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Demystifying the conversations we're already having here at RRE and with our portfolio companies. In each episode, your hosts, Will Porteous and Raju Rishi, will dive deeply into topics that are shaping the future, from satellite technology to digital health to venture investing and much more.
Raju: Okay. Hello, listeners and viewers. Welcome to another episode of RRE POV. I'm Roger Ricci, and I'm joined by my partner, Will Porteous.
Will: Hey, everybody.
Raju: At RRE, we're a well-established venture capital firm, and we've raised eight venture funds and two opportunity funds over the span of 32-plus years. And as such, Will and I know this really well.
He actually spends a lot of time talking to LPs. We've had the absolute pleasure of having worked with some of the best limited partners across institutions, corporations, and family offices. And As Will and I both know, over the past year or so, we've been asked by three or four of our largest LPs to help them think through their AI strategies.
Um, so this is what this episode is. This one goes out to the ones we love, our LPs. Which reminds me of that R.E.M. song, and I kind of feel like I need to acapella a short riff of this.
Will: Are you gonna sing for us, Raju?
Raju: I think I am. I am. Uh, just totally acapella. All right. This one goes out to the one I love.
This one goes out to the one I've left behind. A simple prop to occupy my time. This one goes out to the one I love. Fire! Anyway. Okay. All right. I had to do it.
Will: Absolutely. With gratitude to, uh, to Michael Stipe and to our LPs out
Raju: there. Absolutely. Sorry for that diversion. You guys know I love music. I just can't.
I get... Okay, w- back to the main programming. So what do LPs do about incorporating AI into their workflow? This is... You know, recently, I've had intimate one-on-one sessions with several of our LPs, and I usually walk them through the following. And by the way, every one of these guys is overworked, everyone. I mean-
Will: Yeah, I think that's the, the most important starting point.
I, I got a lot of sympathy for our LPs sitting on the other side of the table trying to manage all the inflow of new managers they're looking at, trying to stay on top of their existing fund managers, trying to think about their portfolio construction model, trying to do it all with a really small team.
It's, it's a lot. They're unsung heroes.
Raju: Yeah, and, and you know, like, we feel that as well. Like, there's kind of a, like, small divide between limited LPs and GPs. Small divide. I mean, we're both in flux with tons and tons of, you know, sort of opportunities. We have to evaluate them. We've got to go deep into diligence, and it's just gets overwhelming.
And a lot of these organizations just aren't like, you know, kind of flush with analysts and associates and, you know, principals to help do all that work. So I think AI is gonna be a really important tool. So, um, what I want to start with, Will, is just, um, a bit for our LPs for them to understand today, just today What are the LLMs actually good at, and what are they really overhyped at?
So yeah. Yeah, maybe we can just like, um, go into some context around those pieces and, and I know you u-used it as well, but like what it's good at, you know, if you take multiple files, including long text and, you know, just a bunch of PowerPoints, spreadsheets, whatever, and you need to summarize it, or you need to draft initial content, it's not so bad at that.
You know, you can think about just the analysis of a particular company or a space. Like you're getting into a space, you're, you're looking at a sector and you're, you know, copied like five or six key documents and you can throw it into one of these LLMs and it can give you some feedback. Um, I think it's pretty good at that.
What do you, what do you think? What's, what's your feedback on that?
Will: Oh, absolutely. Yeah. Uh, summary and synthesis and kind of k- helping you get to the heart of the matter and learn quickly. Yeah. I mean, I, I think, I think we get leverage out of it that way. I think our LPs can too.
Raju: Yeah. And I think most people are starting to experiment with that or use that in a, a more rigorous way.
The second that it's good at that, you know, people are le- a little bit more reluctant to use, I would say, but maybe they should start, right, is, and this is on a personal level, is just, you know, AI-based workflow automation. Simple stuff like AC NAC. You know, read an email, draft a response. Don't send it.
Don't, don't send it. Do- don't have it send. Um, although I, I know people who've tried this. You know, they just have, like, automated, like, the response function, and it will bite them. Every once in a while it bites them. So I, I do think that's a tool. I, I've tried it. Have you tried it?
