The AI Revenue Operating System

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Julia Nimchinski: The host, who.

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David Watson: Vp. Of sales with Viso and Bharat leads our pre sales engineering team.

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Bharat Jindal: Hi, Julia, it’s great to meet you in person. We’ve been interacting over emails. Thanks for having us.

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Julia Nimchinski: Yeah, our pleasure.

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Julia Nimchinski: Let’s dive in.

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David Watson: Awesome.

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David Watson: So we have, like 5 slides to set the table for who we are and what we do, and then we’ll use the the balance and bulk of our time for the demo to show you the platform.

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David Watson: Avizo is a 9 year old company. Avizo is an AI revenue intelligence platform that does a few basic things. We do predictions. We do guided selling that leads to increased win rates, increased productivity. And it also leads to lower cost and a better user experience by eliminating single point solutions. And we also help our clients like Honeywell, eliminate Crm costs that was highlighted in hbr white paper.

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David Watson: We were excited last year to be recognized by Forrester as the capabilities leader in the revenue Intelligence Space, and we work with hundreds of customers. We work with large companies like a Honeywell, Lenovo and Netapp, fast growing companies like whiz and ironclad and middle of the road, spectrum countries, companies like ringcentral and Bmc software.

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David Watson: we see the world from a enterprise autonomy level 5 levels, basic Rpa magentic workflows, co-pilots and AI agents, a composition and coaching, and then the fully autonomous enterprise which we don’t believe we’re there yet. But Avizo helps guide our clients through these 1st 4 levels of enterprise autonomy.

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David Watson: So how do we do that?

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David Watson: We do that through our unique Api 1st architecture that allows Avizo to act as an AI brain for our clients and an AI operating system for your enterprise. Fundamentally, we unlock value in IP that resides in structured and unstructured data and systems. This includes insights, persona, specific workflows, guidance predictions

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David Watson: and the ability just to talk to data in natural language. The value prop is, how can you get that new employee fresh out of school as productive and as intelligent and as proficient as somebody with 15 to 20 years

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David Watson: of enterprise experience.

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David Watson: Just to quickly highlight a couple of our clients, as I mentioned Hbr. Did a white paper on Honeywell last year. Our AI helped drive more sales. They had 150 million dollars in incremental revenue, but we also helped them reduce costs by eliminating Crm. Spend for licenses that weren’t needed.

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David Watson: Wiz is another one of our clients when we 1st started working with them. A year ago Google tried to buy them. Their valuation was 23 billion. A year later, with us, it moved to 32 billion. We helped them with increased win rates, increased productivity. And now I want to turn it over to Brock to show you the platform.

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Julia Nimchinski: Correct.

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Bharat Jindal: Thank you, David. So with that, I’m going to transition to showing you some of our capabilities. Julia, as David mentioned Avizo, provides you with an AI brain that provides some tremendous capabilities through avatars and AI agents. Let me know if you can see my screen. Okay.

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David Watson: Yes.

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Bharat Jindal: Okay, excellent. So I’ll start by demonstrating some of the AI agents that are embedded in the platform. Julia.

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Bharat Jindal: So as an example, when you go in as an ae, you could be a new ae who’s just started on the job. You could be somebody who’s coming up to speed. And the idea behind these AI agents and these experiences to provide A’s and new folks the ability to ramp quickly

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Bharat Jindal: and generate revenue quickly. So here I’m going to show you some ways to interact with Avizos AI agents.

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Bharat Jindal: One of the ways in which you can interact with the AI agents is by. Let’s say I’m a new A, and I work within the esports sector. Right? So let’s say, my focus is esports. I’ve just been hired.

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Bharat Jindal: I haven’t come up to speed, and I don’t have the ability to engage a technical person on the call with me.

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Bharat Jindal: I I’m not getting the help I need. I need either a Csm. Or an Sc. For me to help with the call, but I don’t have that ability, but I can engage with this AI agent, which is embedded within a viso.

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Bharat Jindal: And I can start asking questions off of this AI agent.

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Bharat Jindal: Right? So I can actually, while I’m on the call with the customer, I can ask questions like.

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Bharat Jindal: Hey, what are the offerings that we provide? And the example we have here is Cisco.

