Uday Kumar Javangula: So basically.
Uday Kumar Javangula: Karen, I think this is the first time you're watching this right demo.
Uday Kumar Javangula: Or earlier we discussed it.
Uday Kumar Javangula: Not sure, OK.
Veerendra Nidadavolu: Yes.
Uday Kumar Javangula: So basically capture is in enterprise enterprise search platform with knowledge management and AI features are enabled here.
Uday Kumar Javangula: Which basically what it does is.
Uday Kumar Javangula: Anyone of organization having a huge data that is being created by multiple departments right over the period of time each department is learning something and they're going to create our knowledge, right?
Uday Kumar Javangula: So we need to acquire that knowledge from different sources.
Uday Kumar Javangula: And make it as a single unified repository.
Uday Kumar Javangula: Ker and Veeru. So this platform. This platform does a complete.
Veerendra Nidadavolu: Here is it.
Uday Kumar Javangula: Integration of your multiple data legs that you're having, whether it is Slack or Microsoft Teams SharePoint or your share data shared data server.
Uday Kumar Javangula: Right. What we it can do is it can auto tag all the information we can index in real time and make it as a single unified path for a knowledge finding.
Uday Kumar Javangula: Storage and knowledge finding. So using different modules we have here.
Uday Kumar Javangula: So that is a base of it. If we go in depth.
Uday Kumar Javangula: So there are multiple features on top of the capture. Why we are calling as enterprises, so each of the enterprise organization having data in multiple sources right?
Uday Kumar Javangula: So they don't need to wait for the data requesting over the e-mail or waiting for some time to get that information. Finally from the server or something.
Uday Kumar Javangula: Instant we can have A1 unified platform.
Uday Kumar Javangula: Where they can knowledge consumers can log in and search against the knowledge that has been in next.
Uday Kumar Javangula: So here we provide the multiple levels of search whether it is keyword search, NLP, natural language search also on top of it we have AI capability where you can ask general given prompt which is this experience of ChatGPT experience we can provide but they're not going to.
Uday Kumar Javangula: Get from the public data. They're going to get from the internal organization knowledge.
Uday Kumar Javangula: So on and overall what we can call is it can be an internal Google further nomination, right?
Uday Kumar Javangula: Capture is an internal Google where you can search anything that you're indexing in real time.
Uday Kumar Javangula: That is a background of the application. So and the platform is.
Uday Kumar Javangula: Multilocking so you can also see the application in multiple different locations. So system supports 43 languages in terms of UI as well as the content that is being stored right?
Uday Kumar Javangula: If we go to the basic features, then I'll show the demo how this works. So the key features.
Veerendra Nidadavolu: Yeah, we can go to the demo guys.
Veerendra Nidadavolu: This is I think I understood the case you are trying to do. So basically the the there are a few questions, but let's look at the demo first.
Uday Kumar Javangula: That's right.
Uday Kumar Javangula: Basically, knowledge search primarily search users will be more, so I'll start with the search, then I'll go aheadavit how the content will be created in capture using authoring modules.
Uday Kumar Javangula: So there are four different module circu. One is a search for knowledge search, capture authoring for knowledge creation are also integrating your existing. We have a prebuilt connectors.
Uday Kumar Javangula: So those connectors will help you to grab all the data.
Uday Kumar Javangula: To capture and we can fine tune the results by using dictionary.
Uday Kumar Javangula: So this is a search.
Veerendra Nidadavolu: So what are you using in the back end to crawl through the? So I'm jumping ahead that you guys want to know what you want to go ahead because can you do you mind opening?
Uday Kumar Javangula: Right.
Veerendra Nidadavolu: Just search for. Did you guys look at sesame?
Veerendra Nidadavolu: Not here in Google.
Uday Kumar Javangula: OK.
Veerendra Nidadavolu: See assignments.
Uday Kumar Javangula: Right.
Veerendra Nidadavolu: Yeah, come down.
Veerendra Nidadavolu: Face me.
Veerendra Nidadavolu: Sorry, that was the first one.
Veerendra Nidadavolu: Yeah, open that.
Uday Kumar Javangula: Nicely, OK.
Veerendra Nidadavolu: Did you look at that platform?
Uday Kumar Javangula: Check this one.
Veerendra Nidadavolu: Yeah.
Veerendra Nidadavolu: So for enablement for sales, if you go to for sales or for customer service, they do exactly this, but a lot more extensive.
Uday Kumar Javangula: Hmm hmm.
Veerendra Nidadavolu: They even curate the content based on what you want to search for what, what pitch you want to do, what is the custom coming in, and they have been integrating with profiler and others other stuff too.
Uday Kumar Javangula: Yeah, we do have. I'll focus, yeah.
Veerendra Nidadavolu: So there are a lot of tools now coming up and these guys are the leaders right now as far as I know in terms of the curation of content.
Uday Kumar Javangula: Mm hmm.
Veerendra Nidadavolu: For sales, marketing or support or whatever you want to call it.
Uday Kumar Javangula: Right, I'll showcase Kiran.
Uday Kumar Javangula: Maybe I'll make a note of this as well, so let's see how this looks like.
Veerendra Nidadavolu: Yeah you can.
