09:05Simon Høiberg · Tutorial
How AI Agents Run My SaaS
Five concrete jobs one SaaS founder handed to an AI agent — and what changed when he did.
June 8thThe argument in one line.
AI agents that carry unlimited buyer context and can close deals end-to-end are economically defensible at $100K to $400K per year when priced against the labor they displace rather than the compute they consume.
Read if. Skip if.
READ IF YOU ARE…
- A B2B SaaS founder or revenue leader deciding whether AI agents can realistically own enterprise sales cycles, not just assist them.
- An AI product builder trying to understand how to price agent capacity against human labor costs rather than usage metrics.
- A founder who has been through a major exit and wants a candid take on when to take personal liquidity versus rolling equity forward.
- A go-to-market operator who has watched SDR-to-AE-to-CSM handoffs destroy deals and wants to understand what a unified AI alternative looks like.
SKIP IF…
- You want a technical architecture breakdown of how AI agents are built -- this is a commercial and strategic conversation, not an engineering one.
- Your business is B2C or early SMB -- the pricing model and use cases here are squarely enterprise.
- You require third-party verified metrics -- all figures come from the founder and are unaudited.
The full version, fast.
1Mind builds lifelike AI agents called superhumans that handle the entire B2B sales motion: inbound qualification, product demos, live Zoom calls as solution engineers, and post-sale onboarding. The company prices each agent at roughly the cost of the human role it replaces, typically $100K to $400K per year on a flat annual contract, and has reached 211% net dollar retention because most enterprise customers add a second agent within 90 days. Their own AI agent, Mindy, sources 78% of company pipeline and generates eight figures per quarter in qualified opportunities. The thesis: buyers deserve a consistent, context-rich experience across the entire purchase journey, and humans structurally cannot provide that at scale.
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Create a free account →Who's talking.
00:52guestAmanda Kahlow
00:00hostNathan Latka
Where the time goes.
00:00 – 00:52
01 · Cold open / highlights reel
Key stats teased first: $1M in 3 months, 600% growth, 211% NDR, $400K enterprise customers.
00:52 – 02:08
02 · Guest intro and what is emotionally intelligent AI
Amanda reframes the buzzword as go-to-market superhumans; explains replacing all GTM roles from top of funnel through close and CS.
02:08 – 03:16
03 · Tech stack and how it works
Multi-vendor face layer (commoditizing), proprietary context graph as the real moat; real enterprise customers confirmed.
03:16 – 04:45
04 · HubSpot case study
Fiona handles SMB demos end-to-end, qualifies, and closes. Increased HubSpot SMB revenue by 25%. Replaces 89 SDRs and 19 sales engineers.
04:45 – 05:29
05 · Why GTM handoffs are broken
Context loss across SDR-AE-SE-CSM is the core buyer-experience problem. AI has no capacity limits and can sell into any vertical.
05:29 – 07:42
06 · Pricing model
Annual flat subscription. $100K per agent (cost of a human). Moving away from metered toward bucket-based to give CFOs predictability.
07:42 – 08:04
07 · Founderpath sponsor
Nathan pitches founderpath.com capital program.
08:04 – 09:05
08 · Revenue milestones confirmed
$1M in 3 months confirmed. 600% YoY growth stated. 18 months in market.
09:05 – 10:49
09 · 211% NDR and hallucination track record
90-day expansion to second agent is the norm. Zero hallucination incidents in 18 months -- only issue was outdated customer-provided content.
10:49 – 11:10
10 · Nathan back-calculates ARR
Nathan does live math: 600% growth from $1M at month 3 implies north of $6M ARR by month 18. Amanda deflects but does not deny.
11:10 – 13:47
11 · The 6sense exit and funding philosophy
Amanda left 6sense around Series C at $380M valuation. Company later hit $5.2B. She took liquidity but retained equity.
13:47 – 14:52
12 · Founder liquidity advice
Taking a secondary when you lose control is not shameful. Amanda advocates openly for founders to protect themselves before outcomes materialize.
14:52 – 17:57
13 · Eating their own dog food
Mindy sources 78% of pipeline, 8 figures per quarter. Nigel AI SE now handles solution calls. Real 90-minute Alteryx CRO conversation driven entirely by Mindy.
17:57 – 20:11
14 · Cloning vs custom personas
Cloned AI Adam at owner.com and Jack at Winning by Design for personal brands. Alteryx used founder wife likeness. Conversation design matters more than the face.
20:11 – 22:34
15 · Technical depth and inbound model
Stack of simultaneous agents for latency, cost, accuracy. No outbound -- all inbound via Mindy. 20-40% engagement rate from site visitors vs 2-5% for chatbots.
22:34 – 23:13
16 · Margins and cost of goods
Caching as primary margin lever. 80-90% gross margins maintained. Targeting revenue-generating motions not support tickets.