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Bharat Jindal: So this particular AI agent is trained on Cisco’s data as an example. So this AI agent is, is modeling, and and our models are running with Cisco’s data that is publicly available

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Bharat Jindal: to show this demonstration. So I’m going to say, Hey, I’m on a on a call with a customer. I need to understand what are the offerings that we provide under esports and entertainment.

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Bharat Jindal: The AI agent is actually analyzing all the data, all the models, to provide me with a very specific answer about the fact that Cisco provides a range of products around esports and entertainment initiatives.

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Bharat Jindal: And then there are different families of products, including routing switching data center wireless. And I can ask these questions through audio. I can do this through a pre-baked set of questions that are available here as well. The next question I’ll ask is, hey, what are the sub products that are available under esports?

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Bharat Jindal: And these questions can also, Julia be asked from a real live avatar that avatar actually is not working with your zoom, but which we wanted to show live. So I’m showing it to you in a chat interface, if you will. But in this example, you come back with very detailed information on the sub products

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Bharat Jindal: within esports that are applicable, and I could keep on asking detailed questions about these subproducts. For example, I could say, Hey, could you elaborate more on the 5G. Lte for gaming? And you know, so you can just sort of.

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Bharat Jindal: And and the point is, you don’t have to know all the products as a new hire. You don’t have to come up to speed on all the capabilities you can just engage with these AI agents that can make you productive

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Bharat Jindal: and help you generate revenue very quickly. You could then say, Hey, you know. So this is providing with very specific information on the public lte option that is available. And again, it’s tapping into all sales enablement collateral that might be available. All the materials that are in product knowledge documents that a technical person would generally go into. So there’s no need for you to go into slack and interact with multiple people. You actually have all the information you need here. In one place.

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Bharat Jindal: you could kind of drill into information about competitor stuff. Right? So you could say, Hey, how? How do our products compare to Juniper? Because I’m getting a question where the prospect is evaluating another competitor and Cisco competes with Juniper?

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Bharat Jindal: So the agents here will actually go behind the scenes and and show specifically in which situations which is low latency situations.

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Bharat Jindal: Cisco stands out compared to juniper in these fashions. And it’s comparing product line items. It’s comparing very specific detail product information. And you could just keep on going. You could ask any open, ended question about cost, about very specific products like, I could just pick any any of the output. It’s provided me and I could ask detailed questions about that. And I could just keep on going. And that’s the idea is to provide these agents. These are also available as avatars

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Bharat Jindal: the other way to navigate, and the other way to navigate through Avizo is to actually look at the summary that Avizo generates our AI agent. Framework

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Bharat Jindal: generates a summary for every individual in the organization

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Bharat Jindal: so it could be an Ae, it could be a Bdr. It could be a 1st line manager in this example. I’m going in as a chief revenue officer

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Bharat Jindal: who wants to plan the week out. I need some recommendations on where to focus, because I have hundreds of things looking to catch my attention.

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Bharat Jindal: So I’ll actually

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Bharat Jindal: Look at the summary that this agent has generated for me.

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Bharat Jindal: Hello, John, as you are getting ready for the week to start. Here are the key areas of focus based on my analysis. Firstly, congratulations on being on target. To reach your quota aviso AI predicts that you’ll exceed your quota of 83.3 million dollars and achieve a whopping 105 million dollars. That’s fantastic news.

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Bharat Jindal: Your overall. America’s bookings are 18% below historical pacing to meet quota. Ted Williams is above pace and has a big deal at Voipa that is not in commit or upside deals. The commit. Pipeline has decreased from 37 million dollars to 35 million dollars over the past week for America’s.

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Bharat Jindal: Most of that drop came from southwest team and overall the pipeline has reduced by 22 million dollars. It’s recommended that a detailed review of the pipeline be conducted to identify the root cause. There are a total of 12 million dollars worth of deals at risk and 20 million dollars in upside. However, we should focus on pulling deals worth 2.7 million dollars. Now let’s do a quick review of customer calls that happened over last week.

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Bharat Jindal: Top competitor mentions were Google Aws azure. And the top negative aspects during the calls were span feature integrations, visibility, documentation for a detailed summary. Please check the weekly digest in the notification section of Miki, your chief of staff. Thank you.

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Bharat Jindal: So again, it’s bringing a summary of information. It’s not giving you news about what happened last week. But it’s saying, here are the 3 things you need to focus on. It also provides you with actionable notifications. So the same weekly summary is available here, and you can certainly drill into that.