Veerendra Nidadavolu: You can make a note of it. So now back to our so these guys are using copilot to integrate, and they're using NLM based.
Veerendra Nidadavolu: Processing for duration of the content.
Veerendra Nidadavolu: So how are you curating the content?
Veerendra Nidadavolu: So here, while the content creation is one part existing content, how are you searching?
Veerendra Nidadavolu: What algorithms are you using?
Uday Kumar Javangula: So yeah, so basically.
Uday Kumar Javangula: When we have this connectors right? So what?
Uday Kumar Javangula: We have a specific indexer in the background.
Uday Kumar Javangula: We store, we curate all the data from.
Veerendra Nidadavolu: OK.
Uday Kumar Javangula: Suppose captures a platform, right?
Uday Kumar Javangula: So it should be integrated with anything.
Uday Kumar Javangula: So if you if you crawl your teams data or SharePoint data, what we do is we create all the data like check all the checkpoints like converting this XML document to proper.
Veerendra Nidadavolu: Yes.
Uday Kumar Javangula: Jason file.
Uday Kumar Javangula: Right, using our background processor, using indexer, then we'll index into open search Kiran and on top of it we create the vector embeddings.
Uday Kumar Javangula: We use a rag approach here which integrated with LLM also with and llama indexes there where we create the vector embeddings on top of the indexed data.
Uday Kumar Javangula: Then user can search on top of it using a chatbot as well.
Uday Kumar Javangula: Kiran, so that is the approach that we.
Veerendra Nidadavolu: When you're using the letter indexing, where are you getting the vector arranges from the vectors from.
Uday Kumar Javangula: This from GPT.
Veerendra Nidadavolu: Is it based on the date from OK so.
Uday Kumar Javangula: From GPT llama, yeah.
Veerendra Nidadavolu: OK.
Veerendra Nidadavolu: Let's continue on with the demo.
Uday Kumar Javangula: So what we do is we once we crawl the data so we.
Uday Kumar Javangula: We index it based on vector embedding ranking and all using.
Uday Kumar Javangula: Then index it to open search when through chatbot. If they're asking right, we'll identify the Internet entity of this question and we'll get the related information and do the rag approach current and get the present as a generative AI response.
Uday Kumar Javangula: Basically in this search we can do keyword search and all regular keyword search. But on top of it if you have any specific.
Uday Kumar Javangula: This system is capable of having multiple data sources as I mentioned, right?
Uday Kumar Javangula: So what we can do is we can grab the data from different data sources that is being retrieved.
Uday Kumar Javangula: So example here we are getting from CBL from web we can crawl any website also here and also some sales force. But if I give.
Veerendra Nidadavolu: So if you want to restrict it to only my information internal to my network, I can do it right?
Uday Kumar Javangula: Yeah, we can do it.
Uday Kumar Javangula: So this is all from internal.
Veerendra Nidadavolu: OK.
Veerendra Nidadavolu: So do you.
Veerendra Nidadavolu: Do you have a context switch also on that user context or it's only based on?
Uday Kumar Javangula: Based on the personalization Karan, so based on the user, suppose I already have access to specific set of data sources right?
Uday Kumar Javangula: So based on the user group visibility, we face, the information will not get entire data here, only based on the profile section.
Veerendra Nidadavolu: So is it only?
Veerendra Nidadavolu: The does it only do search and bring you this or is there an integration with integration to say that OK here are the steps that you can follow to access this information or for example if I want to say you said user manual right, how do I install?
Veerendra Nidadavolu: My how do I do my? How do I do within install?
Veerendra Nidadavolu: How will it take?
Uday Kumar Javangula: Yeah, we can do that.
Uday Kumar Javangula: So this is a keyword search and specific search we can do, but we have a chatbot here, right?
Uday Kumar Javangula: This is experience of the question that you're asking, right?
Uday Kumar Javangula: So where you can ask a step by step process of OK.
Uday Kumar Javangula: So here we can ask.
Veerendra Nidadavolu: Little Mazda.
Veerendra Nidadavolu: Right now, Google has also changed. You see Google is using Google to the top.
Veerendra Nidadavolu: It's now giving you the full test. I mean the steps in the boost so.
Uday Kumar Javangula: Right. So as soon as you search it will give the steps same way.
Uday Kumar Javangula: Here we can choose specific question and style of it. How we want to generate the response right?
Uday Kumar Javangula: Again, this is not getting from Internet current. It is from the index data that has been available in the current repository.
Uday Kumar Javangula: So this document can contain 100 pages, 15 pages, 20 pages.
Uday Kumar Javangula: But we summarize it as a generative AI response based on the question that we ask.
Veerendra Nidadavolu: This is nice because what we're doing for real page right now is we have changed our support activity.
Veerendra Nidadavolu: We have reduced our support tickets by 80%.
Veerendra Nidadavolu: What we did is we did exactly the same thing, but we integrated copilot also into this.
Veerendra Nidadavolu: So we are calling it Lumia a new product that we just launched.
Veerendra Nidadavolu: What it does is you go type in saying like I want to update my payment.
Veerendra Nidadavolu: Type so it will give you the first step saying here is the steps that you need to follow to update your payment plan and then it'll give you a link at the bottom saying do you want to do this right now click here.