Will: I have tried it, and, you know, the
I, I, I tend to get these really long responses, responses that go kind of ramble and go, go kind of all through all the permutations without necessarily getting to the point. So it has, it has to be fine-tuned. Um, you have to invest the time to make it work for you. But I, I agree, and, and, you know, email workflow is such a time sink, uh, for all professionals.
So it- there's a gift there in terms of productivity, to be sure.
Raju: Yeah. I, I think so too, and, you know, I, I think it's better over time. Um, but I, I don't think it's ready to actually make decisions on your behalf. It can draft, and drafting is actually quite good. And so that's something that I think it's good at.
The last piece is something that is, uh, really important in the enterprise, which is something called RAG architecture, and that's connecting to private business data so that, you know, you can get more, I guess, targeted answers, um, that are focused on data that's sort of proprietary to you. Uh, and I, I think the architectures are getting better at that.
Um, and so I think you would agree with those three, you know, sort of core business-oriented tools that could be useful.
Will: 100%.
Raju: The overhyped part is actually making business decisions And this is really important. Uh, I think if you sit there and say, "Allow it to make business decisions," a lot of what we do is relatively unique decision-making.
It's like a little bit of an art form, um, because if you just focus AI on helping you make a decision based on prior results, you don't get breakthrough innovations. And this is why, you know, there's a lot of controversy around whether AI is going to be effective at art because art kind of skims the edges.
And so you gotta think outside the box. You can't train the entire model based on like, "Hey, give me the norm, give me the median, give me the median, give me the median," and have it, you know, kind of push the envelope. Um, and so, you know, kind of making d- business decisions, at least in our world, it's not like, you know, I've done this task 1,000 times and 99.9% of the time it's always the same answer and, you know, like there's an outlier.
Th- there's y- you, there's sort of like esoteric factors that go into decision-making. So, uh, you know, I don't recommend that. Um, and you know, I, I'm sure you've seen the same, same in, in a lot of the thinking that you've had.
Will: Sure. I, you know, the thing that I, I struggle with is the, the, the results that you get with AI-based decision-making are often, uh, uh, over-weighted equally in the sense that y- you, there, there's not the, the appropriate weight being given to decisive factors.
And so you may get nice sounding middle of the road answers in a lot of situations where there's, there needs to be a clear cut yes or no, and there, there are h- there are high stakes associated with that yes and no. And, uh, we, we have yet to sort of make that, that leap to critical decision-making, um, threshold with the AI tools that I think most, most businesses and most individuals have access to.
Raju: And you know, just keep in mind, this podcast is definitely sort of like time-bound. We're gonna fix a lot of this stuff over time and, you know, maybe has a lifespan of a year, maybe has a lifetime, half a year. But certainly for now you don't wanna making business decisions. And the, and the last two over-hyped things is, you know, factual accuracy and complex math.
We know there's limitations around that. You know, you gotta ... It's almost like you gotta talk to your teenager. You know? It j- uh, factual accuracy is just a little bit off. You know, it tries, but you know, w- and we've talked about this in prior podcasts, Will, which is it's not programmed to say, "I don't know."
Will: Indeed.
Raju: It's programmed to give you an answer It, it's really desirous for the AI to give you an answer
Will: And to exaggerate its, its confidence, its, its certainty in that answer.
Raju: Exactly. Exactly. You know, so they're all kind of, like, focused around giving you that answer. And, um, you know, even if it's a math answer or it's doesn't have enough data, it doesn't say, "I don't know," and we gotta retrain them to do things like that.
And then there's bigger limitations that we've talked about in the just prior, well, two podcasts ago, which is, you know, we have context window limitations. That means if you've got massive amounts of data and you ask a difficult question, like a complicated question, there's just not enough tokens that can- Yeah
get you that answer. Um, so you can either ask a very simple question in a large data lake, or you can ask a larger question in a small data lake, but, you know, as soon as you get big data lakes and you ask a complex question, you're-- it's just gonna barf on that. And then cost, right? Those are the things that we understand as limitations, you know, across.
So that's kind of a grounding function for everyone. You know? It does a good job today summarizing files, you know, drafting initial content, um, doing some automation of a workflow, like drafting an email response. It does a good job in connecting with private, you know, sort of business data. What it's over-hyped at is making actual decisions, giving you factual accuracy across the board that you can trust every time, and complex math.
