or.
Kiran Rajendran: Ticket created in the system, right?
Kiran Rajendran: So when a customer do a search.
Kiran Rajendran: Are they going to see two different records or single record?
Kiran Rajendran: To do the topic and customer say.
Uday Kumar Javangula: It depends on.
Uday Kumar Javangula: Yeah, it depends on our configuration.
Uday Kumar Javangula: Basically, if you define the user, group and visibility as a slack, right?
Uday Kumar Javangula: Based on the crawl type then it will recommend the data that is related to Slack itself.
Kiran Rajendran: Mm hmm.
Kiran Rajendran: OK, but my question more like can we combine all those contact into a single container show here?
Kiran Rajendran: If the topic is topic, customer everything is same.
Uday Kumar Javangula: Sleeping.
Uday Kumar Javangula: Yeah, we can recommend that.
Uday Kumar Javangula: I'll show you that how it works.
Uday Kumar Javangula: Suppose this is a keyword search.
Uday Kumar Javangula: I'm just showcasing it. If you do the chatbot search, then we'll recommend all the articles related to that specific Slack topic.
Uday Kumar Javangula: We'll recommend those articles.
Uday Kumar Javangula: They can see if it is useful or not on the recommendations pointer.
Kiran Rajendran: OK.
Uday Kumar Javangula: That's a real times sync can happen for multiple same way I'll I'm not showcasing other things because same way it will work for all the teams and all. We can also sync anyone of our teams recordings right? So this is our teams recordings where we can sync all.
Uday Kumar Javangula: The recordings and transcripts here by providing a just a meeting ID, assume that we are in a meeting. If we record this meeting right.
Uday Kumar Javangula: There will be a meeting ID generated over the outlook right outlook or team's.
Uday Kumar Javangula: To provide the multiple IDs and sync the data to capture on top of it using AI by giving a specific meeting ID, you can ask different questions on top of this meeting ID to get the insights of this meeting.
Uday Kumar Javangula: I'll show you that.
Kiran Rajendran: OK.
Uday Kumar Javangula: So that's.
Uday Kumar Javangula: Integration point of view let me showcase.
Uday Kumar Javangula: Yeah, creation as well.
Uday Kumar Javangula: I'll take this channel, which is a patches order.
Uday Kumar Javangula: So here we can generally content authoring will happen by different authors right where they can write the title and create the content by using editor. What we did is it's not, at least by using this AI feature, right?
Uday Kumar Javangula: We should make author life easier if they provide any specific title here like.
Uday Kumar Javangula: So if you provide this specific any term and we have this create with generative AI, so it will prompt like if you click on this initial draft it can be created using AI. So if you if you are familiar with ChatGPT prompting and all you can give.
Uday Kumar Javangula: The prompt how you want this specific data or content to be generated by giving a prompt you can generate it or if you select no we can choose different options whether.
Uday Kumar Javangula: It is from general or internal audit.
Uday Kumar Javangula: General in the sense it takes from the web, right?
Uday Kumar Javangula: You can create the content from web by collecting to the agenda to a or if you already. Since the capture is already having a knowledge right, so we can choose internal knowledge by providing different channels from which channel you want to generate it from charge channel or E.
Uday Kumar Javangula: FAQ or whatever, or all right. So assume that if I choose a web and we can choose.
Uday Kumar Javangula: How the text should should be rendered right?
Uday Kumar Javangula: Whether it is in professional form format or standard or classical.
Uday Kumar Javangula: So when we do this generative AI, so our AI engine will process the term that is being provided with the title and gives them specific draft version of the data here automatically.
Uday Kumar Javangula: This is one of the generative AI.
Uday Kumar Javangula: For authoring point of view.
Uday Kumar Javangula: And on top of it, they can extend more.
Uday Kumar Javangula: The author can extend it more by adding the other data by referring to it.
Uday Kumar Javangula: One more feature is if you believe this sentence is not framed.
Uday Kumar Javangula: Well, right again.
Uday Kumar Javangula: We can rephrase it by using generative AI and choose a style whether it is standard style, rephrase it so it will generate a new paragraph on top of it and updates in real time into this field text field.
Uday Kumar Javangula: Just extended in a standard format since I've selected standard.
Kiran Rajendran: Yeah, this is really a good one, but we source you are referring for this generation same data source with the OR indexed.
Uday Kumar Javangula: OK.
Uday Kumar Javangula: I'm selecting currently web but index stores it can be selected by using internal knowledge and select the channel.
Uday Kumar Javangula: It will generate the same way.
Kiran Rajendran: OK.
Uday Kumar Javangula: Yeah, we are providing the flexibility, yeah.
Kiran Rajendran: So we need to verb. We need to verb where it is looking the data, that means Google.
Uday Kumar Javangula: It looks out the open AI no open AI charge.
Uday Kumar Javangula: Yept Open AA platform which is.
Kiran Rajendran: Open edge platform. Open. OK, OK. OK. So for is that a license or free?
Uday Kumar Javangula: No, it's a paid 1.
Uday Kumar Javangula: Paid. We have a paid account. So on top of we did this.
Kiran Rajendran: Book.
Kiran Rajendran: Code OK.
Kiran Rajendran: OK, makes sense.
Kiran Rajendran: So which means if customer wanted to use it, they need to have that license whatever the A platform they use, right?
Uday Kumar Javangula: Actually we did.
Uday Kumar Javangula: The.
Kiran Rajendran: OK.
Uday Kumar Javangula: If they have any existing AI models.
Uday Kumar Javangula: So capture is flexible to point to that AI AP is or we can fetch that same way if they have any custom model, they can do that.