Veerendra Nidadavolu: So when they click on it, we take the user context and then we'll directly take them straight to the page where they can just update the log in and update the payment and then it automatically defects.
Veerendra Nidadavolu: So with that, our support tickets went down by almost 80%.
Veerendra Nidadavolu: Because so it it's a now I now I got the.
Uday Kumar Javangula: Yeah.
Veerendra Nidadavolu: Any.
Veerendra Nidadavolu: Again, where you can create new ones, you can store them all. That is fine.
Uday Kumar Javangula: Usually if you see the document is having all this data right specific to it.
Uday Kumar Javangula: So now it summarizes, then finish.
Veerendra Nidadavolu: OK, so you can go to the document also from there.
Uday Kumar Javangula: Yeah, they can open the document specific to suppose if you are the if you are doing any customer support or something right they can search it and find the relevant document and provide the real time support for end customer.
Veerendra Nidadavolu: Yes, and the the other question is now.
Veerendra Nidadavolu: Now this is for existing documents that we are creating right?
Veerendra Nidadavolu: How how long does it take to come?
Veerendra Nidadavolu: Crawl and capture and get the data ready.
Uday Kumar Javangula: It will be real time, so as soon as suppose if you sync your SharePoint right.
Uday Kumar Javangula: So in real time the data will crawl and it will be available for end user search.
Uday Kumar Javangula: I'll show you that quickly the use case one of the.
Veerendra Nidadavolu: The data can be PDF. Also I was able to ask what type of documents do you support PDFs for?
Uday Kumar Javangula: It can be any support anytime.
Veerendra Nidadavolu: Excel.
Uday Kumar Javangula: Yeah. Excel anything?
Uday Kumar Javangula: I'll show you right away.
Veerendra Nidadavolu: But I'm going to day. You said you 100,000 PDO so much time it will.
Veerendra Nidadavolu: Take to problem.
Uday Kumar Javangula: Just 10 minutes, just 10 minutes.
Uday Kumar Javangula: Just 10 minutes.
Veerendra Nidadavolu: And how often do you for all this?
Uday Kumar Javangula: And we've done that.
Uday Kumar Javangula: So we what we do is we show the we have a badge of in background, OK.
Uday Kumar Javangula: So for every frequency that we set up, so it will keep on monitoring, the delta will be indexed to the data.
Veerendra Nidadavolu: Different some video scam all day. And then what if I take perform?
Veerendra Nidadavolu: No, that's what I'm taking to ask.
Veerendra Nidadavolu: Like you know, next one is when you are doing the PDFs right?
Veerendra Nidadavolu: Usually it's the PDFs and the Excel set up the most, and the open docs.
Veerendra Nidadavolu: These three are the hardest ones to go.
Uday Kumar Javangula: 'Cause if you add this one right, I'm just adding this a customer support.
Veerendra Nidadavolu: Right. You integrated with Slack.
Uday Kumar Javangula: Yeah, this line, yeah, integration.
Veerendra Nidadavolu: Yes, Slack is fine because slack you see ads directly access the data. So and this is stored in a pretty easy.
Veerendra Nidadavolu: Can you try with the? Can you show us with the PDF?
Uday Kumar Javangula: Yeah, sure.
Veerendra Nidadavolu: When you're showing it in video, the support doc.
Veerendra Nidadavolu: Stay far away.
Veerendra Nidadavolu: I guess we can follow.
Veerendra Nidadavolu: I want to see how it's coming.
Veerendra Nidadavolu: It's PDF and tables.
Veerendra Nidadavolu: Look at the content once. Yeah. Uday, open the one which Venkat I shared you.
Uday Kumar Javangula: So.
Veerendra Nidadavolu: I'll pull it.
Uday Kumar Javangula: That I have indexed it as a major.
Veerendra Nidadavolu: Oh.
Uday Kumar Javangula: It has a image.
Uday Kumar Javangula: Actually we need to do OCR on top of it.
Veerendra Nidadavolu: Exactly so because.
Veerendra Nidadavolu: Even for PDFs, there we should.
Veerendra Nidadavolu: We should do an OCR, right? Otherwise?
Uday Kumar Javangula: OK.
Uday Kumar Javangula: Correct. So correct it will be.
Veerendra Nidadavolu: It won't give you the accurate information, so you are doing on top of it.
Uday Kumar Javangula: We're using AW bless OCR where we'll identify the PDF data if any mails or something is right. If you upload image JPG then we do using AWS CR service Kiran.
Veerendra Nidadavolu: OK.
Veerendra Nidadavolu: Look for all PDFs and images. Everything you're using here.
Uday Kumar Javangula: AWS currently before Oscr.
Veerendra Nidadavolu: And what format are we looking at when we're extracting the data like?
Veerendra Nidadavolu: A.
Veerendra Nidadavolu: What do we call?
Veerendra Nidadavolu: You give 32 any characters.
Veerendra Nidadavolu: It doesn't matter of any special characters that we're not sure about.
Uday Kumar Javangula: No special special characters were.
Uday Kumar Javangula: I mean, because since we're indexing to open sets, right?
Uday Kumar Javangula: So we have to restrict some special characters so that index will not fail, right?