And then there's bigger questions about cost and the types of questions that you can answer, um, depending on the size of the lake. So what I'm gonna do to divide this next piece up is, um, how to get started. You know, what are the areas where we think all LPs should be playing around with given that they're incredibly busy?
And then we're gonna talk a little bit about the specific nuances around corporate LPs. Um, and we have a fair number of corporate LPs and, and, um, for those of that you that aren't yet LPs, you know, we've got a very sophisticated program for them, you know. And that program involves, you know, giving them a feel for technology trends that are pertinent to their sector, um, but also these kinds of, you know, touchpoints where we'll spend a couple hours and we'll go through their line of business.
And if you're a manufacturer, that means how do we get started with AI in manufacturing, and how do we get started in finance, and how do we get started in other sectors? So ground floor, all LPs, family offices, corporates, institutions You know, first thing I always tell them is you got to start playing with the tools, and you got to start taking some training.
And, you know, this kind of goes into one of the other podcasts that we've had, Will, where we talked about sort of where, what's that layer that actually is the starting point for every technology inflection shift? And, you know, I've always said it's been services, and we tried to, the world tried to poo-poo that and say it was, "Oh, no, we're going to go right to platform companies."
But the reality is you got to start playing with the tools, and that may mean that you bring some outside resources in, um, to train. And I'm sure you're coaching your companies that are sort of thinking this through as well that aren't native AI, you know, like how to get started. But, you know, that's kind of one of the things I've been talking about
Will: Sure.
Raju: The second area is maybe just bringing in some AI skills in-house. Consultants, maybe an analyst that you're gonna hire. Make AI skill sets a kind of a requirement, you know, across the board for, uh, those individuals, and start doing the things that we talked about earlier, right? Like, let's look at, you know, basically how do I do sort of email or, um, drafting a response.
Um, and so those things are relatively important. What I wanna jump to from there, and those are kind of obvious, i- is a couple of our portfolio companies. I'd really like to jump in. I'd love for you to sort of ta- talk about the first one, and I'll talk about the second one. And the first one is, you know, one of our GPs, uh, Vic, is CEO and founder of this company Originalis.
And Originalis is an AI-native tool set designed for, you know, both venture as well as, you know, our LP set. And that is a really fast path for a bunch of people who, on this call, who may be sitting there saying like, "How do I kickstart this? Yeah, I get it, you know. I can go and start playing around with it.
I can go, you know, replace an analyst with somebody who's got AI skill sets. I could start having it, you know, compose some emails, but I don't have time." You know, uh, and, and, and so, you know, maybe spend a few minutes and kind of go through some of the things that Originalis can do, and maybe on a subsequent podcast, we'll have Vic back on and have him talk about kind of the capabilities.
But I think this is an important one for people to understand. This applies to any one of our LPs that, you know, independent of whether they're institutional, corporate or, you know, family offices.
Will: It's important to begin by the kind of assessing the origin where all this came from. Because it began as an initiative internally for, for us to get more leverage in our investment decision-making process and our due diligence activities, and then in our portfolio management activities.
And, uh, you- We began our, our commitment to the Originalis team about 18 months ago and, and began using the tool in earnest here at RE about nine months to a year ago. And over time, uh, it's grown into a, a fundamental platform for us that supports all new investment decision-making consideration. And it solves both the complex and the mundane in, in, in that process.
Um, we, we use the Originalis platform to ingest, uh, data sets and due diligence items and reference call notes and, um, a whole-- all the artifacts that emerge in the process of considering, uh, a new investment decision. And it's that synthesis properly weighted that I think is created the first aha moment for us as a partnership, which is all of a sudden it, it has become much, much easier to keep the firm as a whole abreast of what's happening in the consideration of a new investment, and to package that information i-in the right ways with the right level of, of importance, uh, so that people are, are kind of immediately in the flow rather than waiting until the end of hearing about something to read a perfectly crafted investment memo that they may have waited weeks to get into their hands.