Kiran Rajendran: Mm hmm.
Kiran Rajendran: OK. Because we have this Oracle OCI, you know license, right?
Kiran Rajendran: Oracle already provides stuff, so I think it's part of OCA licensing, which we already have.
Uday Kumar Javangula: 5%.
Kiran Rajendran: We can utilize that as well for web.
Uday Kumar Javangula: We can utilize that web in the sense it it points to the specific area platform, whether it is PT or Google.
Kiran Rajendran: Yep.
Kiran Rajendran: Left. OK.
Uday Kumar Javangula: So there's that is an on authoring general authoring works, but knowledge creation is with generative AI feature which we have here.
Uday Kumar Javangula: So now let me go back to the search.
Uday Kumar Javangula: How chatbot search will happen here again?
Uday Kumar Javangula: Here also we are providing the multiple options.
Uday Kumar Javangula: Here we can choose style off it how we want to generate it so we can file a query here in NLP question general question you ask right on top of a knowledge.
Uday Kumar Javangula: This capture is flexible of multiple integrations, right?
Uday Kumar Javangula: So you can get the answer from different sources.
Uday Kumar Javangula: Either it is from internal knowledge or external knowledge.
Uday Kumar Javangula: So that is why we have different things.
Uday Kumar Javangula: Which data source you want to point to capture with GPT 40 or you want to search on top of it?
Uday Kumar Javangula: Any C build data so we can search on top of it.
Uday Kumar Javangula: Or GPT with different style.
Uday Kumar Javangula: Let me ask a question specific to knowledge which is already having internally indexed the content.
Uday Kumar Javangula: Maybe I'll. I'm going to ask questions specific to.
Uday Kumar Javangula: I I did index to some couple of Mazda data into it.
Uday Kumar Javangula: If you ask any step by step process right so system will understand that Internet entity and based on the style that you selected will give you the ChatGPT experience here.
Uday Kumar Javangula: But it will be from your existing knowledge which you are already indexed.
Uday Kumar Javangula: Now based on the search term, it files a query and identifies all the Internet entities by using the rag approach of Al. It is giving the new content generation from the source link of this document.
Uday Kumar Javangula: Creates a generative AI content on top of the indexed content of your native documents.
Uday Kumar Javangula: It will.
Uday Kumar Javangula: You can ask multiple questions.
Uday Kumar Javangula: Either you need step by step procedure or different repair process of a specific manufacturer or a car. Any kind of thing you can ask the prompt then capture search will provide you that experience here.
Uday Kumar Javangula: Related to specific videos or images or there specific to these search terms, if any matching will get that.
Uday Kumar Javangula: Videos as well so user can play through it or access that other datas that is available images or videos.
Uday Kumar Javangula: So we recommend those information here.
Uday Kumar Javangula: We provide the source link so that it gives a clear picture that this data is being generated.
Uday Kumar Javangula: Which is matching with the search terms from this document.
Uday Kumar Javangula: It can be A-150 page document or hundred page document, right?
Uday Kumar Javangula: It has a huge but it it will create generic format on Agenda 2 AI and gives you points 123 step 123.
Uday Kumar Javangula: So this is a search experience on AI.
Uday Kumar Javangula: Any questions I can I have Abhijit also in this call so.
Uday Kumar Javangula: Need any clarifications or questions?
Uday Kumar Javangula: You can start.
Kiran Rajendran: So regarding this image and video, right?
Kiran Rajendran: So that data also embedded into the index database, right, Abhijit? Yeah.
Uday Kumar Javangula: Index database, yeah.
Kiran Rajendran: OK.
Kiran Rajendran: But while indexing how AA is trying to make sure this particular content is matching with this image.
Kiran Rajendran: How there it is doing today?
Uday Kumar Javangula: So currently image we have internal OCR as well right?
Uday Kumar Javangula: If these search terms we does the OCR on top of the image. OK, it stores the data, all the metadata, right?
Uday Kumar Javangula: If it can it, it creates a multidate.
Kiran Rajendran: Oh, wait, OK, OK.
Kiran Rajendran: So there's a mapping metadata mapping OK, OK already there.
Uday Kumar Javangula: Yeah, mapping will be there.
Kiran Rajendran: And when we retrieve this data with respect to image and text content, do you see any performance difference?
Uday Kumar Javangula: So basically what we do is this data is stored in a file repository. OK only the metadata will be available in index Kiran so that it will not create much performance. So that it is flexible of any data we can load because this has a storage from file.
Kiran Rajendran: Mm hmm.
Kiran Rajendran: Infin flow. OK, OK.
Uday Kumar Javangula: Server but metadata is in index.
Kiran Rajendran: Oh so.
Kiran Rajendran: OK, makes sense. So the content is giving the URL and you are using the URL to fix the image or video from the file server.
Kiran Rajendran: OK. But I think Abhijit, we can even embed this image on video as well in the in the index database, right?
Kiran Rajendran: OK. But that that we have, we are not using or we are planning to use in future.
Abhijit Thakare: Basically, these videos we can embed into the database, index it into the database and using OCR technique we are performing the.
Abhijit Thakare: Text search and the new content is generated. Whatever we can see. If you can see the contents are totally new, the answer which is coming.
Abhijit Thakare: Which is totally new.
Abhijit Thakare: It is not the exact words copied from the document in the source link.
Kiran Rajendran: OK, OK.
Abhijit Thakare: So yeah.
Abhijit Thakare: Yeah. So the The thing is will work that first of our LP algorithm will extract the relevant document based on the semantic meaning of the question, not only semantic meaning, syntactic meaning and semantic meaning.