23:13 – 25:08
17 · Wrap and CTA
Nathan summarizes metrics. Amanda plugs LinkedIn. Solo host close.
Lines worth screenshotting.
- Pricing AI agents against the cost of the humans they replace unlocks six-figure enterprise ACV; pricing per seat or per call leaves most of the value on the table.
- 211% net dollar retention means the installed customer base grows faster than churn -- existing customers are a better sales team than new outbound.
- The SDR-to-AE-to-SE-to-CSM handoff destroys buyer trust by asking the same questions repeatedly; AI eliminates that context loss by design.
- 78% of 1Mind pipeline is generated by their own AI agent, making the go-to-market motion a live product demonstration to every prospect.
- Fiona, HubSpot SMB agent, does the work of 89 SDRs and 19 sales engineers and increased SMB revenue by 25%.
- Moving from metered pricing to bucket-based pricing removed CFO budget anxiety and accelerated enterprise deal velocity.
- Caching LLM responses is the primary lever for maintaining 80 to 90 percent gross margins despite heavy AI API costs.
- Cloning a founder face and voice does not outperform a custom AI persona on conversion -- conversation design matters more than identity.
- Multi-agent orchestration that routes simple queries to smaller models and reserves reasoning models for complex questions keeps latency, cost, and accuracy balanced simultaneously.
- Founders who lose operational control should take personal liquidity at that moment, not wait for the next funding round.
- Buyers are beginning to trust AI agents more than human reps because the AI cannot lie to close a deal.
- A sales engineer role does not disappear when AI replaces call work -- it transforms into maintaining the AI knowledge base.
- 1Mind sources zero pipeline from outbound; all acquisition is driven by the website superhuman, Mindy.
What enterprise AI agents actually need to work.
WHAT TO LEARN
Lifelike AI agents can own the full enterprise sales cycle -- but only when priced against human headcount, trained on current data, and designed for conversation depth over surface chat.
- Pricing AI agents at the cost of the human role they replace makes the ROI calculation immediate for enterprise buyers rather than speculative.
- 211% net dollar retention is achievable when the product naturally expands: most customers add a second agent within 90 days after seeing the first one work.
- The real risk with AI agents in sales is not hallucination -- it is deploying them on outdated product documentation; the AI faithfully repeats whatever it was trained on.
- A company that generates 78% of its own pipeline using its own product is the most credible demonstration of that product; the sales motion becomes the proof of concept.
- Moving from metered per-conversation pricing to a bucket model removed CFO budget anxiety and accelerated deal velocity -- predictable spend beats granular billing for enterprise.
- Maintaining 80 to 90 percent gross margins with heavy LLM usage requires caching responses aggressively and routing simple queries to smaller models; the cost structure is a choice, not a given.
- Conversation design -- how the agent guides the buyer, handles objections, and demos the product live -- matters more than whether the agent face is cloned from a real person.
- Founders who hand operational control to a board without first taking personal liquidity leave themselves exposed; taking a secondary when you lose control is financially rational, not disloyal to the company.
Terms worth knowing.
- Go-to-market superhuman
- 1Mind term for an AI agent that handles inbound qualification, product demos, live sales calls, and post-sale onboarding -- replacing multiple human GTM roles with a single continuously available agent.
- NDR (Net Dollar Retention)
- The percentage of recurring revenue retained from existing customers after expansions, contractions, and churn. 211% NDR means existing customers more than doubled their spend in a year.
- ACV (Annual Contract Value)
- The normalized annual revenue value of a customer contract. Used to benchmark deal size in B2B SaaS.
- Context graph
- 1Mind internal data structure that stores buyer history, product knowledge, case studies, and conversation context so the AI agent can give accurate, situationally relevant answers at any point in the sales cycle.
- PLG (Product-Led Growth)
- A go-to-market strategy where the product itself drives acquisition and conversion rather than a sales team, typically via free trial or freemium.
- SDR (Sales Development Representative)
- An outbound or inbound sales role focused on prospecting and qualifying leads before passing them to an account executive. Often the first human touchpoint in enterprise sales.
Things they pointed at.
Lines you could clip.
04:45
“The handoff from an SDR to an AE to a sales engineer to a CSM is atrocious.”
09:45
“Humans hallucinate nefariously. Sales reps do it to get the deal done.”
03:46
“Fiona is doing the job that would have taken 89 SDRs and 19 sales engineers.”
14:52
“78% of our pipeline has been sourced and created via Mindy, our superhuman.”
13:55
“If you give up control, take a first bite at the apple.”
Where the conversation goes.
00:52 – 05:29denseProduct and go-to-market overview
05:29 – 08:04densePricing and deal structure
08:04 – 11:10denseRevenue metrics and growth
11:10 – 14:52steadyFounder backstory and liquidity
14:52 – 20:11denseInternal use and product depth
20:11 – 23:13steadyTechnical architecture and margins
Word for word.