The process can now move along in a much more dynamic way. The wonderful thing we discovered in the process of this is that the Originalis tools that are so good at helping us weight various factors in considering a new investment for the portfolio are equally good in evaluating fund managers, both in the context of a new fund commitment-
Raju: That's super interesting, yeah
Will: and even when it comes to monitoring. And, and so a lot comes down to that RAG issue, that willingness to feed it, uh, li- internal data, legacy data. But, but once trained, uh, a, a firm can apply its own rubric and get really powerful, really useful output to support their decision making. And today we see a great group of early adopters among our LP base who are using the Originalis platform for their own decision making.
Raju: You know, may-maybe just Talk about some of the, you know, one of the capabilities. Just pick one area that Originalis can sit there and say, "Hey, look, you're a limited partner. You see you've got a bunch of managers that you have to support today, and then you've got to sort of support, you're evaluating potential new managers, or every one of your l- your, your GPs is kind of every few years kind of coming back."
What's like one output that, you know, Originalis takes care of or drafts? It doesn't have to be like the end, end game, but just gets you far enough along that it's like, oh my God, like it just saved me a ton of work, and we all know how hard this is for everyone here.
Will: So I'll, I'll pick, I'll pick two because one is the obvious one you'd expect and one is non-obvious, and they're, they're both critical elements.
So the obvious one is in, uh, is in performance evaluation. When you're looking at an existing team and you're trying to get a sense of what, what deals have been the moneymakers and who did them, and allowing the Originalis platform to basically digest the typical diligence objects you'd find in a, in a data room for a, for a firm that's raising a new fund allows, allows you to, to basically grind through that question of who's generating winners and who's really generating the alpha in this fund manager, uh, really efficiently, uh, without having to dispatch an analyst, without having to really unpack the cash flows and, uh, do your own set of weightings.
But the other one, and the one that I, I think is sort of most inspiring, is, um, it will score a partnership on culture.
Raju: Hmm. That's super interesting.
Will: You can not only incorporate references from, from CEOs and others, but it'll look at the tenure of the partners. It will look at their relative, uh, track records.
It will look at the roles that they play within the partnership. It will look at their own operating experience. And it will, of course, look at the question of turnover within the firm, uh, but in an intelligent way, one that's orient or- oriented towards understanding the, the, the tenure of major contributors to the success of the firm versus perhaps the, um, transitory activity of more junior or, or, or non-permanent team members.
So it's quite intelligent on those two factors, one quantitative, one obviously qualitative, both critical to thinking about the future, uh, of a partnership that you may be making a 10 to 15-year commitment to.
Raju: Yeah, I love it. I want... I kind of want to sing again. I'm not going to. I'm not going, I'm not going, I'm not going to do it.
All right. I will talk about Farsight, which is one of our portfolio companies. It's one that we invested in, and they've been on the podcast. So, you know, if you've been a regular listener, you've kind of heard about them. Um, and their initial focus was really just large M&A organizations, mid to large M&A organizations that were sort of separate on their own, like, you know, big firms like, um, Deutsche Bank and, and smaller ones, you know, uh, as well, so those M&A banks that exist.
Uh, because the CEO worked for a few years at, you know, General Atlantic and a few years at Evercore, and the whole team went to MIT, they really constructed this M&A in a box kind of packaging, which was ama- it's amazing. It's a really, really amazing tool. And for you corporates that are out there, they have extended themselves into that dimension.
So if you're a corporate VC and you invest in fund to funds, and you also do direct investing, um, or you diverse div- you know, like you basically like are selling entities inside your corporate, which happens all the time in big corporates, this is an amazing tool for you. It's an amazing tool. And this one is, you know- Ready for prime time.
It, you know, they've got large, large customers. I'm, I'm not gonna say who they are because I think there are br- are press releases going out shortly, uh, that are, like, mind-blowing. Like, they're the top five firms and- Household
Will: names.
Raju: Yeah, household names. Household names. So they're working with, you know, sort of crazy good, uh, folks already.
But if you're a corporate VC or a corporate LP and you invest in GPs and you invest in companies or your companies acquires, um, you have to look at this one. You have to look at this one, and we can make an intro. The demand is so strong in this one, Will. Like, we can move the needle obviously 'cause we're on the board and, um, get, you know, any of our LPs that are interested in this software kind of ahead of the queue or, you know, move them, move them along a little quicker.
