AI Negotiation Intelligence: How Infinity Loop Redefines Procurement ROI

Russ Fordyce 🏆 (00:01.18) Welcome back to the winner circle. I am here with Nithin from Infinity Loop and he is an AI excellence award winner. And we're going to talk to him all about AI procurement and legal negotiations. Nithin, welcome to the award show and congratulations on the award. Nithin Mummaneni (00:15.31) Appreciate it, thanks for having us, Ross. Russ Fordyce 🏆 (00:17.564) Yeah, why don't you give us a little bit of background on Infinity Loop and how you started it. Nithin Mummaneni (00:24.398) Sure, we started the company, we former management consultants, working for Fortune 500 companies and private equity firms. helping restructure their businesses. So one of the key things that we did was identify the top contracts that these customers had and figure out the leverage points and actually renegotiate on their behalf, often driving savings. And then we would charge a percentage of savings on the actual realized amount. that process was so manual and it was across different industries where it was CPG, healthcare, manufacturing, cosmetics, defense. they all were kind of buying similar goods and services. Obviously they had had their nuances by industry and we were tasked with doing this stuff manually and we wanted to solve that problem on just identifying the top, you know, savings opportunities at a high level. and then providing the details around what strategies actually work when negotiating these suppliers. So we've actually developed a machine learning algorithm that has been trained on this data sets. And we pull in macro data as well. it's basically, we want to tell you, did you get a good deal or not? And provide the backup for that as you're going through a negotiation. So that's what we've done. And we're seeing a ton of savings in direct impact. Russ Fordyce 🏆 (01:38.952) Now. Procurement and contract negotiation aren't the first area I think of when I think about AI agents. What drew you into that specific space? Nithin Mummaneni (01:51.81) Yeah, was just, you know, it's from direct experience, right? Is we wanted to solve the problem of just answering the question, did I get a good deal or not? And right now, even still, it's so subjective, right? And one of the key things that we're seeing across our customer base as well is that companies are looking for solutions that have direct ROI impact. when implementing AI solutions. And one of the best ways to do that is to identify where you can actually save money using AI on your third party expense base. Because that gives you kind of the leeway and kind of quick wins in terms of implementing AI in your organization. So from a strategic standpoint, that's what stood out and why we went down this path of building Infinity Loop into the AI platform for cost optimization. Russ Fordyce 🏆 (02:44.079) And for somebody outside the space, what does a typical enterprise procurement contract negotiation process look like? You know, without the AI. Nithin Mummaneni (02:51.404) Yeah, depending on the side. Yeah. So, a lot of the times now with these enterprise companies, they're running RFPs. They are spending hundreds of millions and sometimes billions of dollars on a variety of third-party expenditure, whether it's software to keep the business running or industry specific nuance, things like, you know, auto manufacturing where they're buying the actual parts and putting it together. it could be things like chairs and things like that. So. All of these things, you know, they're spending money on and they have teams associated with each category usually if it's a centralized procurement function. So they would manually read through all these contracts. have relationships with these suppliers. You know, they spend about 80 to 90 % of their time actually doing the prep work. on understanding what are the market benchmarking, what are the dynamics, am I getting a good deal, what are the things I can do to protect myself as I go through negotiations with the specific supplier. So all of that now happens manually. And obviously the negotiation itself happens over phone, in person. And we're bringing all of that online to provide kind of the decision intelligence around. helping companies automate all of that legwork so they can focus on the value added aspect of actually doing the negotiation, which so happens to be the fun part as well. Russ Fordyce 🏆 (04:14.543) Yeah, well, it's interesting. I worked for a lot of different companies in my career, and I don't think we had a centralized system in any of them that I knew about for even responding to RFPs or any of that. So I imagine just getting an enterprise into a platform alone has got to be a significant advantage. Nithin Mummaneni (04:36.566) Yeah, that too. again, bringing it online and we integrate with existing systems, you know, such as, know, Kupa, SAP, Oracle on the P2P side of things, and then on DocuSign, Ironclad on the legal side, because we don't touch any type of redlining. We don't touch the spend approvals orchestration layer. The best way to think about us is we're kind of this middleware kind of intelligence layer that doesn't really exist right now. And that's what we're focused on. And if you can think about it, like as we're integrating with these systems, we don't touch the workflows. It's just the decision intelligence. We call ourselves a negotiation intelligence platform. And that's really what it's geared towards is we could care less