Full Funnel as a System of Action with RevSure

Julia Nimchinski:
Next up is Pindar Singhdengra, founder and CEO of Rapture AI, and who was the first one, I think, in the industry, to deploy agents’ forms effectively. And we’ll showcase today Full Funnel System of Action. Super excited for this one, always love your sessions. How have you been Defender, and what’s new?

Deepinder Singh Dhingra:
I’ve been doing great. Thank you so much, Julia. Great to be here. It’s all about agents deployed for the enterprise. where we come from. So, just to kick it off, hi everyone, I’m Dipinder, I’m the founder and CEO of RevShore.
We are on this journey to build the only enterprise-grade revenue system of action for B2B, enterprise revenue teams, across The marketing, the… sales, the SDR, BDR, and the customer success motion.
So when we mean full funnel, we mean across the end-to-end GTA motion, not just a marketing motion, not just a sales motion, not just prospecting and beyond, or not just You know, hey, for sales teams, or for SDR teams, etc. Today, you know, I’ll be talking about how do you think about going from systems of record To systems of action.
We all know that for the past 100 years, revenue was run by human beings. But the current GTM tech stack was never built for agents. It was built for humans.
How would you go from a GTM tech stack built for human beings, built for specific user personas, specific teams, to a GTM tech stack built for agents that can actually act right from anonymous visitor to close one revenue and beyond, and that’s what RevShore really focuses on.
What’s happening today, if you think about the past couple of decades, is that There are a number of point solutions. We have all heard about the GTM Frankenstack, the text crawl, and GTM, but essentially, B2B GTM is broken. Across fragmented buyer journeys, fragmented and disconnected tools, messy data, siloed teams.
Each of the tools that were built in the past, whether that’s the CRM system, the marketing automation system, the buyer intelligence tools, the sales automation prospecting tools, the paid ad tools, ABM intelligence tools, etc. In and of themselves, they are great tools.
But they only focus on specific parts of the GTA Motion, they’re only specific on serving specific needs of different user personas and teams, and therefore, they create their own systems of record.
Each of these systems of record is very deep in its own perspective, and the part of the GTM funnel that it kind of addresses, whether it’s, hey, attracting visitors, converting them into leads or accounts, and nurturing them into marketing qualified, then nurturing them into sales accepted, sales qualified, etc.
Whatever GTM ocean you have, whether it’s an inbound, outbound, product-led, marketing-led, lead-based, account-based, PLG, community-led. In fact, the kind of customers that we work with. They all have hybrid GT emotions.
I have not seen, in the enterprise a pure PLG company, or a pure ABM company, or a pure lead-based company, every enterprise that has multi-region, multi-channel, multi-segment, multi-product. GTA Motion. hybrid GTM motions are operating in parallel, right?
And so what ends up happening is that even though we try to connect these tools to different integration technologies, these are more band-aids than solutions. And in a world of agent-type AI, this actually causes more problems than solutions, because it creates more noise. It amplifies the noise.
When an agent doesn’t have full context, you actually give that agent a reason to go rogue. Because at the enterprise level, if you’re going to act on millions of contacts and execute billions of actions.
at enterprise scale and enterprise complexity, if you… if the agent doesn’t know the full context, if the SDR agent that you probably You know, a best-of-breed agent that you bought, or an inbound qualification agent that you bought, a best-of-breed agent from some vendor. Right?
Does not have the full context of what’s going on in the marketing motion, or what’s going on in the sales motion, or whether that visitor actually came to me through a website, expressed some intent, or went to my trade show, my webinar, my field event, etc. Right?
They’re going to cause the same problems, because they’re going to act on incomplete data. And although you might automate a lot of outreach and qualification and campaign execution, they’re going to end up creating the same siloed problems, you know, and lead stagnation. not great pipeline quality. Why are my leads not converting?
Why is my pipeline not progressing through the stages? Those same problems are going to recur, but this time, actually double… at double the scale than what was there earlier.
So how do you take this existing broken B2B GTM tech stack in the enterprise, and make it agentic AI ready, and convert these individual systems of record that have deep but partial data into systems of action.
For the enterprise, you might say, hey, you know what, we’re going to rip out all of this tech stack, and we’re going to have some, you know, one technology rule at all, AI-native CRM, or AI-native marketing automation system, but that’s not practical, because heterogeneity is the fact of life in the enterprise.