Uday Kumar Javangula: So that will do the operation in background once we extract using ticker parser right through ticker parser, we'll extract the content on top of it. If any images we do the OCR then index into convert into Jason Kiran and index into open search.
Uday Kumar Javangula: With rate with embeddings.
Veerendra Nidadavolu: OK.
Veerendra Nidadavolu: Dangerous and does this suggest content also right now or is it just a search and search articles and do it?
Uday Kumar Javangula: Search the articles by Sessions will be based on suppose recommendation.
Uday Kumar Javangula: Recommended articles will recommend right and also if they store some kind of what do you say?
Uday Kumar Javangula: Specific to a particular term, if you want to look look over in feature right, what we can do is we can do the saved search, they will notify that user as soon as the data is available on that search.
Veerendra Nidadavolu: Yeah.
Uday Kumar Javangula: Recommendations and the articles.
Veerendra Nidadavolu: OK.
Uday Kumar Javangula: And also top viewed articles recommended articles will be given and we can also notify if they subscribe to their channel. They can get all the notifications over the specific document or entire channel when there when there is a new content published, correct.
Veerendra Nidadavolu: Got it.
Veerendra Nidadavolu: OK. And can, is it a widget that we can capture into plug into any any web page that we want to or is it?
Veerendra Nidadavolu: This one.
Uday Kumar Javangula: It's not tightly coupled, it's API driven so we don't need to use same UI or just. You can include this search bar and give that response.
Uday Kumar Javangula: We can integrate with any web page by passing all the parameters and authentication because we made it as AAPI driven so we can plug in play. We did this integration with Salesforce as well where we can.
Veerendra Nidadavolu: The integration with Salesforce and others is good. What I was asking is do we have any any kind of like for example at second I take this chatbot and put it on my webpage.
Uday Kumar Javangula: Do.
Uday Kumar Javangula: Like embedding, right?
Uday Kumar Javangula: Embedding kind of, no. All we need to do is we just need to enable the API. That's it.
Veerendra Nidadavolu: My web page, yeah.
Uday Kumar Javangula: Same pattern if you enable it in your webpage then it will trigger.
Veerendra Nidadavolu: OK.
Uday Kumar Javangula: You can select what source you need, maybe in your own data source. What we can do is we can enable your index here.
Uday Kumar Javangula: Then you can search all these things from your data only through chatbot.
Veerendra Nidadavolu: OK. And is this?
Veerendra Nidadavolu: Like is this right now or is it a in house list?
Uday Kumar Javangula: It's what I can say is it's not SAS based.
Uday Kumar Javangula: We're not at SAS model yet, but implementation point of view like what you can say is single tenant.
Veerendra Nidadavolu: No, no.
Veerendra Nidadavolu: OK.
Veerendra Nidadavolu: So we can run it on, we can give it as an instance to anyone.
Uday Kumar Javangula: Instance. So anyone with any cloud, not just AWS.
Uday Kumar Javangula: Currently it's running on AWS but works with OCI and GCP as your as well.
Uday Kumar Javangula: So based on the customer need.
Veerendra Nidadavolu: It's basically the cloud agnostic.
Uday Kumar Javangula: No diagnostics.
Uday Kumar Javangula: So you can take anything, any cloud, but it is compatible.
Veerendra Nidadavolu: And what's the minimum data size that they are looking at on the Max data size that they're looking at?
Uday Kumar Javangula: So minimum actually we are going with.
Uday Kumar Javangula: At least minimum data with 1TB or something maybe?
Uday Kumar Javangula: Kind of thing if they have data so.
Uday Kumar Javangula: So initially we can go with any find out GB or something or 1TB.
Veerendra Nidadavolu: OK, in any specific?
Uday Kumar Javangula: So since it can crawl multiple.
Veerendra Nidadavolu: So right now, you're not really any kind of online based algorithms to prod, right? They're all in there.
Veerendra Nidadavolu: So if I want to restrict it only to my my network, can I restrict it or still the algorithms are going to the to the web to?
Uday Kumar Javangula: No, no, it will be within nine hours within the capture itself. So it's not going outside data.
Veerendra Nidadavolu: It's not like chatty, but it's not going any problem. We're doing anything outside to match or suggest anything, right?
Uday Kumar Javangula: No, no.
Uday Kumar Javangula: No, no. It is within in House because what we are going to do is we are generating AI right, generating response, right. It will recommend based on the recent findings of the document. Then we are generative AI through ChatGPT we're suggesting but it cannot crawl our data.
Veerendra Nidadavolu: Huh.
Uday Kumar Javangula: By charging. Currently we're using paid version of GPT 40, so we have an agreement with that data will not be crawled.
Veerendra Nidadavolu: OK.
Uday Kumar Javangula: For the training point of view, Karan.
Veerendra Nidadavolu: Sure, this is nice.
Uday Kumar Javangula: Same way I'll show you the ring part.
Uday Kumar Javangula: Simply like integrations, what we have and also a what we have used a for knowledge generation. Also we can use Karan.
Uday Kumar Javangula: These are all the connectors we have.
Uday Kumar Javangula: So basically.
Veerendra Nidadavolu: These are out-of-the-box kind of present.
Uday Kumar Javangula: Out-of-the-box. So based on the customer need we can enable disable. So based on the plan or something so they can do all this.