Kiran Rajendran: Got it.
Abhijit Thakare: Both and then then this is performed by an NLP algorithm and then based on that relevant document.
Kiran Rajendran: Mm hmm.
Abhijit Thakare: Gen.
Abhijit Thakare: AI will work on that relevant document.
Abhijit Thakare: It can be one document or more than one document also.
Abhijit Thakare: And based on that document, the new generated contents are generated, yeah.
Kiran Rajendran: Code it.
Kiran Rajendran: My question is more specific to showing this image or video on this UI as a response right? So in order to show this image or video on the UI like this.
Abhijit Thakare: Yeah.
Kiran Rajendran: Always we prefer to use as file server or without file server. We can manage this.
Uday Kumar Javangula: As of now it is with file server so that to to to reduce the performance right Karen? So it will be difficult. Suppose if you take image we need to convert into base 64 and index we can index it.
Kiran Rajendran: OK.
Kiran Rajendran: Performance issues.
Abhijit Thakare: Yes.
Kiran Rajendran: Hmm.
Uday Kumar Javangula: But again retrieval it takes some time.
Kiran Rajendran: Right, correct.
Kiran Rajendran: Yep, OK.
Uday Kumar Javangula: So instead file server is the best approach.
Kiran Rajendran: OK, makes sense.
Abhijit Thakare: Yeah.
Uday Kumar Javangula: The same way we can ask a question in different local as well.
Uday Kumar Javangula: Like same question or a different question by logging into.
Uday Kumar Javangula: Different local right now it is with English, right?
Uday Kumar Javangula: So let me I'll just show you that as well. So that the experience can be.
Uday Kumar Javangula: Experience with a different locale, how it looks like since the system supports multiple languages, right?
Uday Kumar Javangula: So based on the user login language, the system will convert all the labels into that specific locale. I've logged into Japanese profile, can ask a query.
Uday Kumar Javangula: So then it will bring the specific document later to that Japanese search term and renders the data. As Abhijit mentioned, right, the generated and content can be either from 1 document or multiple documents.
Uday Kumar Javangula: So in this use case, so it is fetching from two different documents related to this query.
Uday Kumar Javangula: Which makes end user life easier to get the required data based on the search term they provide.
Uday Kumar Javangula: I just ask this one.
Uday Kumar Javangula: Show the question.
Kiran Rajendran: So he's just converting the whole day English content or the Japanese index content.
Uday Kumar Javangula: Say anything.
Kiran Rajendran: Or just directly picking.
Uday Kumar Javangula: Japanese Japanese content, which is already there here.
Kiran Rajendran: OK.
Kiran Rajendran: So we uploaded already a Japanese content into the index. That is OK.
Uday Kumar Javangula: Yes, yes.
Uday Kumar Javangula: Do the translation English local English document to multiple local translation right? So from the document it is created here so it is fetching from the.
Kiran Rajendran: OK.
Abhijit Thakare: Based on the query, whatever you fire first, it will identify the local, whether whether it is English or Japanese, and then based on our NLP algorithm, the second step is our NLP algorithm will come into the picture will which will find out the intent.
Abhijit Thakare: And semantic meaning of that question and based on that it will find out the relevant documents and then it will do some chunking strategies. Rag approach and generate the new content based on the.
Abhijit Thakare: Relevant documents.
Kiran Rajendran: OK.
Kiran Rajendran: Got it.
Uday Kumar Javangula: So that's experience from search and also we did.
Uday Kumar Javangula: Multiple R&D on a single data like to predict the data like we next the airline data of sample American Airlines data where we can find it through chatbot to get the insights of it whether they can cancel the flight or what are the incentives we can.
Uday Kumar Javangula: Improve for the customer. So we did that use case as well for usable data.
Uday Kumar Javangula: I'll. I'll take care.
Uday Kumar Javangula: I'll take back to the service connect quickly, but maybe we can do another call if you are running late.
Uday Kumar Javangula: So this is a service connect quickly I'll showcase as I mentioned right service connect is to allow the to handle any kind of customer issues.
Uday Kumar Javangula: So it takes all the hotline service request and we can configure. We can also provide the complete analytics of data which is being available in the system, including the number of tickets that are being created.
Uday Kumar Javangula: It gives a complete picture.
Kiran Rajendran: The logo is doing.
Uday Kumar Javangula: Use all the information from where we got the tickets and all with the complete analytics.
Uday Kumar Javangula: And service request can be tracked by using different types.
Uday Kumar Javangula: We customize it for one of the other use case so that you can see multiple types of.
Uday Kumar Javangula: Service request.
Uday Kumar Javangula: Where we can capture all the information here life cycle of the ticket and also it contains the knowledge integration.
Uday Kumar Javangula: Kiran, you were asking, right?
Uday Kumar Javangula: How if agent is talking to a customer, how you can support by using a channel, right?
Uday Kumar Javangula: So we did this knowledge integration, since this service contains a separate module, but it has integrated with the knowledge if any specific search term is available, right?
Uday Kumar Javangula: Based on the customer concern query, we pass that query along with.
Uday Kumar Javangula: Here it is a use case of Mazda.
Uday Kumar Javangula: Vehicle and model year so it will convert model air automatically and files a query against the knowledge. So it gives a relative information to it based on the search term. They can link this data.
Uday Kumar Javangula: And they can also refer to the document and answer the customer query easily while they're in chat communication.
Uday Kumar Javangula: The same interaction can be available through chat as well.
Uday Kumar Javangula: We also have a chat feature where end user can connect to the chat by giving a request, right?