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00:00You share how many months from launch did it take you to break a million bucks of contracted revenue? We broke a million from when we went out selling within probably three months. Are you open to sharing a percent, like a growth rate over the past twelve months?
00:11Are we talking, like, 500% year over year growth or a 100% year over year growth, or where are you generally? 600%. Can I ask you what you're doing today or a range?
00:18We had a 211%
00:19NDR. I think on your website, you said you have 45 customers. Uh, we have over 60 now.
00:23What does emotionally intelligent AI mean? Take me past the buzzwords. We are building what we call go to market superhuman.
00:28So we are replacing all roles across go to market. So everything from the obvious that everybody else is doing, top of funnel, inbound. She's on your website.
00:37She gives a pitch, gives the demo. She doesn't just pass off to an SDR. She goes all the way for the close.
00:42Check individual customers now already paying just eighteen months after lunch. $400,000.
00:46Amanda, as we wrap up here, you just you know, 30,000,000 series a closed. What was the valuation? Hey, folks.
00:53My guest today is Amanda Kahlo. She's a three time entrepreneur and the former founder and CEO of Sixth Sense. She's currently the founder and CEO of OneMind, which she launched to transform the go to market strategies using emotionally intelligent AI.
01:05Amanda, you ready to take us to the top? Let's do it. Alright.
01:07Okay. What is emotionally intelligent AI mean? I I don't know who came up with that.
01:11That's not typically what how we what we call ourselves, but we are building what we call go to market superhuman. So we are replacing all roles across go to market. So everything from the obvious that everybody else is doing.
01:22Oh, here we go. There's our superhuman right there. Uh, top of funnel inbound.
01:25She's on your website. She gives a pitch, gives the demo. She doesn't just pass off to an SDR.
01:30She goes all the way for the close. Mhmm. And so then we also join Zoom calls, so she could be here on the call with us, and she can act as a ride along sales engineer, solutions engineer to give the demo, answer the tough technical questions.
01:42Then we have a lot of PLG companies that are using it for onboarding to get people to go from free trial to paid. And lastly, in customer success. So how do we onboard our customers through the life cycle for more of an enterprise motion?
01:53So our goal is not to be just top of funnel. Our goal is to support the full life cycle and give the buyers one experience that's consistent throughout. Interesting.
02:01Yeah. Mean, I'm using here on my screen. I had to mute it because it's she started talking to me, but you guys get the sense of what's happening over here.
02:06Amanda, just out of curiosity, what's the tech stack here? I mean, is this a, like, a hey gen real time API plus 11 Labs voice on top of it, or what what's the tech stack here powering her? We use a multiple of different technologies.
02:18Some of it's our own, and some of it's third party vendors. We actually have our own pipeline for most of it. We use multiple different face vendors.
02:25The face, I believe will be commoditized at some point. So we did start out building face and then we kind of said, you know what, we're more about the brain, we wanna build the context graph and be the decision logic and the engine behind so that she can give the real live demo so that she can, um, support and create ROI calculators on the fly, etcetera.
02:43So, yeah, it's a combination of different things, and a lot of it's our own. Mhmm. Really interesting.
02:47Okay. Let's do a couple of these examples here. HubSpot, VSP.
02:49Just to be clear, are these real? These are real paying customers, real examples? They are real enterprise paying customers with logos that we are allowed to talk about.
02:57So these are not experimental budgets. Most of these customers are paying the cost of a human, um, plus and have multiple, like sue like, HubSpot, for example, we are working on, I believe it is their fourth superhuman, um, to go live across their journey.
03:12Fiona, the one you're seeing here, was the first superhuman that we built for them. It's in the top left corner. Fiona is set to talk to their SMB business.
03:19So when somebody asks for a demo, they engage with Fiona. She gives the demo. She qualifies.
03:24She takes them through to close, and they were able to increase their revenue by 25% in their SMB small business segment. Interesting.
03:31Does Fiona make commissions? No. That's a good thing.
03:34And she is you know, we did some analysis for HubSpot and we looked at the transcripts and the number of conversations and the quality of conversations, the depth and the content she was talking about, and it would have taken 89 SDRs and 19 sales engineers to do the job that Fiona is doing today. Interesting.
03:50So quantify, mean, everyone watching my show totally understands the old sort SDR, BDR to AE ratios and then their CSM after that. Everyone understands that flow. How are you I mean, you probably ran that playbook at 6¢ actually as you scaled 6¢.
04:02How is this radically changing that? Is this like a cost structure change? Is it something else I'm missing?
04:06If you look at the jobs report, AI has put on more jobs than has taken away. I think there are some legacy ways that we ran go to market that is highly inefficient, as you mentioned. But, really, a lot of companies today are stuck, and they're not growing, and they need to cut costs.