Veerendra Nidadavolu: And the reason why you did not TFs.
Uday Kumar Javangula: Mail server OK.
Veerendra Nidadavolu: If it's Microsoft.
Uday Kumar Javangula: Microsoft SharePoint we have right?
Uday Kumar Javangula: We did SharePoint and teams.
Veerendra Nidadavolu: It's a SharePoint, right?
Veerendra Nidadavolu: DFS is similar to JBA, right, so?
Uday Kumar Javangula: Jira. Yeah, you have a JIRA engine that.
Veerendra Nidadavolu: Gmail will help me automate this GFS right from Microsoft so.
Veerendra Nidadavolu: The foundation server.
Uday Kumar Javangula: We do have this service cloud for data mapping.
Uday Kumar Javangula: Mechanism. Also AWS 3 and all. So where?
Veerendra Nidadavolu: And on top of this, I'm doing any kind of customer analysis.
Veerendra Nidadavolu: Also the if I want to run like for example now that you have integrated with Salesforce, if I say.
Veerendra Nidadavolu: You made a clustering of cases by.
Veerendra Nidadavolu: Singap cases between this period X2 to the last 10 days.
Veerendra Nidadavolu: Something like that.
Veerendra Nidadavolu: Can we do?
Uday Kumar Javangula: Yeah, we can do it because we did predictive analytics. Kiran example, we have this integration we have made.
Veerendra Nidadavolu: OK.
Uday Kumar Javangula: So using AI, right?
Uday Kumar Javangula: Suppose we what we indexed is we took one of the airline customer data right where we get comes customer tickets right service requests.
Uday Kumar Javangula: So we indexed all the data on top of AI. We need to predict like who are the customers have more chances to cancel their ticket in next one week, right?
Uday Kumar Javangula: What are the allowances that we can give, how we can upsell the opportunities based on the usage of the customer?
Uday Kumar Javangula: So that is also through AA itself. What I mentioned is so here itself.
Uday Kumar Javangula: In Sibel we can get.
Uday Kumar Javangula: So what that does is based on existing data, it will identify what are all the up sell.
Uday Kumar Javangula: If we need to mention the specific area, then we can it.
Uday Kumar Javangula: Right now it is giving the basic answer.
Uday Kumar Javangula: We can ask multiple question on top of the tickets that are being indexed.
Veerendra Nidadavolu: Because one of the things that we we have done additionally on top of this is we have integrated our sales force. We took the cases and for internal teams it keeps giving generating reports for us on a weekly basis. That says if I say go and say like.
Veerendra Nidadavolu: OK, what am I?
Veerendra Nidadavolu: Top issues reported to customers in last three months. Then it will.
Uday Kumar Javangula: Yes, yes.
Veerendra Nidadavolu: Look, where is it?
Veerendra Nidadavolu: Left wing on our site we cluster them according to the tickets and the the common themes, right?
Uday Kumar Javangula: Understood.
Veerendra Nidadavolu: And then it predicts like, OK, these are the top 10 cases or top 20 cases that you can explore.
Veerendra Nidadavolu: So the next quarter we fix, we work exclusively on those top 10 or 20 cases to resolve the issues.
Uday Kumar Javangula: Thanks.
Veerendra Nidadavolu: So like that, if we do everything because then since we're going against the cases, it's it's different from cases versus your right.
Uday Kumar Javangula: Same.
Uday Kumar Javangula: Knowledge, yes.
Uday Kumar Javangula: Yes, correct.
Uday Kumar Javangula: So using this right, we can also identify the agent performance also like for the customer support who who is a agent that provided the quick resolution.
Uday Kumar Javangula: So because this since data is indexed, it's depending on our prompt so it will fetch all the information from that.
Veerendra Nidadavolu: Yeah.
Veerendra Nidadavolu: OK.
Uday Kumar Javangula: Details Kiran.
Veerendra Nidadavolu: Like, when did we do this?
Uday Kumar Javangula: Integration.
Veerendra Nidadavolu: So when did the product launch happen? The first version? MVP.
Uday Kumar Javangula: Actually we implemented for one of the education industry since this like it's already launched. It's been one year using by other customer. But we are planning to implement for different like automate industry and manufacturing.
Uday Kumar Javangula: So we are targeting some kind of Indian companies as well along with us. So example we are talking to a couple of like eBay or.
Veerendra Nidadavolu: OK.
Uday Kumar Javangula: Those are all in progress. All some kind of sales demos are happening.
Veerendra Nidadavolu: OK.
Veerendra Nidadavolu: Sales pitch.
Veerendra Nidadavolu: Talk about what you guys can do for.
Veerendra Nidadavolu: Farmer industry. What you guys can do for?
Veerendra Nidadavolu: The some.
Uday Kumar Javangula: That's good health, health care.
Veerendra Nidadavolu: Mechanical companies or something like that?
Uday Kumar Javangula: Yes, we we do the pitching like that.
Uday Kumar Javangula: We do because we are talking to most of the manufacturers, right?
Uday Kumar Javangula: Manufacturers and engineering point of view, we did demo for we even we customized the chat board for Renault as well.
Uday Kumar Javangula: So based on the engineering documents identifying the.
Uday Kumar Javangula: Faults of an engineering diagram using this AI.
