Demo • RevSure AI — Full-Funnel GTM Agents in Action

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Julia Nimchinski: The Finder, welcome to the show! The founder and CEO of RevShare AI, and we will be seeing the demo.

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Julia Nimchinski: Super excited to host you. How are you doing?

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Deepinder Singh Dhingra: I’m doing great, thanks for having me, super excited to be here.

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Julia Nimchinski: Yeah, let’s dive into the demo!

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Deepinder Singh Dhingra: Perfect. So, what I thought I’d do is to give a quick introduction

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Deepinder Singh Dhingra: to who we are at Refshore, and then jump into the demonstration.

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Deepinder Singh Dhingra: In the introduction, I’ll particularly talk about how we think about the space of what we like to call full-fund aging

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Deepinder Singh Dhingra: the AI.

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Deepinder Singh Dhingra: for B2B teams, and B2B teams… B2B GTM teams with complex GTM motions, right from mid-mark to enterprise B2B companies.

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Deepinder Singh Dhingra: So what Repshow does is help these teams optimize for the end-to-end buyer journey, right from the anonymous visitor, through the different stages. This is just illustrative, whether any company has lead marketing, sorry, lead-based.

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Deepinder Singh Dhingra: account-based or PLG self-serve funnels, we adapt to that. And more importantly, hybrid GTM options, because in the complex world of B2B GTM and enterprises.

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Deepinder Singh Dhingra: have a mix of duty emotions, inbound, outbound, product-led, marketing-led, sales-led, etc, right? So we help them optimize for this end-to-end journey, right, from anonymous visitor to off-need, close one, and beyond.

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Deepinder Singh Dhingra: And more importantly, we help understand the synergies and optimize for the interactions between the marketing, the SDR, BDR, AE, partner motion across online and offline channels. Within that, we focus on three broad use cases using our Agentech AI.

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Deepinder Singh Dhingra: The first one is marketing optimization. How do we help better track, attribute, measure the performance of campaigns that are running by marketing? And then predict the performance so that we can then start suggesting recommendations, which campaigns to double down on.

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Deepinder Singh Dhingra: How to refine them in real time, how to figure out which campaigns can help you improve targeting versus nurturing, and then drive conversions and expansion.

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Deepinder Singh Dhingra: The second use case is pipeline acceleration, helping

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Deepinder Singh Dhingra: These teams prioritize the right leads and accounts based on predictive and anticipative algorithms that can anticipate which lead will convert to pipeline or account this quarter, next quarter, versus the next one, next quarter. So it’s very time-bound, because we all know that we have precious time, and we want to focus

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Deepinder Singh Dhingra: on the top opportunities and accounts and leads, rather than other ones that we can continue to nurture. And then finally, giving better predictive visibility.

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Deepinder Singh Dhingra: to these GTM teams as to whether they’ll meet their pipeline generation and pipeline coverage targets. If not, right, what to do about them, how to optimize marketing, and then how to drive the right acceleration. So it kind of serves as a loop

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Deepinder Singh Dhingra: That helps you drive better revenue productivity. Now, how does this work? We do this by deploying agents.

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Deepinder Singh Dhingra: at every stage of the funnel, across the marketing, SDR, BDR, sales motion, and partner motion.

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Deepinder Singh Dhingra: Each of these agents is not a disconnected agent. So what’s happening in this world of agents today is every agent is an isolated agent and doing its own activities. But imagine an agent in marketing that has no context about

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Deepinder Singh Dhingra: what’s going to happen downstream, or imagine an agent in the SDR-BDR world which has no context about what happened upstream, right? We avoid that by deploying team of agents. Now, how does this work, and why is this more effective? Because we bring together a full funnel agent-like AI infrastructure.

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Deepinder Singh Dhingra: That starts by integrating full funnel context, so we sit on top of the entire GTM tech stack.

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Deepinder Singh Dhingra: right from the marketing automation systems, to the CRM systems, to the paid ad tools, the ABM tools, chat engagement tools, sales automation outreach tools.

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Deepinder Singh Dhingra: buyer intelligence, de-animization, connected TV, advertising platforms, etc. Any data that’s in your product usage and your customer success intelligence tools. And thereby, what we have access to is the full funnel context.

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Deepinder Singh Dhingra: So not only do we know what’s happening in the marketing motion, we have the full funnel context of what’s happening across each of these motions, across all of the different channels, whether that’s online.

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Deepinder Singh Dhingra: or offline trade shows, conferences, webinars, events, etc, or online channels like Google, Meta, LinkedIn, and the other usual suspects that you have.

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Deepinder Singh Dhingra: Once we integrate this data, we harmonize it, so there’s a tremendous amount of context engineering that goes into harmonizing the data, dedupling it, dedupling it, creating the linkages. You know enterprise data is very, very messy, and when we start working with customers, we often find customer teams that have, like, 10-member teams.