Let's just put it that way.
Will: I'd love for you to tell our listeners a little bit about your perspective on this sector because they're not the first company to come along really delivering capabilities to support workflow in this area, and yet they're breaking through and, and some of their predecessor competitors are actually being ripped out.
So, so maybe you could help our listeners understand why Farsight is winning in such an important area.
Raju: Yeah. This, uh... Thank you so much for asking that question 'cause I did not explain that properly. So there are other pieces of software out there, including Claude, right? Claude will, you know, help you do sort of M&A functions, you know, g- provide data.
You know, one of the most important things in M&A is, uh, production of the CIM, which is the confidential information memorandum. So this is an example of how they deal with things. CIM production takes a long time. You know, in most, uh, M&A shops, it, it can be four to six weeks to create that CIM because you're taking this giant data room and you have this PowerPoint that you typically use as a big firm And you get an army of analysts to go scour the data room and populate each one of those slides one by one.
Farsight, basically you take their, your PowerPoint template embedded into Farsight. It hits the data room, and I think it's like fifty thousand, a hundred thousand simultaneous queries, and minutes later you have a SIM, a draft SIM. Let's just say it's ninety percent accurate to start with. Um, that is incredible in the grand scheme of things, and if you look at the competitor products out there, they cannot do that.
They are query-based. So basically what they do is they give you access to the data room to basically query what's inside the data room, and you're still effectively building those slides one at a time. Um, and Farsight is giving you the end product. Um, so that's a big differentiator. The second big differentiator is Farsight actually embeds your methodology and incorporates it and gets smarter and smarter each SIM you generate.
So each time you use the product and you've adjusted it slightly and said, "This is the workflow that, you know, kinda... This is the story arc that actually matters in getting a company sold or getting a company acquired," or, "I've gotta pitch this to the board of directors, and so this is the arc I need."
Every time you use it, it gets better and better and better. And so it is different than all of the other competitors 'cause it's focused on the end product. It's not focused on giving you a query engine. And it incorporates a lot of the thinking of the master organization into its small language model, which is dedicated to each player.
Um, and so they are taking share. They are beating the competitors. They are ripping out the competitors, and anybody who uses them sees why this is a much more sophisticated two-year ahead of everybody else kind of product. Um, and you know, like, I'm very proud of it. I know that Ari is very proud of it. Um, these guys are, are a special, special set of founders that have done something pretty remarkable
Will: Indeed they are.
Raju: Yeah. Well, thank you for that opportunity. So, so kind of like let me recap a little bit for all LPs. You know, what should you do? Well, let's start playing with the tools. Let's get some training, maybe bring in some talent, either in the form of consultants or analysts with AI skill sets, and start looking at some tools that are dedicated to your function, whether it's Originalis or Farsight or both.
Um, I think those are really excellent tools, but you got to get over the hump even though, like, you know, this isn't a world where you can wait forever to like... for things to mature. You just got to start, you know, getting. For corporates, and we have bunch of them, right? And, you know, as I said, we have a program where, um, I think it's worth mentioning the program a little bit.
Like for corporates, we actually will spend, you know, a couple days every Six months or so, um, more w- walking them through technology shifts and kind of what's happening, um, or if the time is better spent for them to sort of understand how AI is gonna impact their business, we'll do that. We'll spend the time.
Um, it's a very, uh, mutually beneficial re- relationship. Um, we love for our corporates to give us feedback in terms of what they're seeing and to take a look at some of the products that we're thinking about investing in and see if it would be a fit for them and what their feedback is before we make that decisioning loop.
But, like, one of the things we've done is gone in and talked to them about like, "Hey, you're a manufacturer. You're a giant fintech. You know, you are a healthcare organization, and you are trying to sit there and say, 'How, how is AI gonna change my business?'" And, you know, g- and, and that's beyond just, like, the corporate, uh, venture capital slash, you know, LP arm.
That's like, "What's it gonna do to my business? What's it gonna do to my business?" And, you know, we have a methodology here, right? So in the ground floor stuff, and I don't care what company you are, you have to be doing right now, right now, you have to get some AI fluency in your business, you know. And, uh, you know, we talked to the corporate VC pieces, but every part of your business has got to get some AI fluency, and it has to be directed from the top.