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Bharat Jindal: If I was an Ae. Who’s focused on the quarter is going to give me very specific ways to increase engagement on accounts, take actions. So these are actionable notifications that are embedded. It’s gonna prompt me this AI agent will prompt me, for when would I would? I like to set a meeting? And I could basically provide that information. And through integration with Google Calendar or whatever the platform is for our customers of a choice, it’ll actually set the meeting up, generate a meeting brief

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Bharat Jindal: based on the customer’s history.

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Bharat Jindal: So think about efficiency and productivity in terms of providing all these things for you and doing these tasks for you. So these AI agents will actually do the work for you. The other way. To interact with these agents is by asking questions. So I could ask. You know, if I was a chief revenue officer.

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Bharat Jindal: this agent will automatically populate questions for me, based on the type of questions I might be looking to get answered. If I’m an ae. The questions could be more about, hey, update the Crm for me. What’s happening with my deal? Change the change. The forecast for these 10 deals within my commercial segment. Right? So any insight available within within a viso as an AI revenue operating system are also available within this agent I could ask questions like.

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Bharat Jindal: How does my pipeline look like for next quarter?

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Bharat Jindal: That that’s the pipeline for next quarter. I could even focus on current quarter pipeline.

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Bharat Jindal: How does my pipeline look like for current quarter?

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Bharat Jindal: So it’ll give me very detailed insights on pipeline for the current quarter, based on coverage quality spread maturity and multiple metrics. Right? So you can interact with the application and system in this fashion.

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Bharat Jindal: Some folks prefer this. Some folks prefer an avatar where you can have a real time conversation with the avatar. But think of this as a conversational interface, and if you’re in a you can ask it to update deals, change stages. The other part of the Avizo agentic framework are digital twins. A lot of our customers want to have their A plus players be replicated.

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Bharat Jindal: and as businesses grow, how do you take your A plus players and make them available across the board? So if you have an Rvp. For example in a mia, and that person in Benelux is a heavy hitter. But you want their insights available to everybody in the organization. Avizos AI. Agent framework can help you do that right, because we can feed all the information about that person.

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Bharat Jindal: All the calls they’ve done, the way they coach their teams, the way they operate with the customer the way they interact over emails. We can feed all of the knowledge set. And IP, that is customers. IP vis-a-vis emails calls

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Bharat Jindal: to these agents, and they will then make it that agent available for any rep in that organization, so that Rvb. Could be a personal coach available through this AI agent

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Bharat Jindal: for anybody to ask questions. So in this example, we’ve trained this AI agent using data from our CEO Trevor Templar.

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Bharat Jindal: And now you can ask any question of this agent on how to talk about Avizo in terms of its value. Proposition, how is a Vizo and AI revenue operating system? How is Avizo helping massive customers like Netapp, Lenovo, Wiz. Which is a massive company that got acquired by Google? And how did Avizo provide Roi for viz. Pretty quickly, which helped them beat their numbers and grow quickly.

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Bharat Jindal: So those are some high, level capabilities that are available within Avizos AI agentic framework. Some other things I would quickly highlight to you are things like cadences.

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Bharat Jindal: So think of cadences as as playbooks.

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Bharat Jindal: So every organization has processes. They follow in the go to market team

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Bharat Jindal: based on which week of the quarter they’re in, folks might be focused in week 1, 2, and 3 on generating pipeline. All those best practices tend to vary by organization, by segment.

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Bharat Jindal: But Avizo basically takes all of those best practices. And think of this as a cheat sheet. We provide all those best practices and playbooks embedded within Avizo as a product. A lot of our competitors provide these things as services offerings where you can bring in an Si or a Gsi and spend 6 months with them to implement. But in our case these things are embedded in our product. So in this example, I’m going as a 1st line manager, Olivia

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Bharat Jindal: right? Depending on where Olivia is in the quarter.

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Bharat Jindal: what she will see around looking at her business. So 1st of all. She’s getting a macro level view based on where her business is, where her team is. She can go into different sections of the application, and she could say, Hey, I really want to focus on slip deals and understand engagement levels on those deals, engagement levels being information available through email calendar information on how the prospects are reacting on these deals to the work that the team is doing.