Uday Kumar Javangula: Then we dynamically create the hotline service request or service request dynamically based on the inputs provided by the user.
Uday Kumar Javangula: Here.
Uday Kumar Javangula: Chatbot for service request will be look like this.
Uday Kumar Javangula: Let me open it.
Uday Kumar Javangula: So this is a general generic chatbot where you can raise a request.
Uday Kumar Javangula: So it has a connection with your service connect.
Uday Kumar Javangula: Here we integrated that with the search portal. So without login general customer can raise a request by giving all the information first name, last name and all.
Uday Kumar Javangula: So we'll provide all the information we need to understand right how what is a issue they're looking for.
Uday Kumar Javangula: So we provide all the categories here. This is specific to automobile data.
Uday Kumar Javangula: See if they have any issues specific to thermal event, right?
Uday Kumar Javangula: We recommend the subcategories from break or from other thing. They can choose it.
Kiran Rajendran: So for this category generation, are we using AI?
Uday Kumar Javangula: So this is from API itself, not AI, because this category is already there in system right service connect system. If you look at the service connect data.
Kiran Rajendran: Yeah, but assume if we don't have that category available in the system with the content using a, we can generate the categories automatically and display it to the right.
Uday Kumar Javangula: We can generate, yes, we can generate that capable is capable to generate.
Kiran Rajendran: OK.
Abhijit Thakare: Yeah.
Kiran Rajendran: OK.
Kiran Rajendran: Got it.
Uday Kumar Javangula: Will recommend if any already resolve tickets are there right? So they can refer to this tickets if it is matching. Also will provide how much percentage matching it is giving.
Uday Kumar Javangula: We give the order chronological order highest priority as 75 or 100% matching it.
Uday Kumar Javangula: If they are happy, they can provide that feedback or if they are not happy then they can select no.
Uday Kumar Javangula: What it does will ask For more information because we need.
Uday Kumar Javangula: More clarity on it, how to provide the support right, so some more information will request for end users.
Uday Kumar Javangula: Whether it is a win example will provide this win here.
Uday Kumar Javangula: They just this. What is the concern?
Uday Kumar Javangula: So.
Bhupendra Modi: If we go to, say, the full sentence right, share your VIN number or what is the mileage means?
Uday Kumar Javangula: Yeah. Yeah, we can.
Bhupendra Modi: I know it's a small thing, but because it's from the demo perspective. But yeah, we understand what you are doing, yeah.
Uday Kumar Javangula: Yeah, yeah, we can do that.
Kiran Rajendran: Yeah, it'll be more.
Uday Kumar Javangula: Yes. Yeah.
Uday Kumar Javangula: So just once it is available, right?
Uday Kumar Javangula: So then it will come connect to this chat immediately online with this user.
Uday Kumar Javangula: See, it has created one new ticket here and notifies us earlier it is 160 so a new ticket is created and it connects to the. We got a notification here so.
Uday Kumar Javangula: From this notification we can see.
Uday Kumar Javangula: You can join to this ticket for the customer.
Uday Kumar Javangula: Device so we can chat back and forth to get all the information right. And since it is a service request point of view, anytime we need to navigate to the knowledge or case details more information, right?
Uday Kumar Javangula: So we can.
Uday Kumar Javangula: Navigate to that details or we can download or we can add resolution or sync the data or upload the attachments back and forth.
Uday Kumar Javangula: So it's a kind of customers support portal we built which helps for a customer experience and resolving their issues.
Uday Kumar Javangula: But they can go back to the Sr. service request and more information based on that, right?
Uday Kumar Javangula: Since the user has already provided, we create all the provided information dynamic case.
Uday Kumar Javangula: They can refer to the knowledge if they get any information here thermally event or anything generated stop.
Uday Kumar Javangula: Refer to that and provide the help to the end user.
Uday Kumar Javangula: Also add attachments by using attachments or create the task and events can log the efforts to the audit trail of how the ticket is created on all the instant information.
Uday Kumar Javangula: But there are more.
Bhupendra Modi: Service request in the chat are there also some open source framework or it's we have built?
Uday Kumar Javangula: Which we have built right?
Bhupendra Modi: From scratch, both of them open.
Uday Kumar Javangula: It's it's we have built.
Uday Kumar Javangula: Yeah, it is with Vue JS component.
Bhupendra Modi: OK.
Uday Kumar Javangula: Complete open, open source technologies.
Uday Kumar Javangula: We use bhupendra's very limited.
Bhupendra Modi: Yeah, but it's ********, right? Yeah.
Uday Kumar Javangula: Correct. It's our code where we can communicate this chat and all. So share that.
Bhupendra Modi: And can we also or did we do anything with the social media like?
Bhupendra Modi: Reading the social media thing and then understanding what people are saying and maybe are they complain or something like that because you are. You said a lot of things you are able to index, right?
Bhupendra Modi: Are you able to get few things from the social media as well?
Uday Kumar Javangula: We didn't write because yeah, we didn't write any social media, but web crawling we did.
Bhupendra Modi: Or anything in the social media.
Uday Kumar Javangula: Suppose if any organization having any micro sites right?
Uday Kumar Javangula: Usually organized micro sites so we can crawl the micro site and on top of it we can make that index available for finding the insights from the micro site govindra.
Bhupendra Modi: Mm hmm.
Bhupendra Modi: OK.
Uday Kumar Javangula: There are more features, but I think maybe as I mentioned it, I there are multiple features on top. That's right, yeah.
Bhupendra Modi: Can you show the E signature?
Bhupendra Modi: What you have done?
Uday Kumar Javangula: The signature is more of like showing that.