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Deepinder Singh Dhingra: Trying to just solve this problem of bringing

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Deepinder Singh Dhingra: full funnel data context and data harmonized into one single source of truth, whether they use some data lake environments, like Snowflake or Databricks, etc, but they’re struggling with this. So our full funnel context data harmonization solves the problem so that we have granular visibility as to what’s happening at every lead, account, opportunity, campaign.

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Deepinder Singh Dhingra: channel as well as the overall business level. With that, then we can deploy our predictive AI engines and drive real-time orchestration and activation using our agentic AI. So our perspective and our key IP is the full funnel context, which is represented in the GTM data graph, and then the multi-level predictive AI engine that starts learning patterns

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Deepinder Singh Dhingra: So, based on historical data, we started learning patterns very contextual to the GTA motion of every customer’s, very specific to the customer’s GTA motion.

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Deepinder Singh Dhingra: across their full funnel and their full GTA motion, not just in a silo or isolated way. It understands all of the interactions and what’s predictive of conversion versus non-conversion, what activities

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Deepinder Singh Dhingra: Campaigns, leads, accounts are more effective than others.

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Deepinder Singh Dhingra: And that’s what drives our overall agentic AI flow, which is a quick schematic here, where we integrate the data, we harmonize the context into our graph.

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Deepinder Singh Dhingra: We run our predictive AI models that then serves all our AI agents that do reasoning to take action. I’m going to take a quick pause here before I move into the demo. Does that sound good, Julie?

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Julia Nimchinski: Yeah, it sounds wonderful, and we have a lot of people watching here. Let’s just, yeah, let’s dive in.

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Deepinder Singh Dhingra: Perfect, great, thanks for that. So, how does it all work? I’m gonna unpack this journey, because the how is as important as the what, because you first need to trust data. AI is only as good as the context that it receives, and that context that it receives is based on the data.

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Deepinder Singh Dhingra: that is flowing in through the different systems in the business, in the messy world of your business, right? So, what we do is we first start by integrating data, and what I’m showing you is how we eat our own dog food. This is RevShore on RevShore. This is our instance. So, for example, we integrated data, in this case from the marketing automation system, CRM,

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Deepinder Singh Dhingra: our G2 Sendoso first-party pixel that tracks. We do cookie-less tracking, fingerprinting, also that tracks anonymous visitor journeys. LinkedIn, we can also Google, Twitter, etc. These are our channels that we focus on.

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Deepinder Singh Dhingra: our B2B that helps us dearize. We have several data partnerships, and our B2B being one of them, including LimaData, Bitscale, Autobond.ai, etc, that helps us enrich data further, right? All of this data gets integrated, and these are just some examples of the data integration that other customers and other

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Deepinder Singh Dhingra: companies. We’ve integrated over 40 in this data model.

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Deepinder Singh Dhingra: which is our… the data graph, right, that harmonizes and links all these accounts, activities, campaigns, campaign members, visitors. For example.

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Deepinder Singh Dhingra: This is the Fusion data graph that’s integrating data across all of the, in this case, leads and contacts across the marketing automation, the CRM system, social engagers, dearized visitors that are net new that you might not have known about in your CRM and your marketing automation system, all of the resolution of campaigns that are coming from all of the

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Deepinder Singh Dhingra: channels, including, you’ll see, it’s not just marketing campaigns, it’s sales campaigns, it’s SDR, BDR campaigns, referrers, UTM-based campaigns, all getting mapped. And what then we have is thousands of attributes of data that’s ingested into our data graph.

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Deepinder Singh Dhingra: at the visitor level, account level, opportunity level, etc. What this does, then it serves the foundation of

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Deepinder Singh Dhingra: stitching together the most granular level journey. So now, I’m showing you what the context, what is the context that powers the AI? So for each of these leads, and I’ve kind of now switched to a sanitized environment, just to respect confidentiality, right? So here, for example, for each of the leads here.

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Deepinder Singh Dhingra: right, where it’s contextual to your GTA motion, it’s for your stages, it’s for each company’s GTA Motion. We’ve been able to stitch together all the activities, campaign touches, and funnel movements across all of the sources that we’re getting integrated. So, every lead’s journey is captured, as well as the context of each of those interactions.

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Deepinder Singh Dhingra: Right? To serve the full funnel context. Now, and you have this for every lead in your GTA Motion.

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Deepinder Singh Dhingra: Not only at the lead level, or the contact level, you see that different leads have different journeys, different number of touchpoints that they have interacted with. What’s very important is that in the upper mid-market and in the enterprise, what you would appreciate is that cross-channel nurture is one of the majority of the GTO motion. Yes.