The, the leadership has to embrace the fact that AI is going to be, whether it's now or 10 years from now, it doesn't matter, right? Like, you gotta get some AI fluency. The second thing is use case identification. What are the use cases that actually can make a difference in your business? Um, and everybody kind of looks at customer service as a potential use case.
They look at knowledge management inside the business as a use case. They look at basically sales and marketing as a use case. Um, but you have to identify the ones that are most meaningful, and that requires each of your leaders in your business to sit there and say, like, "What's the one thing that would make a difference?"
And then you can sort of look at it across the business and say, "Of the five or six or seven that bubble to the top, which two or three really are the ones that we're gonna focus in on?" So that's really important. The third, and this is really important, is a dashboard that monitors the core AI metrics And if I were to synopsize our last couple of podcasts around AI, we've talked about all of these to some degree, like token cost.
So the reason token cost should be on your dashboard is because people make the mistake of thinking that today's token costs are gonna be tomorrow's token cost. And, you know, we all know that a lot of the LLMs were subsidizing those tokens, and now they're getting more toward, like, 50% subsidization or full price.
But you gotta monitor the cost because any ROI that you're gonna build around your use cases is gonna be dependent on the cost of those tokens. So that should be on your dashboard. There are a bunch of use cases, Will, and you know this, that are real-time in nature. They're real-time. So that means that the AI has got to think and respond in real time, so latency matters In those use cases.
Uh, because in some instances, you don't want the customer or the prospect or the, you know, whoever, business partner to know that they're actually chatting with an AI, um, or they're talking with an AI, so latency matters. Security should be on your dashboard, right? Like, if we have still holes in a bunch of, you know, stuff, you may decide that you need that AI in-house versus being able to go out and, you know, have a round trip in an outside world.
Answer and quality satisfaction. Um, this is super important, right? Like, I mean, when's the last time you called your wireless carrier? Oh my God. I, if we could just solve this one issue, just like, I need to get to the right agent, right person to talk to. Oh my God, I... It's like an hour every time.
Will: But as a B- as a former Bell Labs guy, it, it has special meaning for you, right?
You, you, you get kind of a warm feeling when you
Raju: do it, right? Yeah, I do. I do. When I, when I call my carrier and I wait an hour on the phone, I'm just like, "Yeah, man, this is what it's all about." I feel good. It's like it's a war... No, it's just so painful. Like, um, if AI can do one thing, it's get me to the person that I need to talk to.
Will: Yes.
Raju: Even if it doesn't give me the answer on how to solve it, I just need to get to the person. You know, that's like step one in all of this. Uh, but answer quality, satisfaction, hallucination rate, context window, all important. Token cost, latency, security, answer quality satisfaction, hallucination rate, context window.
Those five or six need to be in your dashboard, and you need to monitor them. And so let's say you've done this. You got some AI fluency, you d- identified some use cases, and now you have some AI metrics, and you know that the ROI for your use case makes sense when token costs are X price and when latency is Y and then when answer and quality satisfaction is Z.
Um, then the ROI makes sense, and you can go forward with a full deployment and move out of pilot, which is a- what a lot of people in corporate land are struggling with, is how to move out a pi- pilot. I, I, I think that's like sort of ground floor for all the corporates. The items that I think corporations by and large need to consider today absolutely leveraging are effectively three areas: sales and marketing customer service, and software development.
Would you agree with that, Will, or you think there's another one out there? 100%. Yeah.
Will: 100%. There, there are quick wins in all three of those areas.
Raju: And I'm not talking about taking over the sales and marketing, um, with an AI, but facilitating, supplementing your sales and marketing organization with AI tools, supplementing your customer service w- uh, with, with, you know, some tool sets and, you know, speeding up software development.
Um, I actually think software development's furthest along, right, in terms of its ability to create value out of the gate. Um, you know, you still need your senior developers to sort of authenticate and make, you know, sort of like, uh, get people comfortable, but it is furthest along. Uh, I, I think sales and marketing is an interesting one.
It isn't dissimilar to the ones we talked about for corporate venture or for limited partners, um, where we talked about Originalis and Farsight. There are tools for sales and marketing today that can help you find prospects better and faster. They can tell you when your prospect is contract is up for renewal with one of your competitors.