Uday Kumar Javangula: From the article point of view and the article is created right?
Uday Kumar Javangula: So if you enable this, it's again a custom canvas. We built it custom component, OK we can enable at the specific channel level.
Uday Kumar Javangula: So at the channel level.
Uday Kumar Javangula: Type.
Uday Kumar Javangula: So here we can enable that resignation component.
Uday Kumar Javangula: With this component so we can select.
Uday Kumar Javangula: For this channel, at what workflow step you want to get that signature from?
Uday Kumar Javangula: Author or reviewer, right? Since we have a workflow so you can choose at published level, you need to get it or you can give the default signature automatically which takes for all the channels.
Uday Kumar Javangula: Or if the author or publisher wanted to give it, they can also give while approving this step.
Uday Kumar Javangula: Bhupendra. Second, customized you can customize as per your need.
Bhupendra Modi: Hmm.
Uday Kumar Javangula: I can do canvas.
Bhupendra Modi: OK. And and for the E signature there is I think we say HTML canvas, right, that's what you have used or it's something very different?
Uday Kumar Javangula: So it's a canvas only it through view JS we built the component yeah and they can do all the type or they can upload the image their own images they can upload.
Bhupendra Modi: It's.
Bhupendra Modi: It was only right. OK. OK.
Bhupendra Modi: Yeah, English is good option.
Uday Kumar Javangula: So.
Uday Kumar Javangula: Three options with the different fonts and all which gives experience.
Uday Kumar Javangula: The signature.
Bhupendra Modi: So can people save their signature and will it come automatically or every time they have to sign?
Uday Kumar Javangula: They can save it.
Uday Kumar Javangula: They can save it and for the next approvals, automate it will populate.
Uday Kumar Javangula: We can also ask for a user whether you want to overwrite or not, yeah.
Bhupendra Modi: OK, perfect.
Bhupendra Modi: Perfect. Perfect. OK.
Uday Kumar Javangula: You can configure this at the channel level and while approving the step right workflow level.
Uday Kumar Javangula: Then it will recommend add the signature on this step.
Uday Kumar Javangula: You can enable disable.
Uday Kumar Javangula: It's it's up to us, right?
Uday Kumar Javangula: Configuration like this.
Uday Kumar Javangula: So any questions I can take that up and?
Uday Kumar Javangula: Probably I think we extended this.
Bhupendra Modi: Do you also create any document or any contract or anything or?
Uday Kumar Javangula: Which one?
Bhupendra Modi: We are not creating any contract sort of thing, right?
Bhupendra Modi: Just on the knowledge side, we are giving the functionality for now right, not inside any document.
Uday Kumar Javangula: And only for the knowledge, yeah, knowledge article standpoint of you, bhupendra to make sure that this article is authorized by so and so user so that it gives workflow level steps also for that article the signature.
Bhupendra Modi: Good morning.
Uday Kumar Javangula: Gives like this, drafted by who and their signature, right?
Uday Kumar Javangula: Reviewed by.
Bhupendra Modi: OK, go.
Uday Kumar Javangula: We also have this bulk local translation feature.
Uday Kumar Javangula: I was talking right?
Uday Kumar Javangula: So we can translate this look, one particular local two different.
Uday Kumar Javangula: Locals from English to and choose the engine.
Uday Kumar Javangula: Amazon translator.
Uday Kumar Javangula: Too specific to Japanese or French.
Bhupendra Modi: And you also do the sentiment analysis also, right?
Uday Kumar Javangula: Yes, personalized notification and also can be doable.
Bhupendra Modi: OK.
Uday Kumar Javangula: We can do auto publish without reviewing. If you want to do auto publish, it will create a document automatically or you want to make it as a draft. Let let the other user with that local can review.
Uday Kumar Javangula: They can review it and request so it will automatically translate this document to the desired selected locals.
Uday Kumar Javangula: And we can see that in the.
Uday Kumar Javangula: And automatically it will say this DDE and Frfr is published just now.
Uday Kumar Javangula: Jay, is it?
Uday Kumar Javangula: And we can see that in regular time.
Uday Kumar Javangula: So translated locals, right?
Uday Kumar Javangula: So if it is Japanese then document will be populates automatically.
Uday Kumar Javangula: And provide all the information with the translator locals.
Uday Kumar Javangula: We maintain the versions also version in the sense if you look at this article.
Uday Kumar Javangula: Everywhere we need to track everything, right?
Uday Kumar Javangula: Each version will be created here so.
Uday Kumar Javangula: We provide the document history with all the information.
Uday Kumar Javangula: How many versions are being there and we can compare the document version with the changes we available, right?
Uday Kumar Javangula: Whether it is related with the red and green with add-ons so we can revert it or we can delete that version as well.
Bhupendra Modi: Got it.
Uday Kumar Javangula: You can choose multiple versions.
Uday Kumar Javangula: Vice versa.
Uday Kumar Javangula: Show the differences.
Uday Kumar Javangula: There's no difference.
Uday Kumar Javangula: But every time it updates it creates a content.
Uday Kumar Javangula: And recommendations will be there.
Uday Kumar Javangula: Feedback customer feedback would be good.
Uday Kumar Javangula: Feedback, bad feedback, everything.
Uday Kumar Javangula: We track it for the knowledge improvement and such is point of such is mostly on a global search for Oliver knowledge data service connect for our customer experience point of view.
Uday Kumar Javangula: It's open for questions.
Bhupendra Modi: When user feedback, is it more like a survey?
Uday Kumar Javangula: Not just a survey, it is an actionable feedback.
Bhupendra Modi: Or.
Uday Kumar Javangula: We get the we collect the feedback.