04:19They need to do two things. They grow, and they need to cut costs. And so what bigger problem to solve than those two things through the form of an AI superhuman that basically can do the job and do it exponentially better, more efficiently, and serves the buyer.
04:31So my ultimate goal, why I'm helping companies grow, I'm helping them cut costs. My number one thing that we are focused on is creating a better buying experience. The handoff from an SDR to an AE to a sales engineer to a CSM is atrocious.
04:45If you think about and there's there's loss of context and memory. We ask the buyers the same thing. We take them through the journey, and it's if you put yourself in your the buyer's shoes, the process that we go through with humans today is ungodly.
04:56So this is a new way and a new world to have unlimited memory recall capacity limits, can talk to any industry or vertical. Like, for example, if you're talking to, like, a Cisco or a Databricks that sell to every industry and every vertical and anyone who needs data, for them to staff sales engineers and solutions engineers that understand the nuances of every vertical and every industry they sell to is virtually impossible.
05:17But with AI, it has no capacity limits. So she can actually solution sell in a way that humans could never in the past. This makes sense.
05:24How are you pricing this thing? What's the average customer paying per month today? It's an annual contract or a multiyear contract commitments that we have from most of our customers right now.
05:33Nothing surprised. Like, it's a flat subscription. We do have some fair use cap, but I'm not in the world of metering.
05:38The whole pricing model around AI is very difficult to get right. And my goal is So you don't just to be clear, you don't price it. If I have you build one role for me on one license, am I gonna pay you $10 a year on average, or what's the general cost?
05:50It's the cost of a human, so it's 6 figures. It's like a 100 k. Like a 100 k for one.
05:54Okay. At least then if she if the agent you build for me handles 10 calls versus handles 10,000 calls, you're saying there's no variability pricing there? There is a variability.
06:03We cap we price based on the size of your business, and then we give you then we have a very good understanding of how much we think it's you're going to consume. So we're doing that on the back end, but we're not metering it. We were.
06:13So there are a lot of customers of ours that are on a metered pricing system, but we were moving and we're shifting over to a world that just makes it easier to know exactly what you're gonna pay for and the outcome that she's gonna produce and what that looks like. Yeah. I would imagine, like, my hesitancy to signing up to something like this is, okay.
06:26If I if I release her on my website and she has a thousand conversations, and I was like, oh my gosh. I thought she's only gonna have five. And now I owe Amanda, like, a million dollars when I thought it was gonna be, like, a 100 k.
06:35That makes it really hard for me to predict, my budgets for with my CFO. Is that did you experience that firsthand? Is that what happened?
06:40No. We we actually set you would buy a bucket of conversations. So let's say you we would estimate, okay, you're gonna have a thousand or 2,000 conversations a month.
06:48So let's call that 12,000 or 24,000 conversations a year, and I would sell you a bucket of those conversations. And now what we're moving towards is we still have that concept, but I'm basically giving you exponential what we think you're gonna do. Like, if you have 10,000 visitors to your site, I think I get 20 to 40% will talk to the superhuman.
07:05If you got all a 100, I'm not going to charge you more. So we're really looking at this of, like, we wanna be in partnership. We don't want to prohibit you and slow you down from using the superhuman.
07:15We don't want you to put her in a corner. We want to give her as much exposure he or or she or they, whatever pronoun you wanna give your superhuman, as much exposure as possible to drive as much value in your business as possible. So we're just trying to align back to the values of our customers.
07:28And so it's kinda taking that away in a way that says, alright. I'm gonna five x. I can't go to 20 x or a 100 x because then I'll be out of business.
07:35But I'm gonna go to five x what we think you're going to use. Guys, remember, I am not just a YouTuber. I'm investing into my third fund.
07:41We've deployed $250,000,000
07:42into 550 software companies so far, again, at founderpath.com. If you're interested in capital, I would love to cut you a check because I know you're investing in your education. You watch my show.
07:53So sign up at founder.com, and when you get the onboarding email, I reply and I see all those. Just reply and say, Nathan, I found you through YouTube, and I'll make sure to prioritize you. I would love to cut you a check.
08:03Check out founderpath.com. It's okay. So fair to say though average customer today is paying somewhere between 100 k and 200 k per year for, you know, one or two agents handling some bucket of conversations?
08:12Somewhere 100
08:13yeah. Three, four hundred k. Yep.
08:14Okay. You have customers today already. I mean, you've already gone.
08:16You have customer I mean, you just launched from what I can tell, but you already customers paying 400 k per year? Yes. That's incredible.
08:21We have had some remarkable in fact, I just did a chart for my team yesterday where I was looking at all the go to market SaaS companies and AI companies. You know, even looking at, like, Clay and Gong and my former company, Sixth Sense. I was the founder and former CEO of Sixth Sense.
08:33I look at the trajectory of the first five years of when they were in business. And our growth curve is like straight up compared to anything we've ever seen in go to market. Well, me push you on It's been pretty wild.