about the workflows. We just want to tell you, you know, what opportunity is there on this particular contract and how can you actually negotiate it to get the best outcome for the company. Russ Fordyce 🏆 (05:25.437) So you've been doing this a bit now. Where do you think the biggest inefficiencies or risks are in that whole procurement process that you guys are kind of... Nithin Mummaneni (05:36.908) I think the biggest thing is that negotiators right now are... whether it's the procurement team or the business unit leads, they just don't know if they got a good deal or not. Right. So what is the actual market rates look like? What are the things kind of other companies have gotten in terms of the deal structure, the agreement, right? Is it specific pricing structures? Is it, know, specific payment terms? There's a variety of different inputs in terms of the deal structure. So them now having a flavor of what's actually getting completed in the market lately gives them a leg up when they are in with the supplier to tell them, hey, I'm overpaying by $200,000 on this deal, but I also know to ask for these 10 to 15 items that I want to implement just to protect myself, to reduce any type of downside risk. Russ Fordyce 🏆 (06:26.173) So you're sitting in the middle of this decision kind of process, right? And you're the intelligence engine. I guess are the employees loading in the information and then getting a result out your intelligence engine? Or is it a report? What does that look like to the end user? Nithin Mummaneni (06:47.564) Yeah, so from an input standpoint, all we would need is the contracts themselves. So whether it's an SOW amendment, a rate sheet, it could be basically all of the different files associated with a particular deal they would upload. And then we would ask them specific questions on the context. So are there specific trade-offs that you want to make? How aggressive do you want to be in your negotiation approach? The model then takes all of that into account to tell you, Hey, here's how, you know, we see the platform. So we basically come up with our own proprietary scoring algorithm. So we score every contract to say, you know, you got a C plus on this particular deal for these reasons. Here's how you go and fix it. So it's like a school teacher kind of grading your report card. On top of that, we tell them from a pricing standpoint, you know, you're overpaying by, you know, a hundred thousand dollars. then take all of that information to say, okay, now here are the seven specific negotiation strategies in detail with benchmark data on how you can actually realize it. Right? So what are the leverage points that you can say and do on the phone or email? And we provide all of that in a compact view so that teams can actually use that information to structure their negotiation approach, but then actually go through the next negotiation with that information in hand. Russ Fordyce 🏆 (08:03.741) So you're handing back the information. You're not doing the negotiation itself. Nithin Mummaneni (08:08.216) Correct. We're not at a point now, in my view, that AI bots run and do negotiations, especially because we're focusing on the larger deal. There's not enough trust at the organizational level to let a bot negotiate a million dollar contract or more. We're not there yet. And I don't think we would be there in quite some time, mainly because it's the direct impact to the bottom line of that company. It's too important. Exactly. Russ Fordyce 🏆 (08:32.977) Well, negotiation is such a human skill. There are so many complexities to it. And I would imagine that AI intelligence engine would have to know every bit about that customer experience, all about the customer, all about the interactions. It would have to know everything in order to be good at negotiating, to understand the context and everything else. How do you think AI is actually improving the negotiation outcomes other than just price? Nithin Mummaneni (09:04.716) Yeah, so, you know, big one could be. Uh, like I said, the contract risks standpoint. So if I were to take you back to COVID, um, you know, we had an engagement, I think it was around ocean freight and the costs of ocean freight actually 10 X. Right. So instead of paying, you know, $2,000 a container, went all the way to 20,000, uh, and sometimes more. And a lot of these companies were not protected from a deal structure perspective. So it was not tied to a particular index. Russ Fordyce 🏆 (09:21.723) Yep, I remember that. Nithin Mummaneni (09:37.648) and all of that. So they got completely fleeced, which obviously impacted their margins. And then that was ended up passing through to the customer, but they could have easily protected themselves if they just tied it to a particular index or figured out some type of tiered model where it caps those increases. So all of those things could have been impacted in a positive way, right? In those types of situations, had they gotten this intelligence ahead of time to bake those things into the contract. themselves. So it's not just from a commercial standpoint but obviously from a risk standpoint that's why we look at the kind of contract risk as a whole which takes into account all of the legal operational as well as commercial terms holistically. It's not just one component. Russ Fordyce 🏆 (10:25.885) Well, I would imagine the efficiencies of it, especially for larger companies who do this a bazillion times a day, a year, a month, whatever, the efficiency alone, having that