Bhupendra Modi: But it got it.
Bhupendra Modi: They can type something or they can say something.
Uday Kumar Javangula: They can type something and we'll recommend that here.
Uday Kumar Javangula: So based on that, we can improve the data bhupendra this article. So we have Inbox feature where it will notify the tasks associated to that user, right.
Bhupendra Modi: OK.
Uday Kumar Javangula: So they need to work example like review, publish or crawling or if there is any feedback related stuff they'll get a notification here.
Uday Kumar Javangula: Of this inbox record, so we'll notify user by using this inbox or including e-mail.
Uday Kumar Javangula: Also, we'll send to the user e-mail.
Uday Kumar Javangula: It's easy for them to track the things.
Uday Kumar Javangula: There's forms for any internals topics you can discuss right internally automation point of view.
Uday Kumar Javangula: You can create the user forms and track it.
Uday Kumar Javangula: Articles also can be exported to apdf.
Uday Kumar Javangula: So PDF configuration again.
Uday Kumar Javangula: It's again, it's open source technology we used to export the PDF of a channel document from K search itself.
Uday Kumar Javangula: That's a just of it, probably.
Uday Kumar Javangula: We can meet again and further questions.
Uday Kumar Javangula: We can take it up.
Bhupendra Modi: You also share this PPT. I think a lot of things are there.
Bhupendra Modi: At least we can review especially these few slides, which has a lot of content.
Uday Kumar Javangula: OK.
Uday Kumar Javangula: I'll share it, I'll share it.
Bhupendra Modi: But then said it gave us a very good understanding and.
Bhupendra Modi: There are quite a few things I think we can do things, but I think we have to think and understand what are the best things to do because.
Uday Kumar Javangula: Right, right. There are multiple features bhupindra. So based on the use case we it's a multi fold use case so it can be applied for any kind of use cases. We can frame it out and do that. OK.
Bhupendra Modi: OK.
Abhijit Thakare: Sorry to sorry to interrupt you bhupendra.
Abhijit Thakare: We have a sentiment analysis.
Abhijit Thakare: We are also done a sentiment analysis on C build data. Like if you have a text of feedback from various customers and it tells about the what is the view of the customer. Regarding that airlines we can perform the sentiment analysis over that feedback.
Abhijit Thakare: And we can find out which are the satisfied customers, which are the not satisfied customers for that particular airlines.
Bhupendra Modi: For sentiment analysis, you get all the case data and or maybe all the chat and even you you take the voice data also it's which type of data you are taking.
Abhijit Thakare: We are taking text data like sentiment analysis, yeah.
Uday Kumar Javangula: Thanks David.
Bhupendra Modi: No, but but. But the cases are you taking only cases or are you taking the chat or even any other channel like the emails or any other thing.
Abhijit Thakare: We can configure it for other things also, but currently whatever the use case we have worked out that that use case is for C build data and we crawled A/C build data their feedbacks. Also we crawled and perform the sentiment analysis over that text data of that feed.
Abhijit Thakare: Yeah.
Bhupendra Modi: OK.
Uday Kumar Javangula: The chatbot I mentioned right Bhupendra. We have multiple data sources here where we can access that type of here.
Uday Kumar Javangula: See below captured.
Uday Kumar Javangula: Then we can grab all that information specific to it.
Bhupendra Modi: Yeah, but see, my point is, once you do a chatting with the customer, you can and and we tell them after the chat that you have all the data and then you can tell, OK, this was the sentiment of the customer means why should we even ask a?
Bhupendra Modi: Survey means we if AI can read it you you can at least give an indication that maybe that was the sentiment.
Uday Kumar Javangula: Yeah, we can.
Uday Kumar Javangula: We can get that correct.
Uday Kumar Javangula: We can grab that. I'll show you.
Bhupendra Modi: And just thinking again again.
Bhupendra Modi: And there's no customer who has asked for it.
Bhupendra Modi: Just curious.
Abhijit Thakare: No, no, basically.
Uday Kumar Javangula: I didn't show you that.
Abhijit Thakare: Movement the the sentiment analysis is done for, not for customers.
Abhijit Thakare: It's for internal use of the airlines like suppose Airlines wants to predict which are the satisfied or not satisfied customers so that it can it can promote some offers to the customers in future. OK.
Abhijit Thakare: So if if it wants to find out which are the which are the unsatisfied customers for that particular service.
Abhijit Thakare: The airlines get.
Abhijit Thakare: The data that these are the XYZ customers and then it can float some offers to that customers in the future.
Abhijit Thakare: So that they can be turned on, yeah.
Bhupendra Modi: So I think that's, we say this journey for marketing terminology, we say, OK, how would you stop the journey?
Abhijit Thakare: Yes.
Bhupendra Modi: But the point is, how are you getting that data right?
Abhijit Thakare: Yes.
Bhupendra Modi: Because customer points can be a lot of places customer can talk.
Bhupendra Modi: Service request is one and.
Abhijit Thakare: Yes, service request.
Abhijit Thakare: Yeah, we are getting through service request, yeah.
Bhupendra Modi: Can be another.
Bhupendra Modi: Phone can be another e-mail, hence emails can be another or maybe some other criteria they can define, but at least done a structured data. How can you get?
Abhijit Thakare: Yes. Yeah.
Bhupendra Modi: That's the question.
Bhupendra Modi: OK, but technically, if we can do it from one, we can do it from all right.
Abhijit Thakare: Yeah.
Abhijit Thakare: Yes, yes, yeah.
Bhupendra Modi: That's how it should be.
Uday Kumar Javangula: Show you that one of the news.