Not because of just the DTM tech stack, but because of the number of channels. We work with enterprises and above mid-market companies that have 50 channels. Each of those channels are generating data, and generating signals, and generating interactions from their individual data sources, so that heterogeneity is going to stay.
So our perspective is to convert these systems of record into systems of action. One needs to think about… A harmonized context layer. Without a harmonized context layer, And a unifying substrate that can unify the data intelligence across the disparate and fragmented GTM tech stack, which is going to continue to operate. Right?
In their specific parts of the DTMotion. your agents will not have full funnel context, and therefore will not reach their full potential. And you won’t have a coordinated system of action that will share the same context.
Every agent Imagine every agent, whether it’s your prospecting agent, your campaign refinement agent, your deep funnel optimization agent, your deal intelligence, lead account research agent, nurturing agents, etc. Having the same context of the lead account opportunity, the past interactions. The… the movements, the decision traces, etc.
And that’s what our perspective is. You need to create a harmonized context layer as a unifying substrate on top of the existing GTM tech stack to convert your current systems of record to systems of action. Now, how does this context layer work? What the context layer does.
is ingest and harmonize the full business and GTM context, as well as all of the events, interactions, decisions. GTM funnel stages, your GTM lifecycle, etc. So imagine the millions of visitors, leads, contacts, opportunities, hundreds and thousands of campaigns that one might be running at the enterprise level across multiple CRM systems.
multiple paid ad systems, ABM tools. We work with customers who have 65 systems. We work with customers, on an average, who have 20-plus systems across the GTM ocean, and we are harmonizing all of that data. Getting all of the business context, the ICP segmentation, the key buyer personas, the buying group definitions.
The definitions around the campaigns, the channels, the products, the regions, but also getting all of the events, interactions, and decisions that are taking place. Whether the decisions, first-party interactions, second-party interactions, online, offline interactions, product usage interactions, conversation calls. Paid media interactions.
Today, you do find solutions that will focus on the prospecting to conversion, which is just from, okay, hey, I identify a lead, I identify an account, I do some research, I start prospecting, and then try to kind of take that through the, generate a pipeline, then convert that.
And you find solutions that are very good at the… on the marketing side, that are focused on, hey, let me get all of the interactions around paid media, and maybe online, offline. But there is no solution today that can integrate end-to-end.
And the reason I’m talking about this is because for you to achieve full funnel optimization of your buyer journeys, one needs to bring the context across sales and marketing. Because if one doesn’t, then the same problems of the past, sales and marketing misalignment, leads not converting, leads stagnating, decreasing ROIs.
pipeline quality not keeping up, win rates going down, less than 50% of sales rep meeting the quota is going to continue to exist, although we might keep automating more and more. So, bringing all of this into one context layer, an AI engine that can orchestrate autonomous, as well as coordinated agentic actions.
That is the missing substrate for most enterprises. Right? Most enterprises, what they like to do is either have a rip and replace strategy. In the enterprise, actually, that doesn’t work, right? It might work for SMBs. So if you’re an SMB, you could try a rip-and-replace strategy with one, you know, system that can try to do it all.
But you really need to have a more practical approach where you’re actually converting your current systems of record in your GTM tech stack to become agent AKI ready. Now, how does this look? It looks… it starts first by… bringing all of the context across your fragmented GTM tech stack, across the marketing motion, the SDR, BDR motion, AE motion.
And I’m going to show this to you for real, and how this works. Harmonizing all of this context. And we are big believers in eating our own dog food, right? And we do this at scale. Right? We do this at enterprise level, right? Where the complexity, right, can really be daunting, but can be solved for.
So, I’m going to switch to actually showing you how this works, right? Here is… an example where we are essentially harmonizing context across the GTM tech stack. So we’re integrated, across the CRM system, the marketing automation systems.
right, you could bring in activities from G2 reviews, etc, Sendoso, the gifting campaigns, first-party pixels, the paid ad campaigns, de-anonymizing visitors on the website, fingerprinting, tracking all of the anonymous visitors to first conversion and beyond, Twitter, Google Search Console. Bringing across your demo tours, right?
So we use demo tools, ourselves, right? Bringing data across that, bringing the Google Analytics data, search interactions data, etc. Bringing all of this, we have over 22 data sources integrated.