Uday Kumar Javangula: So we did that.
Veerendra Nidadavolu: So can this create a document if I ask some question what create on document ping the difference of some?
Veerendra Nidadavolu: Or something like that.
Uday Kumar Javangula: Yes, we do.
Uday Kumar Javangula: I'm going to showcase that so one is search we saw now is knowledge creation.
Uday Kumar Javangula: How AI can be used for creating knowledge right? If you give just a keyword right?
Uday Kumar Javangula: Suppose out to oned a customer to eBay or if you ask any question, anything.
Veerendra Nidadavolu: Did they create some document?
Uday Kumar Javangula: Yeah, that's what. So let me by giving this keyword right.
Uday Kumar Javangula: If we can create using generative AI, right? If you are aware of prompting and all, we can give the prompt what prompt you need on top of this text.
Uday Kumar Javangula: Or you can choose from web we can generate in different style.
Uday Kumar Javangula: Professional or something or we can also take it from internal knowledge from existing knowledge. Also you can pick from different channel then it will calculate all the processing and fetch all the documents. It will create a new document from internal knowledge and as well.
Uday Kumar Javangula: Let me take web and professional and generate.
Uday Kumar Javangula: Now the document will be created as a initial draft.
Uday Kumar Javangula: So all that, no need to wait for preparing one simple document using this AI, they can create a sample document.
Uday Kumar Javangula: So it gives you one draft version on top of it.
Uday Kumar Javangula: User can update it or further.
Uday Kumar Javangula: Then publish the document. It goes to review process again. Workflow general thing so it saves time for authors as well for knowledge creation using generative AI Veeru and Kiran.
Uday Kumar Javangula: On top of it, if you see if you're not like this paragraph, you want to rewrite it.
Uday Kumar Javangula: So we can also do the rephrase.
Uday Kumar Javangula: Using generative A and we can use any style, then it will update only the selected paragraph with a new style with a new content.
Uday Kumar Javangula: So in terms of generative A with knowledge creation.
Uday Kumar Javangula: Yeah, this the things from Creation point of view accurate.
Veerendra Nidadavolu: But the overall two days, like somebody came from us and they gave me a demo of the real tool.
Veerendra Nidadavolu: So basically what it was, they uploaded all the code cases of related to.
Veerendra Nidadavolu: Yeah, all the web sites of their tool and they asked a question to the tool.
Veerendra Nidadavolu: See I am separating.
Veerendra Nidadavolu: On these conditions, OK, now create an application for me to file in the port. They cannot do that.
Uday Kumar Javangula: Mm hmm right.
Veerendra Nidadavolu: It's legal.
Veerendra Nidadavolu: You cannot do.
Veerendra Nidadavolu: So no, no more cases will not generate petitions online.
Veerendra Nidadavolu: You can give your even if you go to any one of them, they won't give you contracts.
Veerendra Nidadavolu: Contract generation are legal.
Veerendra Nidadavolu: Filing generation is illegal.
Veerendra Nidadavolu: All the way through are available open. That's where the legal that's how the legal industry came out of this whole it cannot do. Case differences cannot give a.
Veerendra Nidadavolu: Uh, connections.
Veerendra Nidadavolu: It cannot give uh, like if you go chatting with it, say like, uh, if you ask them what is IPC, it will give you something.
Veerendra Nidadavolu: Generally say like OK, I want to.
Veerendra Nidadavolu: Someone get and run here.
Veerendra Nidadavolu: Someone did an accident?
Veerendra Nidadavolu: What case?
Veerendra Nidadavolu: What are the charges generally give you suggestive things saying here are general sections applied in case of accident accident, but it cannot give you a.
Veerendra Nidadavolu: File in the executive.
Veerendra Nidadavolu: I saw it.
Veerendra Nidadavolu: They they they might have done it, but they will get into legal trouble because that's the lead we are dealing with.
Veerendra Nidadavolu: We have an application but no it should be able to lawyer lawyer. By the way you go to the file for review or not.
Veerendra Nidadavolu: It has to go through even generation. They cannot do it, can give a template saying here is a gender template.
Veerendra Nidadavolu: Wow, you use these cases.
Veerendra Nidadavolu: So you said, like for example, I'm separating the separation because of.
Veerendra Nidadavolu: Whatever. Let's say mental harassment or physical abuse or whatever. Here is a suggested template of how you can do it.
Veerendra Nidadavolu: It cannot give you sections.
Veerendra Nidadavolu: It won't give you previous references. It won't give you anything, but based on the previous references, it will give you a template.
Veerendra Nidadavolu: So if you go into now go to the chatbot and say I want to create a contract rental contract right now it will say OK I.
Veerendra Nidadavolu: Here is a general template for contract contract, but it won't specify anything unless you do.
Veerendra Nidadavolu: More specifics, OK, add reference of XYZ case then in some cases if we defer it'll not put it saying like OK, I cannot add these references because they will be specific to.
Veerendra Nidadavolu: If there will be personal information, there will be information that they're not supposed to be using, but that tool it created an applications. I think the references. No, that that's illegal.
Veerendra Nidadavolu: That's what I'm trying to say.
Veerendra Nidadavolu: That's illegal.