08:44Since you opened the bucket. Can you share how many months from launch did it take you to break a million bucks of contracted revenue? We broke a million from when we went out selling within probably three months.
08:53That's wild. Yeah. Can I ask you what you're doing today or a range?
08:56You can't. Just say we raised very soon after. We've been in market for eighteen months.
09:01So we've, you know, we've come up on a full year of renewal cycles. We had a 211% NDR, which is amazing.
09:08So these aren't experimental budget. Almost every customer went within ninety days of going live adds a second superhuman. So we have actually more opportunities when you were talking about those higher customers within our customer base than net new.
09:20I think a lot of net new are afraid. They're like, wait. Can this do this?
09:23Is it gonna hallucinate? Are we sure this can represent our brand and our product, and this is how we wanna go go to market? But it's absolutely wild when they see the results and they see that this works.
09:31And I can tell you, eighteen months of being live, I have not had one single customer tell me, touch wood, that this doesn't happen, that the superhuman has hallucinated in a way that has negatively impacted their business. The only time we've ever heard somebody say that it's said something they didn't want it to say is because they gave us content that was outdated.
09:46And so I was like, they point to it and like, wait, we don't do that anymore. And I was like, well, let's go look at what you gave us. Then we cite back to the source and like like, oh, shit.
09:53Yeah. We used to say that. Okay.
09:55So she wasn't hallucinating. She was actually using the content. Using your trained data, which was old and outdated.
10:00Interesting. Which is, like, the point of, like, humans today. Everybody's like, oh, everybody needs a human.
10:05They trust humans. I actually think we're about to cross the chasm where people trust AI more than humans because we have the ability to put the AI on really tight guardrails. And if you do this right and once society actually moves to that point, they're going to demand to talk to something like a superhuman or an agent over a human because humans hallucinate nefariously.
10:22Let's be honest. Sales reps do it to get the deal done. Right?
10:25Yeah. Yeah. And sales and sales reps have emotions and robots don't.
10:28I mean, kinds of things. Well, robots can, and they're starting to we're starting to, like, read the tone of the voice. And when you say no, you mean yes.
10:35Like, oh, yeah. Yeah. I'm gonna buy from you.
10:36That actually Yeah. That's funny. Right?
10:38So I love that example. Yeah. Yeah.
10:40If I can't tell you down on a revenue today, are you open to showing a percent, like a growth rate over the past twelve months? Are we talking, like, 500% year over year growth or a 100% year over year growth, or where are you generally? 600%.
10:49600. Okay. That's great.
10:51That's amazing. Now I can kinda tell you down because you told me you broke a million three months in and you're eighteen months ago. So, like, you basically trying to get to my numbers.
10:57We're doing well. Yeah. If you're at 600% growth and we know you're north of a million eighteen months or sixteen months ago, you're doing north of 6,000,000.
11:04I won't make you uncomfortable, but that's point being, it's incredible growth. So let me go back to the backstory now that we know where you're today and what you're selling. I mean, look.
11:11My first thing here wondering, I'm going, okay. She was the CEO at a very successful, I mean, venture backed. Right?
11:16Yeah. It's 6¢. You then went out, and I think you're launching here with, a massive raise versus I'm thinking if she really believes in this, why not use her own money and just fund this herself?
11:24So two questions there. Did you get really rich at 6¢, and why not self fund now? I get really rich at 6¢.
11:30Um, I think the story that I share openly, um, is, yes, I left at a time when we were racing another round. It actually just happened to be that way. It wasn't like when I was leaving, we were timing it and planning it.
11:41I took five years off, so you can do the math. It was fine. Didn't plan on coming back to work.
11:45I always said I was done, and I was on the bench, and I was having kids, and I was happy. Played a lot of golf. Realized that being a mom is the single hardest job in the world, so I decided to outsource that, come back to work.
11:55And did I sell fund? I mean, maybe I put money in, maybe I didn't. I don't really share that.
11:59I don't think it's Fair enough. I I I will push you on that a bit, though. You have a very unique position because you've been through it three times now.
12:05There's a lot of founders listening to you that are much younger than you or I. I mean, when I first launched my SaaS company, I didn't know what the options were. I thought the signal of success was raise VC.
12:12And, like, if you raise VC, everything was gonna be good, but you and I both know. We have friends that raised a lot of VC. And on paper, looks successful, but they made, like, nothing for a variety of reasons.
12:20So I don't wanna make you obviously talk about your personal net worth and stuff like that. But your context from what I understand was you basically built the thing and you left around the series c when the valuation was around 380,000,000 in January 2020. The company then after that did a series d at a 2.1 and a series e at a 5,200,000,000 valuation.
12:37Without talking about what you personally made from those, can you just for anyone else that's in your same shoes, leaving a company that's raised, you know, a 100,000,000 of VC, passing off somebody else. Do you just hold on to equity and then sell it slowly in secondaries? Again, for the market, not what you did, generally.