Once the data sources get integrated, it’s not just about the integration, it’s about bringing this and harmonizing the data into… Or one common data model. Right? Let me just refresh the screen. Right?
Bringing in this to one common data model that can then allow you to build the overall context graph, do the linkage resolutions, do entity resolutions. do all of lead to account to contact mappings, etc. Once you have that, what that sets the stage towards is your ability to… Bring together, for every lead, account, and opportunity.
bring together all of the interactions that are happening, right? So once you’ve organized the data for every lead and account, and, just to show this to you, all of the funnel activities, campaign touches, funnel movements across all of the different data sources, right?
the unique timeline journey of interactions across the sales motion, marketing motion, SDR, BDR motion, customer success motion, for every lead, for every account, every lead has its own unique journey. The details of those interactions, right?
Not just one, one set of interactions across online and offline channels, not just the interactions themselves, but all of the details of those interactions across all of the data sources, across your GTM tech stack, being integrated. Further. Being able to extract more signals. Right?
From conversations, from emails, being able to infer what are those buyer personas, being able to bring together all of the firmographic, technographic interaction attributes, the time traces of events.
as well as all of the GTM funnel lifecycle decisions that were taking place, bringing together all of the predictive pipeline propensities that, based on the behavior of every lead, based on its unique journey. What are the propensities?
So extracting all of the signals at the lead level, as well as extracting all of the signals even at the account level. Right? Similarly, aggregating all of the interactions at the lead, account, opportunity, campaigns level, this builds a foundation of the context layer.
Once you have the context layer, which is unified across the GTM tech stack, then one can start building agents on top of it. Now, your GTM tech stack is harmonized, all of the systems of record have brought into one context layer, and an enterprise context layer that appreciates the nuances of your business model.
of your lead lifecycle definitions, of your, of your operating calendars, of your buyer personas, of your specific attributes, dimensions, etc. Once that appreciates that, then you are ready to Run agents on top of that. And now the difference is, these agents. are not agents that are individual agents. This is a team of agents.
each agent So, it’s built on the same context. Every agent shares the same context, but now you can build a team of agents across your GTM tech stack. across the context that’s embedded in each individual system, because now it’s brought together, right?
So you can build agents for account research, you can build agents for lead scoring, you can drive agents for prospecting, you can drive agents for up funnel, top of the funnel campaign refinement, reaching out to ICP visitors on your website, reaching out to people who have requested a demo. All of the signals are brought together, right?
Each of those agents can get orchestrated and configured further. Now, this doesn’t have to be… the agentic orchestration layer doesn’t have to be in one environment. It could be… and you could use an agentic orchestration platform.
It could be one of those hyperscaler agentic orchestration platforms, it could be Claude, it could be from Google, ChatGPT, etc. But the idea that made this impossible, that made this team of agents possible, based on the shared same context. was your ability to bring… build this unified substrate of context across your GTM tech stack.
Because once you have this, then you can build these agents, you can expose these agents, you can build agents on top of this. Right? Real-time agents, scheduled agents, all of the whole agentic workflow can be orchestrated with the whole AI and intelligent logic within it. But you can also expose agents into external environments.
You can expose the context layer to, for example, Claude, where all of the context within your context layer is actually exposed as tools. So now, any external agent can access all of these tools to be able to further drive agenting workflows on top. Right? And that’s what helps you convert systems of record that you have.
All of these systems of record that you have, which are… have their own deep but partial data, each of these tools is great for its own purpose. Some tools… different tools are doing different aspects of your GTA motion, but none of these tools can drive coordinated, agentic actions across your full funnel.
And so, once you have that, then you have a real system of action. with the ability to deploy teams of agents, you are harmonizing contacts from your GTM systems. From external data, harmonizing that context into a unified substrate. You’re further overlaying AI learning engines that can help learn signals at scale, right?
Because some signals have to be learned at scale across the GTM motion, and then that drives a system of action where you can deploy agents. Across your funnel, as a team of agents and a team of coordinated agents. So, that’s what I had to show today. Yeah.