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Bharat Jindal: So this AI agent will basically provide her with that proactively. And the information she gets is real time. It’s based on her team, and it’s based on the AI models and all the deep Ml capabilities Avizo has. She could even focus on pipeline trends and forecast accuracy.

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Bharat Jindal: If it’s week 12 of the week, 11 of the quarter, and she’s focused on customer renewal. And she’s worried about retention risk, because that’s what the company is looking at.

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Bharat Jindal: Then she will automatically be guided in that direction is, how do you look at

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Bharat Jindal: the overall retention risk. How do you avoid retention? Risk for certain accounts because renewals are coming up? So think of this, especially in the world of workflows which are getting static. Aviso provides these dynamic workflows that automatically adjust, based upon the persona based upon the person and based upon where they are in the quarter. So these things automatically adjust. Because now we have Llms, we have machine learning.

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Bharat Jindal: And in Avizos case all these things work through our AI brain that David talked about in an autonomous fashion. We only work with a particular customer’s data.

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Bharat Jindal: We don’t mix data. We don’t use large language models like Chat Gpt, that others do. Our approach is compliance centric. We just work with customers data in a single tenant environment.

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Bharat Jindal: and we use very specialized Llms small selective models which give us the same performance specific to that customer’s data. But models are picks and shovels for us. So all of this is happening while we’re leveraging the right models for the right task, and Avizos agent framework picks the right models for the right task and we are cutting edge. We’ve tried out the latest models

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Bharat Jindal: that come out from any of the companies like Meta or anthropic or others. And our data science teams are second to none in the industry. So that’s what you’re looking at is this notion of cadences which are playbooks and cheat sheets available to different individuals, and what gets surfaced to a cro would be different. What gets surfaced to a second line manager or a director would be different.

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Bharat Jindal: We also have these AI items available by persona. Some of our larger customers are saying, Hey, we want these segmented by personas, because what an enterprise rep does

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Bharat Jindal: is different from what a commercial rep does.

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Bharat Jindal: So, as a commercial rep. You might be looking at personalization and outreach.

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Bharat Jindal: And in this example, because the the days of bulk, email and mass outreach are gone, messages need to be personalized.

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Bharat Jindal: And so in this example, if a commercial rep wants to build pipeline, and they want to reach into their specific accounts, they can reach into these accounts in a personalized fashion, because we’re integrated with Linkedin.

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Bharat Jindal: So before this rep reaches out, they can get insights on the person they’re actually reaching out to.

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Bharat Jindal: So Avizo’s AI agents will provide very deep analysis around this person’s personality.

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Bharat Jindal: and also generate, think, provide recommendations on Do’s and don’ts. In communicating with Sarah in this example.

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Bharat Jindal: They’ll also generate emails for for the Rep or the Bdr. Or the Sdr. Right? So they’ll they’ll generate Powerpoints. They’ll generate conversation starters, and and so just to make the conversation more personalized. And we get the feedback all the time because we model this for prospects we’re working with that. It’s freaky because it’s quite accurate. So this is going to provide some options for the rep to copy and paste here, and the idea is not to provide you 100% accurate email. The idea is to give you something

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Bharat Jindal: that is there 80% of the times, right? So that’s the idea. And you can actually leverage Avizo’s capabilities by adding all these individual personalized contacts into a sequence through our sales engagement capability, because Avizo has an end to end platform for AI Ops and revenue Ops.

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Bharat Jindal: And these sequences can be email SMS, messages, phone calls and Linkedin. And you can actually personalize these in some organizations, managers personalize the emails. But there are AI sequences that get generated.

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Bharat Jindal: and then you can execute those sequences using a virtual Sdr the Sdr. Will actually execute the sequence for you.

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Bharat Jindal: I know it sounds freaky.

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Bharat Jindal: but the Sdr. Will then bring the most qualified leads to you, and show you where they are on the overall journey, and also provide you with very specific messaging

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Bharat Jindal: vis-a-vis. How you can work with those specific leads who are slightly more mature

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Bharat Jindal: if you look at what an enterprise ae does. They may be looking to do account planning.