This is unbelievably valuable. Think about that. You're trying to sell to Will Porteous, and Will Porteous is using somebody else's product. You've called him up, and Will's like, "Ah, I'm already using blah, blah, blah, blah," and you're still trying to sell to Will Porteous, and you're like, if you knew when that contract was up for renewal, you would ping Will three months before that and say "Hey, Will, I know you mentioned you were on this other solution set.
Well, you know, we've heard in the marketplace it has a gap. This is what we do better. When's your contract up for renewal?" But you know when the contract's up for renewal, but Will, Will is now an interested party, right? Now he's ready to listen 'cause, you know, six months ago wasn't ready to listen. I'm- I got n- nine more months with this, with this player.
So there are a bunch of sales and marketing tools that help you generate drafts of content, generate drafts of outbound emails, identify who the best prospects are for you, basically draft emails that compare you to that competitor and why you need to be displaced. Um, I, I, I will give you one of those just because we're at...
You know, selfishly it's of our interest. Like, we ha- we're investors in a company called Avena, and Avena is a sales tool that absolutely does this. It tells you, you tell it, "Look, these are the type of customers we're looking for." It identifies who they are, who the, what the emails are, what the, you know, sort of social media links are, the LinkedIn on each of the potential buyers.
It will draft emails. It can even tell you when their contracts are up for renewal with your competitors, and you should be using this. If you are a manufacturer, if you are a corporate VC and your parent company needs this, you should be leveraging this. I know from personal feedback, we've introduced Avena to a bunch of corporates, and they are getting a ton of value out of it.
Um, customer service is one where y- I, do, I don't know. Like, w- ha- have you called customer service and talked to an AI chatbot recently?
Will: Yeah. It's getting better, but there's a long way to go.
Raju: My experience, I don't know if yours are the same It just can do the simple stuff.
Will: Right.
Raju: And even IVRs used to do the simple stuff, right?
You could check your account balance, you can find out what, when the order is gonna ship, you know? But anything that was complicated, it would just like kinda barf, and it still a little bit does that.
Will: Well, you know, Raju, I, I think it probably because a lot of this AI has been, been grafted onto process-driven architectures of the past, and, and what's actually needed is a fundamental rebuild.
I mean, think about all the millions of consulting hours that were invested in defining all of those customer service processes and, and literally mapping out all of those prompt windows so that every process could be tightly defined and tightly responded to. And, you know, the AI era is, is about freeing us from all of that kind of structured responsibility on the part of customers.
Um, it's probably gonna take a, a, a literally a, a what you and I would call a forklift upgrade from, from, from our enterprise selling past, um, for tho- for those big enterprises, and they're... Yeah.
Raju: Yeah. Yeah, I, I, I think there's an intermediate step. It's not getting the answer but getting the right person.
This is what I talked about earlier. I, I, I really think that that is what people should be embracing. How do I get Not the answer, but to the right person faster and without... And so I should be the, the AI should be able to understand the problem that I'm having and who the individual is inside the organization that can solve it.
And sometimes you go into billing and you really, it's a technical problem, but you're in billing, right? Or you're in the technical workflow, but really it's a billing question. Or, you know, you're, you're, you're trying to figure out how to cancel and you can't get the right person, and maybe they do that intentionally, Not, not getting you the right person.
But, you know, there, there's a bunch out there. Uh, you know, I think voice agents are getting better. If you're gonna do voice agents, by the way, a- anybody who's listening, um, you should talk to our, um, company, VoiceRun, because they... You know, I've, we've talked about this in, in podcasts before. You know, doing it alone and trying to figure out all the different voice AI engines like natural language processing, how do I answer the question, actual voice creation, all of them are different LLMs.
Trying to do that on your own without a framework and a forward deployed engineer is a route to failure. These guys help you with all of that, um, and that's why they're getting, you know, over the goal line with a bunch of companies. But I think that, you know, that's the nirvana of it all. Knowledge retrieval is an interesting one in corporates.