12:50Yeah. How would someone like you get liquidity? Yeah.
12:51I mean, think you always look at things look, I am somebody this is me personally. I am somebody who wants to be in control of my own destiny. And so the minute I handed the reins away, I lost control.
13:00Right? Like and that was a conscious choice. It was the best thing for the company.
13:03It was the best thing for myself. I put you know, you could call it ten years at it because I was building the product of 6¢ before we actually raised venture prod venture funding. So I have been at it for a very long time.
13:14And so when that control left my hands, for me, it was a time to have some liquidity. I didn't sell the entire thing, but I you know, my entire share, but I sold enough to take to take time off. And, you know, if I never wanted to, I didn't I don't have to go back to So yeah.
13:28And I yes. Of course. When there's next rounds of funding, all, like I I tell some of my friends who are c level operators at some of the most successful AI companies today.
13:36Actually, I have a really good friend down the street, and I'm like, why have you not taken money off? Like, you guys are crushing it right now. You never know what the world we all think that, like, it's gonna keep going in that direction.
13:46But absolutely, when the opportunity arises, like, take some off and then continue to roll the dice and have some and I still have a considerable amount of equity in 6¢. But I I took the risk off the table, and, you know, it looked like a good decision now.
13:59Yeah. I if I just summarize what you just said, it would be founders listening, you know, you can raise cash and also give up control, but don't do both without taking some personal cash.
14:08In other words, don't give up control and take a bunch of cash with no secondary. If you're gonna give up control, take a first bite at the apple kind of thing. Is that a good summary?
14:15Yeah. I mean, for me, when I lost when I was, like, not in control anymore, it's like, great. I will let this ride, but I'm taking a bite or two.
14:21And when you can, You know, everyone should, and it shouldn't be shamed when you do. Right? Like, I've had some incredible opportunities already at OneMind, and it's important that, you know, I look out for my family, and I look out everybody is looking out for at the same time, but I'm also looking out for the employees.
14:36And the decisions I'm making today because I've had that good fortune in the past are very different than the decisions I made when I was at Sixth Sense. So Yeah. I genuinely care about making sure my entire team is gonna see this as a home run for their career and for their families.
14:49And How big is your team today? How many 72, I believe.
14:5272 p your website is so simple. I know 40 a headline. You're coming on the show, but, like, I wouldn't feel that there were 72 people here, especially with you.
15:00I imagine using your own AI bot to sell your own product. All over. So it's it's amazing.
15:04We 78% of our pipeline has been sourced and created via Mindy, our superhuman. She's putting on 8 figures a quarter, if not more, in qualified pipeline. It's wild.
15:15She is having on average, I think it's fifteen minute conversations up to like, for example, we just, uh, sold a deal with, uh, Alteryx as a customer, and the CRO just posted about their superhuman going live. And when he first heard of us, he went and talked to Mindy.
15:27I think it was ninety minutes when we calculated how long he talked to her. Completely went through the whole solutioning, scoping, all the possible use cases, got a demo from Mindy. And my first call with him, it was he and I because earlier in our sales cycle, in our days when I was taking the sales calls, I literally I was like a notetaker.
15:43I was like, I did not ask any questions. I did not do any selling. It was like, Amanda, you know when to just shut up and listen because he's got ideas for what he wants to do with this.
15:51And then we went into, you know, the procurement process, which always takes longer for an enterprise deal. So, like, the deal cycle was a little bit longer, but getting to vendor of choice and a yes was not that long because Mindy did all the work for us. She's also starting to onboard some of our customers.
16:04We have her in our customer success life cycle. We have her as actually our sales engineer. We have one sales engineer right now, and I literally last week told him, you know, your job is gone, and you now need to maintain the brain of your superhuman.
16:17So sales engineer or the superhuman sales engineer will join the calls. Now we're calling him Nigel. So Nigel will be on the calls.
16:23Um, so Mindy will be our inbound. Nigel will be on the calls as our solution sales engineer, and his job now has transformed into maintaining the brain so that Nigel can be on every call. Not just the call that's three d when you're ready for a solutions engineer, but on the first call to answer all the technical questions right there on day one.
16:39But, yeah, we are eating our own dog food, and so a lot of our head count is on our engineering team. They're all very AI forward. So I believe in this How many engineers?
16:4740 some engineers right now. Okay. Wow.
16:50Yeah. My head of engineer my CTO was head of engineering at Rippling prior, so quite a few that came over from there. He is not doing bad when it comes to recruiting.
16:59Oh my god. I've I've seen good teams, and this one's great. So what what is your recommend I mean, predict the future of sort of AI driven sales.
17:06Right? You because you actually have if you guys look at the screenshot here, there's actually a couple of things happening here. HubSpot, I get the sense they're like, okay.