Julia Nimchinski:
Super impressive, Depender. Always love your sessions for the reality of what’s actually going on in enterprise deployments. I’m curious from the perspective of, you know, your experience actually operationalizing this shift in enterprise. obviously, yeah.

Deepinder Singh Dhingra:
Sorry, go ahead.

Julia Nimchinski:
Yeah, how do you see the ultimate, you know, futuristic stack? So…

Deepinder Singh Dhingra:
My perspective is that there’ll be… there’ll be a bifurcation in the stack.
There will be companies that will try to rewrite the architectures across the GTM tech stack, and those will be companies that Either they will adopt some vendor who’s trying to kind of do the complete end-to-end GTM motion orchestration, because acting on incomplete context is no longer an option.
Because agents are not limited by us human beings, right? CRM was created to help sales teams log deals. Marketing automation was created to help marketing teams execute campaigns and manage their leads, accounts, and prospects, right? Conversation intelligence tools was built to help sales teams review calls, and the leadership review calls.
Now, each of these tools was built with a persona in mind, with a specific team member… with a specific team in mind, and for a specific stage of the GTA motion. Right? But that ended up creating its own problems, because now, on an average, we have 23 tools across the GTM tech stack.
It created problems of silos and all of the misalignment problems, revenue leakage, pipeline leakage, etc. But agents, we have the unique opportunity that agents are not limited by the… by the individual teams that they’re a part of, right? They can adopt the persona of the team, but then they can go across to understand context, right?
So our perspective is there’ll be companies who are actually going to try to rip and replace, but that will mostly work for SMBs and mid-markets. What we are seeing in the enterprise, the architectural approach. is to create an ecosystem of technologies, but create one context layer.
So while enterprises will use multiple… if you think about the three layers of agentic AI for revenue. There’s the foundation models, and all foundation models are converging every 12 months. Right? That’s at the bottom layer.
Then at the top layer, there’s the agentic execution layer, and the agentic orchestration layer, and the companies like Claude and other tools that are helping orchestrate that, and that layer also getting fragmented, and also getting commoditized, so people have different options. Right?
The only layer that is remaining, and that is not yet actually built out. And companies are trying to do this internally, at least enterprises are trying to do it internally, right? We had a customer, for example, who was trying to solve this problem for 10 months. Right?
With a mix of teams, data engineering, data ops, marketing ops, data science, analytics, digital marketing, rev ops, right? Coming together to try to bring the context of the full funnel. And, you know, it’s really tough to build this, because you need to have a very dedicated focus.
It requires you to understand data harmonization, entity resolution at lead account, optionity campaign, across the multiple channels and the multiple systems. You need to do semantic data standardization, linking of leads to accounts to campaigns to campaign members, fingerprinting, all of that needs to come together, right?
And so, the primary architectural approach that we see in the enterprise is to try to build a context layer Right? In between the GTM tech stack, because it’s not going to be possible to replace. For enterprises who have heavy investments. in workflows and processes that are already part of your current GTM tech stack. Right?
You can’t just rip and replace that GTM tech stack. What you have to do is to build a layer on top of that that enables the GTM tech stack to act as a system of action, right? While every tool will continue to build agents on top of it, you need a coordinated substrate to help you build systems of action.
That’s our thought process, yeah, and that’s what we are seeing.

Julia Nimchinski:
Beautiful. Community is asking about the governance element. Can you share your perspective here?

Deepinder Singh Dhingra:
So governance are… there are two aspects of governance. One is the data governance, which is all to do with privacy. And, you know, and confidentiality, etc. So, right, there’s a lot of focus in the enterprise around GDPR, etc, and we, like, we have the required guardrails. We can do PII redaction, encryption of PII data. Right?
Using only non-sensitive PIA data, etc. That’s one aspect on the governance side. And then there’s the AI governance, because to be able to enable these these systems of action. What one has to do is to, is to make sure that the data that… if you… if you… even if you’re sending data for agents to execute, summarize.
recommend, take next… next best actions, and recursively learn, you have to support PI record action. For example, we do… we support that. But also, you have to kind of start thinking about, you know, not using… not necessarily using open source models. Using models that are vetted within the enterprise.
You have to think about no-prompt caching, no-prompt logging. no data about that goes into the LLM model for training, right? No… no fine-tuning of the LLM models or the training of the LLM models based on the context that you feed their LLM models, right?
So that you have to take care of those aspects, but you want to capture the agentic decision traces. Right? So for systems of action to work, you need one central AI brain because you need to capture the decision traces and the AI reasoning traces back into your context layer.
For example, at RevShort, right, when we run our agents, our systemic agents, one of the things that’s feeding our campaign touchpoints are what are the agents that are acting on my leads and accounts. So we bring back our agents’ information and the decisions that the agents are taking and acting on into our own context layer, right?
And that’s a very important thing, because, you know, you will need that one brain to coordinate these systems of action.
So I hope I answered the question both on the, both on the governance, on data and AI side, but also the importance to kind of being able to capture the agent transition tracing back into your context layer, while protecting that there’s no prompt caching, prompt logging.
And there’s PI redaction when information is sent to the LLM, so that the LLM can reason and take action as part of the agentic reasoning logic, yeah.