I wish... You know, like I, every single company I've ever worked with and for, um, frankly, there's always been this thing where you reinvent the wheel a bit. Um, y- you know, the bigger the company, the more people that have had to solve that problem before, but you just don't know who they are. And you remember the, back in the era of this knowledge management, um, you know, CRMs?
Will: Oh, completely. All, all, you know, all, all, all the, all the best approaches, all the best expertise was gonna be centrally collected and available in a pro- repository that anyone could access and train on, and-
Raju: Yeah. You remember intranets?
Will: Mm-hmm. Yeah.
Raju: It wasn't, wasn't an internet. Inside. It was just like, yeah, it was like an internet for inside.
Will: And, and all, all of corporate life could be found somewhere posted on the intranet if you-
Raju: Exactly ... if you needed to. Exactly. Exactly. Um, this problem has been knowledge management, knowledge databases, intranets have been tried before. I think AI is gonna, um, really make this better. Uh, it, I, I think this is an opportunity, and it doesn't have to be perfect, and that's why I think it's okay to start Using AI for this purpose inside of corporations.
And, uh, and we, we said this statistic before, one of the things that's really crazy to me is that 85% of agentic queries are repeats.
Will: Yeah. Uh, Raju, I think this is a really good insight on your part because so much of, so much of the problem around accessing that information historically has been around indexing and searching for it and having well-structured taxonomies.
And if AI... Uh, that's, that's the kind of problem that AI's quite good at, uh, synthesis and, and resolution. And you could... Y- y- y- we c- we can frankly finally start to unlock the value from these old, old corpuses. I don't know if, I don't know if the, the knowledge is still valuable in all those knowledge management systems, but at least now we'll, we'll know how to get it when we need it.
Raju: I think, you know, by... You, you'd be surprised at how valuable it is There's probably like a dude or a gal someplace in the company and she's the only one that knows how to fix this, you know? And it's like, wow, like, you know, it takes you like a week to get it, and meanwhile the manufacturing tool is down for a week or, you know, you haven't really addressed an issue.
Um, so I... There's so much more that we could talk about, like, you know, how to... What are the things within, you know, software development? What business development? Manufacturing should be using vision systems, R&D, data analysis. There's all these sectors, lending, finance. Like, what tools can we apply from an AI perspective for corporates?
I don't, I don't think we have time to go into those. I think we wa- I wa- what was important to get through, at least at the ground level, like if you're a LP that is a family office or an institution, you know, here's the starting point. Here are some tools out there that you should start experimenting with.
And if you're a corporate, you know, everybody's got sales, marketing, software development, you know, those kind of thing. Here's the tools that you should be considering, and here's the dashboards that you should be following. You know, with our core limited partners, the ones that, you know, sort of have been with us for a while, we'll, we'll, we'll spend the time.
And if they're on the call, you know, or are on this, um, podcast listening, we're happy to spend some cycles talking to you about, um, you know, what you should be playing with. And for the new ones out there, you know, understand this is a value proposition that RRE takes seriously. Um, that relationship with our limited partners is important.
And there's no guy inside of RRE that spends more time with limited partners than my partner, Will Porteous here.
Will: Well, this is, this is the kind of area where we, we, we, we wanna be helpful. We're, we're grateful for the long support we have so, from so many great institutions and investors. And it is, it is a critical moment in which everyone's organizations are being changed by these capabilities.
And, uh, we wanna share what we use and what we think can benefit our partners, uh, so that everybody can get some time back or be more efficient.
Raju: Yeah, that's amazing. I'm gonna let you close this one out. And if you wanna do song, you're, you know- ... you can absolutely close it out with a song.
Will: You know, I, I think, I think you, you set a very high standard earlier, so I'm not gonna sing to our audience.
But I am gonna thank our audience for their loyalty, for being with us week after week, for sharing the RRE POV episodes. If you think we've, we've presented something in an interesting way, if you got something out of these episodes, we'd be grateful if you'd pass it along and share it with people in your network.
And, um, and if there's something we've said that you disagree with, we'd love to hear from you too. We're easy to reach. Uh, so thank you as always, and we look forward to sh- having you with us again before long.
Raju: Yeah. And Will, you and I are gonna do karaoke now. I think we should do karaoke. Okay. Thank you all.
Will: Thank you, everybody.