Veerendra Nidadavolu: You cannot do it, otherwise everyone will do it in the PPL.
Veerendra Nidadavolu: Everyone will do it and then go inside.
Veerendra Nidadavolu: OK, now when we spoke to Pawan.
Veerendra Nidadavolu: For NCLB cases?
Kinna Yadav: Yeah, yeah.
Veerendra Nidadavolu: Nclt might be different.
Veerendra Nidadavolu: Nclt might be different thing because nclt is like it's just.
Veerendra Nidadavolu: A.
Veerendra Nidadavolu: Corporations are going to defund.
Veerendra Nidadavolu: And say it is with corporations that are going under it, that they're different kind of units for it. Somebody appearing it somebody not whatever.
Veerendra Nidadavolu: Oh yeah, that, that might be even for those.
Veerendra Nidadavolu: I don't.
Veerendra Nidadavolu: I don't think you can do the law said.
Veerendra Nidadavolu: We can do it.
Uday Kumar Javangula: Probably probably what we can see already, IPC sections like public data is available, right, Karen like Vertex. Vertex will be specific to company using a what we can.
Veerendra Nidadavolu: Yeah, yeah.
Veerendra Nidadavolu: So public data is available you cannot give.
Veerendra Nidadavolu: A.
Veerendra Nidadavolu: Once again.
Veerendra Nidadavolu: That couldn't swift watermark.
Veerendra Nidadavolu: Yeah, this is.
Veerendra Nidadavolu: The.
Veerendra Nidadavolu: That was 40 9.
Veerendra Nidadavolu: 4.
Veerendra Nidadavolu: Yeah. So it will only do it if we provide all the information.
Veerendra Nidadavolu: And charge it will be generate a code case application citing other cases.
Veerendra Nidadavolu: All it says is we can assist in generating a legal document, such as a code case, application or petition.
Veerendra Nidadavolu: Also helps site relevant cases, but you need to provide all the information which is the jurisdiction for this actual backup because they don't access to crawl, they are not allowed to access charging.
Veerendra Nidadavolu: Stations, that's what.
Veerendra Nidadavolu: 'S are not legal.
Veerendra Nidadavolu: Legal is never bounded to the charge deputy.
Veerendra Nidadavolu: Basically any lawyer.
Veerendra Nidadavolu: They refer to somebody documented it. Is that then this error?
Veerendra Nidadavolu: So I.
Veerendra Nidadavolu: Don't even ask this question.
Veerendra Nidadavolu: It will crawl all those things and then you have to put some kind of validation around it to say that who is accessing this. Why?
Veerendra Nidadavolu: For both cases, you cannot just go and write it.
Veerendra Nidadavolu: Download teams.
Veerendra Nidadavolu: They still know who is downloading it right near the end of the day.
Veerendra Nidadavolu: Oh, I don't know who that public can access.
Veerendra Nidadavolu: Oh, you can actually, whatever a is there, hmm?
Veerendra Nidadavolu: A.
Veerendra Nidadavolu: And then open to everyone I access share, but only confined documents not accessible.
Veerendra Nidadavolu: No, no.
Veerendra Nidadavolu: But you see all these files running all the web browser there as a what needs to become books. But case files only export.
Veerendra Nidadavolu: And they go to register and.
Veerendra Nidadavolu: OK.
Uday Kumar Javangula: So that is on a knowledge creation generation.
Uday Kumar Javangula: So based on the input that we provide so we can create that new document on top of knowledge and that.
Veerendra Nidadavolu: Why is Chattahoochee not allowed to crawl legal case documents? Is not allowed to crawl or access legal case documents, or privacy and confidentiality?
Veerendra Nidadavolu: Cooperated licensing restrictions.
Veerendra Nidadavolu: Do this dictionary limitations, data protection laws?
Veerendra Nidadavolu: There are similar ensure that similar AI systems restrict legal and ethical boundaries while operating.
Veerendra Nidadavolu: So there are they.
Veerendra Nidadavolu: They they are not allowed to crawl right now. If someone downloads and does it.
Veerendra Nidadavolu: That's again, I would say that it's kind of a legal grade.
Veerendra Nidadavolu: They might be working in but same same thing. They now want to apply for the Copyright Act.
Veerendra Nidadavolu: Also, they're saying don't allow the chargeability to access copyright files or stories, and everything is the last time we model writers build a the render writers will strike is around that right.
Veerendra Nidadavolu: Because yes, they are using AI to.
Veerendra Nidadavolu: Write their stories and.
Veerendra Nidadavolu: They're bypassing the sub because their writers have all the books online. They'll send it for.
Veerendra Nidadavolu: Script you have settle interactions.
Veerendra Nidadavolu: They're taking that holds in office content and then charge them writing it's own story.
Veerendra Nidadavolu: So there are certain restrictions, but yeah, this this I think.
Veerendra Nidadavolu: Sorry, we we got sidetracked there a little bit, but.
Veerendra Nidadavolu: I think this is this is perfect.
Uday Kumar Javangula: It's on a feature and also we can create data and data we can create and we also have this all authoring features like comparison, right, all the version control mechanism to track everything and also show the life cycle of timeline. We can also translate I mentioned it's a.
Veerendra Nidadavolu: Thank you.