17:11We're willing to test this, but we don't wanna in any way mislead our users. We're gonna say it's powered by AI. Then others like Adam down here at Owner, it's literally it's basically him.
17:20If you didn't know that this was AI, you'd think, well, maybe this actually is actually sort of Adam. Mean, Amanda, do you think we're working to a world where me and you are actually created and it's okay that users are talking to a digital version of us even though it's not really us? I mean, where where do those lines blur over time you think?
17:35Yeah. We're not in the business of cloning, but we do. So we cloned AI Adam because he has a brand.
17:39Right? So he has like, another one is Jack Jocko from Winning By Design, if you know him. But he has a very strong personal brand.
17:47And so he decided to clone himself as well. So we will clone, you know, if it makes sense for the brand of the for our customer. Most of our actually, what an interesting one is Alteryx is they took the founding the founder's wife, and then they made an altered version of her.
18:00So it's a likeness of her, which I thought was great. It was like a a nod and a head nod to her without actually cloning her per se. So the goal is not clones convert more?
18:10I mean, that's the ultimate thing. Does Adam, the owner clone convert higher than Fiona you know, Fiona's Fiona version, basically?
18:16No. I think really it's all about the job that's going to be done by the superhuman and where you put them in the process and the ability to meet the buyer and have that highly relevant contextual conversation and taking them down a path of at the same time, I'm trying to sell to you. And at the same time, I'm trying to upsell and give you the demo.
18:31But I'm also here to answer your questions and meet your needs. I think it's really more of the conversation design and the art. There's a combination of art and science here.
18:39I truly don't believe we have some competitors and ankle biters trying to do what we do. And every time I see them, I'm like, But then you go talk to their their agents, and I call them agents because we have superhuman trademark. But we go talk to their agents, and they're not even close.
18:50You know? It's like Mhmm. Barely answering questions.
18:52It's not natural. They're not going down. They're not having multi goal.
18:55They can't give a live demo. They may show a few slides because they threw three slides in there, but they don't have 10,000 case studies like Alteryx has in their brain. They don't have multiple agents working behind the scenes because what's really happening is you're building, like, a stack of superhumans, if you will, and multiple brains that are all working simultaneously together to keep the latency, keep the cost, and keep, um, the accuracy a 100%.
19:16So you're kinda you're always like, we're dialing. We gotta make sure we don't throw the cost through the roof. She's gotta respond right away.
19:22Otherwise, people think it's not natural. And she's gotta have highly relevant information and allow the buyer to control the experience, but also go for the So Mhmm. There's a lot going on there to make that work and make that work well.
19:33Mhmm. How are you going to market? I mean, I guess you you I think on your website, you said you have 45 customers today, something like that, 45 logos?
19:39Uh, we have over 60 now. But Over 60. So I mean is this is this a high outbound play?
19:43I mean your domain rating for example on Ahrefs, like there's very little organic inbound traffic, but it sounds like that doesn't matter. I mean you're running an outbound playbook? We don't do any outbound.
19:51Actually everything is inbound. I don't know why that Oh. I don't know where that rating is coming from, but I would say all the inbound that's coming to us is highly qualified.
19:58Yeah, I don't know that I can't You're see the getting than 17.
20:02Is that your website Oh, for one Way more. Yeah. Way more.
20:05We of have conversations.
20:07Superhuman Mindy is having thousands conversations a week. So what's the ratio of someone's listening to this going in, should I add this to my website? I get 10,000 hits per month.
20:14Right? What do what would you tell them? Should you say expect that 20% of of random cold hits to your website will engage with Mindy?
20:20What do you think the hit rate is? Yeah. I mean, think about it.
20:22You most most websites have some kind of chatbot on it today. Like, what are you getting in that chatbot today? You're probably getting maybe two to 5%.
20:29You can at least double, if not five x, what you're getting off your traditional chatbot, uh, with a superhuman. And our goal isn't just to be in the real estate of the chatbot. We live our we eat our own dog food or drink our own champagne and the fact that Mindy is, like, front and center on the website.
20:42Right? Like, she is all you that's all you get. However, our customers have it as a chatbot.
20:46They have it as an embedded experience, like, as a banner on the homepage. We have some customers who have it in the top navigation bar, like, talk to our superhuman. So we have it embedded in outbound campaigns.
20:56Let me send you a campaign and have a highly dynamic personalized conversation with that user and offer it instant versus booking a meeting or downloading a white paper. Right? So she lives all over the place.
21:06She lives on LinkedIn profiles, etcetera. Yep. There's a Trying to give a real life example here.
21:09Right? So this is powered by Amanda's company. Right?
21:12One Mind here. This is Winning by Design. So it's one way to install it.
21:15But you said you just told us a bunch of different ways people are using it. Omnilogistics.com. So here, you can see top nav, it says Julie.