Uday Kumar Javangula: Case copilots.
Uday Kumar Javangula: On the weekend I I mentioned I.
Bhupendra Modi: You know in the marketing, how do they sell this?
Bhupendra Modi: Sort of scenario, they say.
Bhupendra Modi: That getting a new customer is very tough, but retaining a customer is much easier and and they put some numbers. So that's always there and that's when when they start that OK, we'll give you this data and you will stop.
Bhupendra Modi: We'll, you know, reduce your churnning of the customer, it's very.
Abhijit Thakare: Yes, yes.
Bhupendra Modi: Way the sale is very catchy, yes.
Uday Kumar Javangula: So data source can be anything, bhupendra. We can configure it whether it is from e-mail or from the chat.
Bhupendra Modi: Mm hmm.
Uday Kumar Javangula: As of now, we crawl the cable service request data and get the information. Same way we can crawl all the meeting from the teams and get that information.
Bhupendra Modi: Is it possible to OK.
Bhupendra Modi: Maybe I'll also see, but you know, social media crawling is also another good use case which can help us.
Bhupendra Modi: And why?
Bhupendra Modi: I'm saying because almost every company is now very much socially aware, right?
Bhupendra Modi: They want to monitor their social channels and if anything negative is coming, they generally want to create a case or something so that they can notify and someone can respond there.
Abhijit Thakare: Yeah.
Bhupendra Modi: So.
Bhupendra Modi: It's good and even to understand sentiments.
Bhupendra Modi: Our own data is our own data but.
Bhupendra Modi: More than that, you have to understand what is happening in socially for your data, for your company.
Uday Kumar Javangula: Yeah.
Bhupendra Modi: So I think social media is very powerful tool nowadays. If we can troll or we can get some more data, it can be very lucrative user use cases, studies.
Uday Kumar Javangula: That sounds good.
Abhijit Thakare: Yeah.
Abhijit Thakare: OK.
Bhupendra Modi: Yeah, you are showing something, yes.
Uday Kumar Javangula: Yeah. So specific to teams meeting, right?
Uday Kumar Javangula: You were asking about the AI on.
Uday Kumar Javangula: Discussions, right, so from this teams meeting, whatever.
Uday Kumar Javangula: With that, we crawled right. All the transcription is crawled here on top of it.
Uday Kumar Javangula: We can ask the action items or a mode of the meeting.
Uday Kumar Javangula: What is any negative feedback from that meeting?
Uday Kumar Javangula: We can ask the question by passing the just a meeting ID.
Uday Kumar Javangula: Then we'll get all the information here, which we get from a generative AI point of view.
Uday Kumar Javangula: Ben.
Uday Kumar Javangula: So if we look at here, we had a discussion about product quality management in one of the meeting which we recorded right the moment I asked about action items. It grabs all the information what we discussed right and dealer management and other information based on the action items.
Uday Kumar Javangula: It gives a complete clear picture by generating a new content of generative an. From that meeting. Same way we can ask the mood of the meeting and all to get more precise information.
Bhupendra Modi: I think that's very that's very, very good feature.
Bhupendra Modi: It's.
Bhupendra Modi: Creating minutes of meetings is anywhere painful thing for everyone. We are organization or any other organizations.
Uday Kumar Javangula: Very, very painful. So this source correct.
Bhupendra Modi: So yeah, if something is giving you directly.
Uday Kumar Javangula: Moment you record, record and index it. You can ask the question on top of the meeting.
Bhupendra Modi: Should be a very good feature.
Uday Kumar Javangula: You'll get all the insights.
Bhupendra Modi: This meeting you have got from teams is it or chat or teams meeting?
Uday Kumar Javangula: Teams meeting teams.
Uday Kumar Javangula: Teams meeting.
Bhupendra Modi: I think teams will soon give this sort of feature right.
Bhupendra Modi: They are anyway given transcript or do they already have a feature now?
Uday Kumar Javangula: Yeah.
Bhupendra Modi: I'm not sure.
Uday Kumar Javangula: I think not yet.
Uday Kumar Javangula: Not there yet.
Bhupendra Modi: Not yet, right?
Uday Kumar Javangula: Not there in teams yet.
Bhupendra Modi: K But but this can be very good feature.
Uday Kumar Javangula: Internally also it helps.
Bhupendra Modi: If AI can do Mo M or the HN items, I think PMS roles will be really you know it will help them as well as anyone, right?
Bhupendra Modi: It's industry wide, Yep.
Uday Kumar Javangula: Hey anyone?
Uday Kumar Javangula: It's yes.
Bhupendra Modi: I think there's smaller features can be categorized in the separate thing, right, like.
Uday Kumar Javangula: It's correct.
Bhupendra Modi: Getting sentiments.
Uday Kumar Javangula: Correct, it's API.
Bhupendra Modi: Getting attention items or.
Uday Kumar Javangula: It's API driven, right?
Uday Kumar Javangula: So we don't need to go with the entire thing, but specific portion we want to implement.
Uday Kumar Javangula: Using any Oracle product right by passing the data to capture and get the insights. So it is flexible to do that.
Bhupendra Modi: K.
Bhupendra Modi: Have you done anything from the cell side as well?
Bhupendra Modi: No, you are mainly concentrating on service side, right?
Bhupendra Modi: Because it's game solution, yes, yes service side.
Uday Kumar Javangula: It's a knowledge management, right?
Uday Kumar Javangula: So solution is more of not an service side only, yeah.
Bhupendra Modi: Sis.
Uday Kumar Javangula: But any specific use can we can check that how it?
Bhupendra Modi: Well.