negotiation kind of layer, that intelligence layer, has got to be a huge efficiency gain of not having to go do all that legwork. Nithin Mummaneni (10:31.086) Mm-hmm. Nithin Mummaneni (10:43.758) Exactly. And, you know, they're saying what we're seeing is that companies are saving between I would say 80 to 90 % of a procurement manager's time doing the legwork. So that's been completely automated. So all they're doing is the actual negotiation and whatever orchestration layer they have. But with that, instead of teams, know, an individual managing call it five to eight deals a week, they can now do 15 or 16. Cause all they're doing is the negotiations rather than doing the prep work. And that's what has been super impactful. Not only do they get the cost savings, as well as the time savings, but it's the whole cost avoidance piece of hiring somebody externally, like a McKinsey, Bain, et cetera, Deloitte, where they charge millions of tens of millions of dollars to go do this work, they now have that complete cost avoidance on that side as well. So again, all of this is attributed to direct ROI. Russ Fordyce 🏆 (11:41.532) And I imagine these, your customers are starting to shift procurement teams and resources as this becomes more kind of standard. And I always like to use the example of the radiologist. all said that, everybody said that radiologists were going to go out of business. And the reality is, is we're doing more radiology than ever. And there's more radiologists than ever because more consumers have access to radiology. So how does this change that kind of role of procurement? Nithin Mummaneni (11:57.922) Yeah. Nithin Mummaneni (12:10.092) Yeah, and what we wanted to accomplish, our mission is to democratize the expertise of procurement consulting into software form so that any and all teams, especially at the enterprise level, can look internally rather than externally, upskill their internal staff and just make them more... it better at their roles, right, at the end of the day. So that was kind of what the mission was, and it still is, is to just democratize that expertise through software. And that's kind of what we're doing. Russ Fordyce 🏆 (12:44.253) Now contracts, you know, this contract and procurement process is super sensitive and as you've identified, it's very, very high risk. You know, you're talking about millions and billions of dollar contracts sometimes. How do you build trust into an AI system for making recommendations in that environment? Nithin Mummaneni (13:01.708) Yeah, so we take security seriously. So some of our advisors are also, you know, ex-CISOs that have kind of helped us kind of approach this. Cause when we thought about it as well as everything's SSO, MFA enabled, and even kind of the data architecture in the backend, no company is going to say, you know, IBM is getting paid X amount with this particular supplier. That's never exposed, right? So there's some creative things we were able to do in the backend for data protection. And we work with mainly enterprise companies right now. So as you can imagine, they have a thorough diligence process on the information security side as well as on the IT side to make sure their data is protected. And we have some of our secret sauce methodology that we've kind of implemented that has been working. Russ Fordyce 🏆 (13:48.698) And as you get into the sales conversation, trying to convince somebody to bring in this intelligence platform, what are some of the objections or concerns that you're hearing that you're having to overcome? Nithin Mummaneni (13:53.646) Okay. Nithin Mummaneni (14:03.714) I think the biggest thing is... In terms of like the adoption of the actual teams themselves, that could be one where teams could see this as a risk of them losing their roles. So that was one where we see quite consistently, but then we were able to mitigate it because it's all about efficiency at the end of the day. And teams more or less love using our solution because it makes them look like superstars and not having to go hire an expensive consultant to go do the same work. So that's all we're doing. We're insourcing it. We're making everybody into internally kind of like the superheroes of their actual roles and that's what they're liking. So it's just about messaging and change management at the end of the day. Russ Fordyce 🏆 (14:45.991) Well, you know, having read through your nomination, what I liked about it is you're building this kind of big engine for big customers with a very small team. What advantages does that give you in kind of, you know, a space dominated by the big, huge players in the market? Nithin Mummaneni (15:03.096) I think we're carving out a niche. We like to move fast here at Infinity Loop and I'm proud that everybody that we've kind of brought on. Speed is execution at the end of the day. And we are able to iterate super quickly, especially on some of the larger accounts that we do have. And they've been able to see tremendous results because something that, you know, they come to us with some feedback, we're able to turn around a day or two. So I think just that speed of adoption and kind of execution has been super powerful. And because we are a lean team, everybody's super talented. I think we one of the best teams in the world around this. So that's what gets me excited. is just seeing the customers faces when we do a really quick turnaround and them actually using it to save money. Russ Fordyce 🏆 (15:51.345) Yeah, it's gotta be almost