Uday Kumar Javangula: Multi language system so you can translate one list document.
Veerendra Nidadavolu: And where is this meeting published right now?
Veerendra Nidadavolu: There is this article getting published.
Uday Kumar Javangula: The article will be it's not in public right? If it's in the in House application, it's not for web. It's internal. Consumers will be there right where they can search for a service manual or service information or any kind of data.
Veerendra Nidadavolu: OK.
Uday Kumar Javangula: It's not publicly available, right?
Uday Kumar Javangula: Only for the specific ordination.
Veerendra Nidadavolu: Sure. OK.
Uday Kumar Javangula: It can translate into multiple locals in one single click.
Uday Kumar Javangula: You can also see the history with all the data that we have. We can compare revert to one version, all content management mechanism we have current year.
Uday Kumar Javangula: So which shows can compare the previous versions?
Uday Kumar Javangula: What has been added?
Uday Kumar Javangula: What is deleted so easy comparison?
Uday Kumar Javangula: For every simple change single change we create a new version.
Uday Kumar Javangula: So if we see there are 7 versions, number of versions can be created.
Uday Kumar Javangula: And the translate article, there are some actions, right?
Uday Kumar Javangula: So where we can give select the locals specific locals?
Uday Kumar Javangula: So basically language translation we are using, we support 4-5 engines here.
Uday Kumar Javangula: One is AWS translate.
Uday Kumar Javangula: Currently we are using but it supports Google Translate deeply line bridge and Azure services as well.
Uday Kumar Javangula: So based on the selection, we can just select it and.
Uday Kumar Javangula: Use auto publish and submit it then automatically system will convert this English local to the selected local on the fly.
Uday Kumar Javangula: Same way whatever that is being translated here, we can see the translator at locals here itself.
Veerendra Nidadavolu: And it does the translation based on what?
Veerendra Nidadavolu: Is there an accuracy percentage or someone has to validate these again?
Uday Kumar Javangula: Two things we can do.
Uday Kumar Javangula: One is if you don't want to get auto publish, we can do a draft version that goes to a request for a specific language user. Maybe a French user where you can validate and correct it and publish for the next version, or auto publish. If you accuracy will.
Uday Kumar Javangula: Be almost 80 to 80 to 85% specific to AWS is good.
Uday Kumar Javangula: We even we checked with one of the Japanese, like Mazda user.
Uday Kumar Javangula: We created this Japanese document and had validated so it looks good.
Uday Kumar Javangula: It is matching accuracy on the Japanese as well.
Uday Kumar Javangula: So then we stick with AWS as of now.
Uday Kumar Javangula: But we have integration with multiple.
Uday Kumar Javangula: Oh, Indian cellular.
Uday Kumar Javangula: I'll show you that.
Uday Kumar Javangula: Like these are all the things deepali and bridge, Microsoft, Google, Amazon.
Veerendra Nidadavolu: OK.
Uday Kumar Javangula: All content management capabilities on top of a for enterprise search and.
Uday Kumar Javangula: Chatbot and also customer support. Also we did service connect module.
Uday Kumar Javangula: For creating the cases and tracking the cases which will be helpful for a customer support standpoint of you on top of knowledge also there where they can refer to this whatever authoring we are doing right content creation. They can refer this knowledge and.
Uday Kumar Javangula: I logged.
Veerendra Nidadavolu: OK.
Veerendra Nidadavolu: I think I got the correct software. Nice platform.
Veerendra Nidadavolu: Good job so.
Veerendra Nidadavolu: Let's let me put in the catch up a little bit and we'll see, OK.
Uday Kumar Javangula: OK, OK.
Veerendra Nidadavolu: Today I need this.
Uday Kumar Javangula: Sure.
Veerendra Nidadavolu: What is that?
Veerendra Nidadavolu: Can somebody from your team check on this legal stuff, whether it's illegal or illegal to do that? What?
Uday Kumar Javangula: Vero will make a note and confirm.
Veerendra Nidadavolu: Yeah. As for the, As for the, I know it's legal, but check once and also can you make one pitch deck for sales?
Veerendra Nidadavolu: Pitch for the sales industry.
Uday Kumar Javangula: Seals industry.
Veerendra Nidadavolu: This can capture.
Veerendra Nidadavolu: Documents.
Veerendra Nidadavolu: That.
Veerendra Nidadavolu: Are particles.
Veerendra Nidadavolu: Let's chat about. OK. Thanks.
Uday Kumar Javangula: Thank you Kiran for your time.
Veerendra Nidadavolu: I'll get back in my lens.
Veerendra Nidadavolu: I just gonna have to say this out.
Uday Kumar Javangula: Sure, sure.
Veerendra Nidadavolu: Thanks Kumar.
Kinna Yadav: Yeah. Thank you. Thank you.
Veerendra Nidadavolu: Thank you.
Uday Kumar Javangula: Thanks, veeru.
Veerendra Nidadavolu: Bye bye bye. Nice breakman.
Uday Kumar Javangula: Thanks for time.
Uday Kumar Javangula: Thank you. Bye.
Kinna Yadav: Thank you.
Uday Kumar Javangula: Thanksgiving. Thank you.
Veerendra Nidadavolu: Hello.
Uday Kumar Javangula: Bye.