21:21That's their superhuman. There she is at the bottom of the page. If you scroll down, there she is as well.
21:25So she is all over there. And they have incredibly
21:28high conversion rates as a result of it and deep engagement. They let you keep the logo there. A little virality built in.
21:34Well, of course. Unless somebody tells us to take it down. I don't think they mind.
21:38It's a partnership. Oh, so is this an iframe? You control this?
21:40Yes. We control this. Yeah.
21:41Oh, interesting. So you have a bunch of I oh, really that's really compelling. So you could push a software update and immediately update all of your customers' websites at once without them having to reinstall new JavaScript.
21:49Oh my god. A 100%. Yeah.
21:51So super And we're constantly it may be because, you know, your question earlier is what models, what faces, what things are you using. We're constantly evolving. So today, we might be using OpenAI.
21:58Tomorrow, we might be using Gemini, and, like, it's a combination. So we might be using smaller models for, like, the answering high or what's the weather like, and then we use, like, deeper models and more reasoning models, like, for the, you know, more complex questions. And then when she needs to give a live demo, it's a whole another set.
22:12So all of these things are working together, and we're constantly optimizing and trying to stay up on all the latest. I mean, the one of the hardest all those credits you include all those credits in your cost of goods sold? Yes.
22:21Can I ask, I mean, what was your cost of goods sold last month on credits? Are we talking like a million dollar a month kind of bill, or are you, like, tool calling to try and, like, keep that, I mean, obviously, as low as possible? Doing a lot of tool calling to keep that down.
22:30Yeah. Yeah. So it's it's not as bad as you think it is.
22:32I will tell you it's like build SaaS margins in this world? Can you keep your margins at, 90%? A 100%.
22:38We're yes. Yes. Okay.
22:39We have found a way in the slot of caching. Like, there's a lot of multiple things that we can do keep this down. I'm not trying to support like, I'm not going after the support use case, which is where you see most of the AI agent tech, you know, tools in this world.
22:51The Decagons and the Sierras of the world. Yep. They're solving the problem of, know, solving support cases and tickets.
22:57That is a very low margin business. Right? So, like, you can't have a million conversations and have it cost a lot because there's not a big upside to that.
23:04If I'm closing a million dollar deal for you and I'm moving pipeline through and revenue through, like, the justification, if this is the equivalent of, you know, going back to HubSpot, 89 SDRs for the cost of one, the ROI is there in spades on the efficiency numbers, let alone the impact on revenue, which was an increase of 25% on their SMB business.
23:22So easy to justify what we're spending here, and we have very we have a very healthy business. You guys are watching this. You it's like she's done it before.
23:29Right? It's like she's done it before. So this is great.
23:32Amanda, as we wrap up here, you just you know, 30,000,000 series a closed. What was the Yeah. I'm not gonna share that either.
23:39For AI founders listening that maybe have a million, 2,000,000, they're watching, what is the market right now generally trading at for, you know, AI companies raising a series a with a couple million of ARR? I don't really pay attention to other people. I am so laser focused on myself.
23:51I know you try to get you got at my, like, where you think my revenue is, but I'm not gonna say that even though Now I can't ask anything else. 600 x off of whatever point in time. So, like, maybe you were close.
24:02Maybe you're I asked very specifically. I I no. I'm not gonna say you were You're funny.
24:07I love this. Doing really well. You might have been under.
24:09Oh, yeah. Okay. Fair enough.
24:10Look. I'm obviously rooting for you. It's a very cool space.
24:12If people wanna follow on Amanda with your story, where can they find you online? Uh, LinkedIn slash Amanda Kahlo. Guys, there, you have it.
24:18A ton of success at her first two companies. You might have heard of them at, uh, at Sixth Sense. Now she's building OneMind.
24:25She's scaling now with 60 customers like HubSpot, like owner.com with their basically, they're the, like, AI agents, but superpower. There's a real face. They join Zoom calls.
24:32They talk like a human. And you know what? They qualify and sell like humans.
24:36That's why the ROI makes sense. She's got individual customers now already paying just eighteen months after lunch. $400,000.
24:41Her model is a licensed fee per role model. So per Mindy or per Fiona or per the Nathan that you launch on your website. Took them three months to break a million bucks of contracted revenue, and all she said was over the past twelve months, you know, a 600% growth rate.
24:54I'll let you take that or leave it however you want. Team of 72 people, 40 engineers, watch out for Amanda. Amanda, thanks for taking us to the top.
24:59This was great. Super fun. Nice to meet you, Nathan.
25:02You won't believe this CEO's revenue. Click here to watch the next episode right now.
The Hook
The bait, then the rug-pull.
Amanda Kahlow launched 1Mind and hit $1 million in contracted revenue inside 90 days. The company is now at 600% year-over-year growth with 60 enterprise customers paying between $100K and $400K per agent per year. Nathan Latka did the math live.
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