Julia Nimchinski:
100%, and community is also asking about, the integration or, you know, how do you work with CRMs and data warehouses?

Deepinder Singh Dhingra:
Yeah, so we support a whole list of integrations, so if you go back… so this is the data graph, but if you go back to the data sources, we support integrations across sales… the CRM systems like Salesforce. Dynamics, Pardot, marketing automation systems like Pardot, Marketo, etc.
Because we work in the enterprise, customers very often have multiple CRM systems. multiple marketing automation systems for different businesses, for different regions, coming through acquisitions. We are probably the only company who was able to harmonize context across multiple CRM systems, multiple marketing automation systems.
From data lake environments, across, like, whether that’s Databrith, Snowflake. BigQuery, etc, Redshift, right? And we essentially are, you know, the ability… because at the enterprise, you could even have multiple data lake environments where context is coming from, right?
So we support the whole gamut of enterprise, the tech stack that is within the enterprise, yeah.

Julia Nimchinski:
Can you speak to some of the customer stories that excite you the most? Somebody who actually deployed these transitions to systems of action?

Deepinder Singh Dhingra:
Yeah, so we work with customers like Zscaler, Normal Security, etc, and with all of these companies, right, the starting point is to build the context layer. So there are companies there where we’ve integrated over 20 systems, billions of touchpoints, millions of leads accounts, hundreds and thousands of campaigns, right?
And then what happens when we integrate the full context layer, then that unlocks multiple AI use cases on top. Whether those use cases are to kind of help to understand the impact of your marketing campaigns and marketing channels down to pipeline and revenue at the bottom of the funnel. Right? To revenue.
And then, you know, whether that is for personalized outreach, which uses the full context of the interactions with… that’s coming from visitors, leads, etc, right? And the results are… improvement in conversions, improvement in GTM ROI, improvement, obviously, in productivity, right?
Often we see, like, 40%, 50% improvement in productivity across the board. But also, there have been teams, internal teams, who are trying to build this, and it takes a long, long, long time to build this. So we also repurpose the efforts of the internal teams on more value-added activities while we take care of the context layer.

Julia Nimchinski:
And lastly, what’s on the roadmap? What are you allowed to share? What excites you the most?

Deepinder Singh Dhingra:
Yeah, so we are, we are building out the full range of AI agents across the marketing SGA BDR AI motion. We’re building out the primitives, which can be exposed as MCP for command like CLI support and API. We already have MC support, CLI support, but for every agentic action.
system action, whether that’s updating your CRM, executing campaigns, orchestrating journeys, executing next best action. We already support a lot, but our vision is to kind of support the end-to-end GTA motion across that. And on the context layer, we continue deepening our context layer with more integrations, but more importantly.
As we deploy more to enterprises, we are getting just better and better in the whole context graph. how do you build context graphs, right? And what information elements you capture in the context graphs? So that’s a huge area of focus for us, yeah.

Julia Nimchinski:
I’m excited to be part of that journey. And where should our community go? Your website, contact you personally. What’s the.

Deepinder Singh Dhingra:
Yes, absolutely. You obviously can… to learn more about Repshow, you can go to Refshore.ai to contact me. You can send me a direct email at dandar at revshore.ai. Would love to chat about how we’ve built this and what our plans are. Great. Thank you.

Julia Nimchinski:
Thank you so much again.