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Bharat Jindal: and a lot of customers are asking us, hey? We want our reps to do account planning. We want them to be accountable for those account plans. But how do we provide them? Some automation and intelligence? Because account planning is a motion that every customer wants to do

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Bharat Jindal: so. This particular AI agent is an example of a multi-agent agent that actually orchestrates multiple sub agents, analyzes new sources, earnings, calls through vizos models and prompt engineering. 10 K reports with an existing customer. We analyze all the emails that are available from the team to show you where the account really stands. If you have access to conversations through our conversational intelligence module, we analyze all the calls

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Bharat Jindal: show you where different personas are in that account, and what you need to focus on and based on your product lines. This particular AI generates a value hypothesis and how to target that account, and it actually generates a pretty sophisticated account plan for you. And we get this feedback all the time that customers want to deploy this. This is amazing. It does take a few minutes right now. This was cached in the demo, but it’s a lot better than reps spending

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Bharat Jindal: 24 h or a week or 2 weeks of time. So if you look at this fairly sophisticated account plan

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Bharat Jindal: with almost 9 to 10 different sub agents, that this agent is actually coordinating. Right? So that is an example of

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Bharat Jindal: one particular process for an enterprise, a, you can do things like activate the team around an account plan. You can do health checks on accounts. All of those are available by personas.

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Bharat Jindal: And finally, I’ll show you one more thing, which is, we have a library of. We have hundreds of horizontal capabilities available as AI agents

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Bharat Jindal: where a Crm. I want to look at path to plan. How do I get to my

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Bharat Jindal: number this quarter by pulling in deals from next quarter that are within a certain range that may not be in commit. Maybe I can put some incentives in place to drive

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Bharat Jindal: towards that number. There are AI agents for reaching out to executives in your target companies by using messages from the likes of Simon Sinek and others thought leadership messages. There are agents for responding to Rfps doing research on companies that may or may not be public.

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Bharat Jindal: and and such. And again. These are completely configurable agents, right? So our customers, generally speaking, enterprise customers.

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Bharat Jindal: get what they get with the product, but they like to customize it and configure it for their business. So it’s very common for us to see our customers configure these agents and change these days, and one final thing I’ll show you is that we have these AI agents embedded throughout the product. So you can actually ask questions. Any English language questions of these AI agents, you could say, hey, around this deal that I’m looking at? Could you help me understand? What are some of the key pain points?

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Bharat Jindal: So if you’re doing a pipeline review, and you’re hearing certain things the deal is in commit. But you want to understand what’s actually happening with this deal. You can get an independent data driven perspective from this AI agent, where what I’m showing you is a pre-populated list of questions. But you can type any question in here and ask those questions. And this is this is the type of stuff viz. Absolutely loved, which is why they deployed it. They’re using us for activity and relationship, intelligence and many other parts of the product as well.

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Bharat Jindal: So with that, Julie, I’m going to wrap up here and see if there are any questions for us.

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Julia Nimchinski: Thank you so much for the Demo Bharat. And thank you, David. I’ve been following the company, for I think it’s currently 10 years or so.

1785
05:51:37.340 –> 05:51:41.620
Julia Nimchinski: since you were actually evangelizing guided, selling as a concept

1786
05:51:42.180 –> 05:51:45.520
Julia Nimchinski: before, it was a thing with Forrester

1787
05:51:45.660 –> 05:51:49.230
Julia Nimchinski: so really huge fan of the platform and your vision.

1788
05:51:50.092 –> 05:51:57.890
Julia Nimchinski: Let’s address a couple of questions from the community here. 1, 1 of the questions.

1789
05:51:58.846 –> 05:52:02.669
Julia Nimchinski: I’m seeing it’s basically around Roi. And

1790
05:52:02.840 –> 05:52:06.000
Julia Nimchinski: how? Yeah, how long does it typically take

1791
05:52:08.150 –> 05:52:12.789
Julia Nimchinski: for the for teams to get on boarded and seeing measurable roi.

1792
05:52:13.790 –> 05:52:19.400
David Watson: Yeah, the onboarding process is pretty quick and simple. We this last year we implemented Netapp.

1793
05:52:19.510 –> 05:52:33.499
David Watson: and as part of the the selling process. They asked us how much time was going to take, and we said, You know, typical implementations 4 to 6 weeks. And it was actually a stumbling block because they thought we were being deceptive. We implemented 3,300 users in 5 weeks.

1794
05:52:34.505 –> 05:52:44.489
David Watson: Whiz! I think we implemented them in 3 weeks. Contract to go live 3 weeks. So it’s it’s it’s quick, it’s fast, it’s low calorie from the customer perspective. Our team does all the work.