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Deepinder Singh Dhingra: you have warm outbounds and pure cold outbound prospecting, but cross-channel nurture is as important, because a lot of the leads, contacts, accounts have been engaging with you for a while, and nurturing them and understanding the influence of them is very important. Now, all of this context.

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Deepinder Singh Dhingra: not only at the lead level, but also at the account level, is being fed. So I’m just going to switch to another example at the account level, where I’m looking at accounts.

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Deepinder Singh Dhingra: Why am I showing this again? The reason I’m showing this is this is what’s needed to build good aging AI infrastructure. Without the full funnel context, without understanding the journeys.

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Deepinder Singh Dhingra: across all of the interactions, account-level, contact-level interactions, across all of the contacts in a particular account, as an example, right? You won’t be able to build good agentic AI that has full funnel context and then can do reasoning.

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Deepinder Singh Dhingra: To help you, to help you drive action. Once this is done, our AI is also starting to learn the macro GTM motion.

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Deepinder Singh Dhingra: Right? That’s happening across the account. The key journeys that are driving conversion. All of this is being shown to you, but this is what’s feeding our Agentic AI. This helps our users understand what’s actually being fed into our… into our Agentic AI models.

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Deepinder Singh Dhingra: Also, predictions that happen at different layers, whether at the overall business layer, at the campaign layer, etc. And then finally, now you can start building agents contextual to every region, channel, persona.

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Deepinder Singh Dhingra: stage of your buyer journey and stage of your funnel. And that’s where the agent take…

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Deepinder Singh Dhingra: environment, and the agent builder starts coming into play. So now that you have all of this context, you can start building your own agents. You can start and build new agents from scratch, or you can start from any one of our templates. The key is that in the enterprise, it’s not just one or two agents that you need. You need the ability to orchestrate a team of agents.

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Deepinder Singh Dhingra: Each for different specific actions, but each that is built on a common understanding of the context and the data and the full funnel, what’s happening across your full funnel. So, for example, let’s say it’s an agent where I want to send

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Deepinder Singh Dhingra: emails to all visitors that were de-anonymized on my website. I can just go into this template, right? I can schedule this manual, scheduled event-based, so we support real-time orchestration. And this agent.

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Deepinder Singh Dhingra: flow can be customized and edited per your requirements. So, for example, you can build your own prompt, right? Say, hey, look at all of the pages visited by visitors, and send them an email based on their interactions, their unique interactions.

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Deepinder Singh Dhingra: And once I run this, right, I’m not going to run it now in live, you can add your own knowledge base, so you can add more context about your case studies, about your product marketing and your messaging playbooks.

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Deepinder Singh Dhingra: About different aspects about your product, different functionality you can add into the knowledge base. All of that, along with each lead’s journey, each account’s journey, each account’s…

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Deepinder Singh Dhingra: interactions, etc, are fed into the AgentTech AI. Once that happens, now, when I run this personalized email at this DMIZ visitors agent.

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Deepinder Singh Dhingra: That will then start triggering off emails very quickly that, you know, I just ran some time back.

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Deepinder Singh Dhingra: These emails are automated and run by our agentic AI layer. This is just one example. Now you can start building further agents for different stages of funnel. You can build an account research agent.

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Deepinder Singh Dhingra: right, as an example, and this will give you… this will give you context about each account. You can… you can further configure

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Deepinder Singh Dhingra: To the specifics of your GTM motion. You can say, hey, what is your ICP? What is your product marketing collateral? You want to redact PII from things, so security, privacy is very important in the Agent EKI for

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Deepinder Singh Dhingra: for enterprises, and that’s what we also support. So I’ll stop here, just to take any questions. I just want to give an overview of our overall AgentKI infrastructure. Trust this was useful and interesting, yeah.

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Julia Nimchinski: Amazing session. We, unfortunately, have only one minute, but, one of the questions we received here

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Julia Nimchinski: Here’s a good one. What’s typical time to value?

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Julia Nimchinski: How soon can we expect actionable insight after implementation?

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Deepinder Singh Dhingra: Yeah, so our overall onboarding is 4 to 6 weeks.

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Deepinder Singh Dhingra: Right? Which is led by our customer success team and our data team that helps you first set up the connections, and then we… once the connections and the ingestion is done, we do a configuration.

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Deepinder Singh Dhingra: That again is a guided configuration, and our… all our customer success team is ex-marketing and GTM folks, deliberately, so they understand the context.

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Deepinder Singh Dhingra: Right? Of, of the business, yeah.

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Julia Nimchinski: Awesome, and where should our community go to get a test drive?

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Deepinder Singh Dhingra: You can go to our website, RevTore.ai, or you can directly email me, deepnder at revfter.ai.

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Julia Nimchinski: This is amazing. Thank you so much. And we are…