immediate when you plug it in. Nithin Mummaneni (15:54.976) Exactly. Russ Fordyce 🏆 (15:57.288) What's been the hardest part of getting from concept into the kind of enterprise adoption? Nithin Mummaneni (16:02.574) I think one big thing. is the hardest part was actually building out the platform just because there's so many different nuanced features we wanted to build in. But because of our kind of experience negotiating in different industries, as well as tacking, chatting with our network. And we had some for early adopter as customers that gave us quick feedback as well. Just seeing all of that, I think has been super rewarding and kind of. I think the biggest thing is just like people are actually saving money. using the platform. So there's like a direct benefit that they're seeing, which has been nice to see. So again, we are focused on ROI. I think everything attributed to what we have built on the platform is about that. And once they start seeing our new customers start seeing demos, they immediately like, this is awesome. And I can just plug in with existing systems and it helps me save money. So it's a no brainer in that regard. You can imagine like some of these larger enterprise companies, they have so many Kind of like distributed systems and teams and they're just been clamoring for solution that actually works and It's all in one place that can plug into some of these other tools. So that's been super rewarding and impactful Russ Fordyce 🏆 (17:22.341) What's amazing to me is we've gone from this kind of, you know, almost a, you know, sideshow experience in AI of, my God, look what it can do, you know, two years ago into this, into what you guys are doing, which is like, you're getting into the really strategic part of the business. And I've talked to others who are getting into the strategic parts of business too. How do you see procurement changing over the next few years as you, as, as we get more AI into that, into that role? Nithin Mummaneni (17:52.748) Yeah, I think a lot of solutions are focused on workflows, which is great. But that doesn't really impact the bottom line. It's always about, did you get a good deal or not? How much money is actually being left on the table? We want to get into those kind of strategic conversations because that's actually what's going to move the needle in terms of saving margin at the end of the day at these companies. So I think where we are positioned is great. There's a ton of AI tools out there that touch workflows, not as much so on kind of what we are doing, which is good because we're kind of carving out our own niche there. But I'm excited to see kind of how adoption evolves in these larger enterprises and the more more adoption that becomes, obviously the models will spit out better answers because they're training on that specific outcome data too. Russ Fordyce 🏆 (18:46.181) I'm wondering if you've learned anything about kind of enterprise process or decision making that you didn't know before after kind of building this out and working through those initial customers. Nithin Mummaneni (18:55.597) Yeah. Nithin Mummaneni (18:59.298) I think we had a leg up just because I was on the other side of the table in my previous roles. just coming to the sell side, it just reminds me of kind of all of the bureaucracy, but because you've been through it on the buy side, you now know what to say and do when you're pitching customers. And it's all at the end of the day, it's about building trust, right? do these people want to buy your solution and put their reputation on the line when they're going and implementing, right? Because they're bringing you into a bureaucratic organization. So you're going to have to get the buy-in. think our backgrounds definitely speak for themselves, having saved over billions of dollars collectively. we can speak to the language. We also do some things in terms of proving out our value prop. through a proof of concept first so they know what they're getting. And I think that's been a huge help. So just seeing that adoption go up and without a high conversion rate because of that. just knowing kind of what they want to hear and say from our past experience has definitely been fruitful for us as we're implementing this in various enterprises. Russ Fordyce 🏆 (20:07.355) without giving away too much of your roadmap, what's next for Infinity Loop? Nithin Mummaneni (20:12.27) Yeah, we were going deep. We're going deep on negotiation because that's I just feel it's such a untouched market, especially across these different verticals. As you can imagine, when you're buying software versus negotiating leases, it's very different. Right. So just making sure that the context and all of that is then tailored to the various industries and kind of expanding the various category sets we do service. So we handle about 23 different categories, broad categories and 150 subcategories so far. So continue to expand that so we can service different industries, I think is the goal. And obviously building out the product features based on our customer feedback as well. Russ Fordyce 🏆 (20:54.173) Well, Nathan, super exciting. Congratulations again on the AI Excellence Award. Judges couldn't have been more clear that you guys deserve some recognition and were, I think, almost the highest in your class. So congratulations on that. We'll be keeping an eye out for what's next on Infinity Loop, and we wish you guys all the best. Nithin Mummaneni (21:12.142) Appreciate it. Thank you so much, Russ.