1795
05:52:46.160 –> 05:52:52.100
Julia Nimchinski: David in terms of competition. How do you even define it now? Is it, Clary? Is it like?

1796
05:52:52.210 –> 05:52:55.780
Julia Nimchinski: What? What are the players, and how do you differentiate.

1797
05:52:56.710 –> 05:53:17.258
David Watson: Yeah, I thought forrester’s report last year was very interesting. 1st off we were very excited to be recognized as the capabilities leader another interesting point is, there are no Crm vendors on that. Microsoft wasn’t on it. Salesforce wasn’t on it. Oracle wasn’t on it. They were there were players like us players like gong players like Clary.

1798
05:53:17.890 –> 05:53:33.330
David Watson: it’s a and I think how we differentiate and where we win is if there’s complexity. One of the the big value props of the Avizo platform is, we’re not bound by the hierarchies in salesforce. So if you want to do a product forecast, great. If you want to do

1799
05:53:33.609 –> 05:54:02.130
David Watson: unborn pipeline, we can do that if you want to look at partial opportunity overlays. If you have multiple crms. If he has disparate crms. You can only do that in the Avizo platform. So if you just need a simple roll up, we encourage our clients. If salesforce works for you. Great. If a spreadsheet works for you, great, if you just need a simple roll up, and your hierarchies are great, go with clarity. It’s a great tool. I’ve used it in the past, but if you have complexity, if you’re looking for AI driven insights, that’s where we win.

1800
05:54:03.840 –> 05:54:09.900
Julia Nimchinski: Amazing. And yeah, one of the questions we’re receiving here is.

1801
05:54:10.250 –> 05:54:17.639
Julia Nimchinski: how does real time guidance from Avizo actually show up for reps during a live deal cycle.

1802
05:54:18.870 –> 05:54:19.309
David Watson: Right.

1803
05:54:20.080 –> 05:54:27.240
Bharat Jindal: Yeah, there are many ways in which it shows up, Julia. One of those is the notifications I was showing at the beginning of the demonstration

1804
05:54:27.430 –> 05:54:30.139
Bharat Jindal: through our agent, Mickey.

1805
05:54:30.410 –> 05:54:38.959
Bharat Jindal: So these notifications show up, and you can then drill into these notifications of the rep and ask questions. That was one of the primary reasons Lenovo chose us because.

1806
05:54:39.310 –> 05:54:42.429
Bharat Jindal: you know, reps are busy with a lot of stuff.

1807
05:54:42.570 –> 05:54:46.580
Bharat Jindal: How do you help them find the right things to focus on

1808
05:54:47.010 –> 05:54:56.020
Bharat Jindal: right. Everybody talks about efficiency, efficiency, efficiency. But the game has changed with these AI models and agents. Now, it’s about productivity

1809
05:54:56.350 –> 05:55:20.350
Bharat Jindal: and a definition of productivity that I learned in business school was efficiency and effectivity and effectivity is a function of revenue and revenue happens when you focus on the right things at the right time, right conversations, right relationships. One of the differentiator I will quickly allude to in answering this question is, Abizo has been on a path to build an integrated platform for last

1810
05:55:20.590 –> 05:55:28.629
Bharat Jindal: 4 years, which includes relationship, intelligence. Ask me anything, capabilities, conversational intelligence.

1811
05:55:28.740 –> 05:55:36.149
Bharat Jindal: and overall forecasting and deal guidance. We’ve been doing that for a long time, and we’ve been on that journey to build all this organically.

1812
05:55:36.320 –> 05:55:40.720
Bharat Jindal: Other platforms in the market are approaching the same thing through acquisitions.

1813
05:55:41.370 –> 05:55:47.230
Bharat Jindal: and I was at Salesforce for about 8 years. Acquisitions can lead to different code lines and tech debt.

1814
05:55:47.640 –> 05:55:57.059
Bharat Jindal: All sorts of issues can arrive for customers. We do see some of our competition copying some of our ideas around Time series architecture we’ve had since day one.