Bhupendra Modi: Yeah. So in this article Oracle, we were talking something for the pre sales and they were saying if you have some AI solution that you can build for sales for telecommunication industry, they were talking something.
Uday Kumar Javangula: I think I'll show you that.
Bhupendra Modi: So, so probably, probably we'll discuss it in a separate call.
Bhupendra Modi: I have to 1st discuss with the internally with team and we will connect with you, but they are saying if any good AI feature et cetera you can promote.
Bhupendra Modi: So categorization and few things should be easy, right?
Bhupendra Modi: If we have the plugins or we have the knowledge we can say we can build something like that.
Uday Kumar Javangula: Correct, correct.
Bhupendra Modi: But we have to do our own brainstorming and understand what can be the part.
Uday Kumar Javangula: So we did another portion using this.
Uday Kumar Javangula: Which is a kit and genie where we can get. You are asking about social media, right?
Uday Kumar Javangula: It is one of the area where we can fetch more of information to find out the opportunities in the market, right.
Uday Kumar Javangula: Suppose competitor against the competitor you want to see to reach them. If they have any problem on that specific company, right?
Uday Kumar Javangula: So Katen is one of the document provided in the US for our nation, bhupendra.
Uday Kumar Javangula: They can log all the information.
Uday Kumar Javangula: How is sales any?
Uday Kumar Javangula: What is the? What is the profit?
Uday Kumar Javangula: Net profit losses where we lost what services we are lacking. So they'll give you all the information to government using this katan document.
Uday Kumar Javangula: So we did one kind of RND here.
Uday Kumar Javangula: How do how we can use AI to fetch that identify the opportunities on top of the K10 document?
Uday Kumar Javangula: So this document will be huge 100 pages.
Uday Kumar Javangula: Or more than 50 pages, 80 pages.
Uday Kumar Javangula: But get finding that opportunities in that document opening is very difficult for us, right?
Uday Kumar Javangula: Even finding it.
Uday Kumar Javangula: So we did this kind of.
Bhupendra Modi: Mm hmm mm hmm.
Uday Kumar Javangula: Use case where we can choose that organization.
Uday Kumar Javangula: Example any Mazda or any other robcat or Oracle we indexed all their katan into the repository. We can give the profile as an investor. If you want to see any investment opportunities or any other things we can ask a query against it. Then based on the profile it.
Uday Kumar Javangula: Grab the document from from the document. This profile specific and give the answer.
Uday Kumar Javangula: Marketing guy or a service provider? Same way we can also see the sales point of view as well.
Uday Kumar Javangula: It gives a complete, clear picture without reading the entire document.
Uday Kumar Javangula: So we build this one of the tool called Kit and Genie. Again it's a chatbot where you can get the insights from the kit and document to figure out the opportunities to pinch.
Bhupendra Modi: Can you bring all those documents and then you pass it to the generative AI and it gives you the summarized information, right?
Uday Kumar Javangula: Yes.
Uday Kumar Javangula: Correct, correct.
Uday Kumar Javangula: That's correct.
Bhupendra Modi: But I think that's a good use case, because at least the sales team, et cetera, we can show them in one of the tab that yeah, this is the extra information for this customer.
Uday Kumar Javangula: Sales team.
Uday Kumar Javangula: Correct.
Bhupendra Modi: So they know that what's happening for that company and what is at very high level.
Bhupendra Modi: See, you know the company names, but they have to do their own research.
Bhupendra Modi: So they can get at least some more detail about that company.
Uday Kumar Javangula: So likewise there are multiple use cases, Bhupinder, which we worked on it as a platform.
Uday Kumar Javangula: So it can be plug and play with different use cases.
Bhupendra Modi: So I think it's a really good. And Kiran, what do you say?
Bhupendra Modi: Do you see some more options where we can do something?
Bhupendra Modi: So Kiran also left.
Kinna Yadav: Kiran, has he dropped? Yeah, he dropped.
Abhijit Thakare: Yeah, Kiran left here.
Uday Kumar Javangula: I think only four of us there, OK.
Bhupendra Modi: Yeah. Yeah, that's.
Abhijit Thakare: Yeah.
Uday Kumar Javangula: I didn't saw that.
Uday Kumar Javangula: I'm sharing my screen.
Kinna Yadav: It's it's almost 9:45, right?
Kinna Yadav: And the meeting was till, yeah.
Uday Kumar Javangula: Yeah, that's fine. No problem.
Bhupendra Modi: So anyway, it is a recorded session.
Bhupendra Modi: It's a very good session.
Bhupendra Modi: Uday and I'm busy will definitely work and we'll see how we can, you know, create some smaller functionalities as a add on which we can sell or market to the customers.
Bhupendra Modi: I'm also thinking if something we can push to the partner portals. I know sales force is far better choice than Oracle, but at least in Oracle also if you push something it will give a good name and you know.
Uday Kumar Javangula: Yes, yes.
Bhupendra Modi: Automatic marketing for our solutions.
Bhupendra Modi: That's also another thought process that I want to work and definitely foreseeable.
Bhupendra Modi: Also we have to connect so we are still discussing what to do and what are our options, but I'll reach out to you foreseeable sales, OK.
Uday Kumar Javangula: Yeah.
Uday Kumar Javangula: Subdue Pender.
Bhupendra Modi: Tense all day tense a visit.
Uday Kumar Javangula: Yeah.
Bhupendra Modi: Thanks for your time.
Abhijit Thakare: Thank you.
Bhupendra Modi: Thank you.
Abhijit Thakare: Thank you.
Bhupendra Modi: Thanks can now organizing it. Thank you.