1815
05:55:57.520 –> 05:56:18.210
Bharat Jindal: you know, those things sound great on marketing collateral. But then eventually, when it comes to forecasting and numbers and deals, rubber has to meet the road. So a lot of customers find that they go forward with pretty looking platforms that were designed for commercial and Mid-market or Smb. Companies. But then, when they have to report to Street, they have to have numbers that actually match.

1816
05:56:18.270 –> 05:56:38.290
Bharat Jindal: And that’s where they need the insights, because Avido has been focused, as you said, for the last 10 years, in a singular fashion on the AI and predictability, and that’s where we shine along with incorporating the latest and greatest. Like the Llms. We see models and picks and shovels. Our approach from a compliance standpoint. We followed this since day, one and

1817
05:56:38.290 –> 05:56:53.599
Bharat Jindal: others in this space. Try Chat Gpt, and we’ve had that feedback from our customers, and they’ve had to redo. But it’s an interesting space. We we have a unique point of view. And we. We have a unique vision in this space which has helped us so far. So that’s how I want to address that.

1818
05:56:53.600 –> 05:56:56.380
David Watson: Julia, thank you for the opportunity to come and present today.

1819
05:56:57.700 –> 05:57:04.329
Julia Nimchinski: It’s our pleasure. Let’s just address one more question and wrap this up. And the question is around

1820
05:57:04.650 –> 05:57:07.420
Julia Nimchinski: non-negotiable integrations.

1821
05:57:07.640 –> 05:57:12.349
Julia Nimchinski: In order to for this to run smoothly in a typical enterprise. Stack.

1822
05:57:12.760 –> 05:57:15.910
Julia Nimchinski: What is what would be your recommendation?

1823
05:57:15.910 –> 05:57:26.839
David Watson: Well, we always start with Crm, that’s our historical roots. Let’s ingest historical Crm, data into our algorithm. Let it do pattern matching. Let it develop a predictive model of what is closed. One and closed loss look like

1824
05:57:27.276 –> 05:57:51.850
David Watson: but then we’ve as brought mentioned, we’ve added other signals over time. So I would say, obviously, Crm is number one, number 2 would be email and calendar. It’s a very rich, juicy signal for deal success or failure. We don’t have to rely on the subjectivity of what a seller puts in Crm. We’re looking at like if brought has a deal and commit next month, and there are no meetings on the calendar, or there are no emails going back and

1825
05:57:51.850 –> 05:58:15.909
David Watson: forth, or there are no verbal conversations. Our AI is going to say brought. How are you going to close the deal with no meetings? How are you going to close the deal? If the only conversation you’ve had is a discovery call, or we’re hearing competitors, or we’re hearing objections. We’re not seeing negotiations. We’re not seeing close plans. We’re not seeing plans to implement. So I would say those 3 things Crm, email, calendar and conversations.

1826
05:58:19.570 –> 05:58:21.550
Julia Nimchinski: Amazing anything to add. Bora.

1827
05:58:22.350 –> 05:58:24.719
Bharat Jindal: Nope. David covered it quite well, as always.

1828
05:58:25.480 –> 05:58:28.740
Julia Nimchinski: Awesome. And what’s the best next step to learn more.

1829
05:58:29.501 –> 05:58:36.299
David Watson: Please feel free to reach out. My email is [email protected] and we’ll be happy to help you.

1830
05:58:37.190 –> 05:58:41.289
Julia Nimchinski: Amazing thanks again, and that brings us to the end of

1831
05:58:41.660 –> 05:58:49.210
Julia Nimchinski: our 3 day summit. Thank you so much to all the speakers, to all you watching, attending the recordings.

1832
05:58:49.330 –> 05:58:50.810
Julia Nimchinski: Sponsors

1833
05:58:50.950 –> 05:59:00.290
Julia Nimchinski: had a blast receiving a lot of positive feedback from all of you, and we’ll be back in June 19th for AI practice sessions.

1834
05:59:00.810 –> 05:59:12.009
Julia Nimchinski: We are also on a mission to build a stock market of scale. So just feel free to book a 1 on one with majority of the sponsors and speakers you’re seeing on the summit.

1835
05:59:12.440 –> 05:59:15.519
Julia Nimchinski: And yeah, stay bold and agentic.

1836
05:59:16.220 –> 05:59:17.000
Julia Nimchinski: Bye, bye.

1837
05:59:17.320 –> 05:59:18.460
Bharat Jindal: Awesome. Thank you. Bye.