Sep 17, 2025 · 29m · the-pitch
$1.4M Ask: Can AI Fix Dirty Hotel Rooms?
Al Lagunas · 13m spoken Jesse Middleton · 2m spoken Jenny Fielding · 2m spoken Josh Muccio · 2m spoken Elizabeth Yin · 1m spoken Paige Finn Doherty · 1m spoken Charles Hudson · 8s spoken
0:00 / 0:00
▶ Watch on YouTube →
gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
Founder Al Lagunas pitches Levy, an AI-powered room inspection and workflow assistant for hotel housekeepers, seeking $1.4 million on The Pitch. Although the investor panel is impressed by his frontline empathy and grassroots sales hustle, valuation impasses and concerns over hospitality sales cycles prevent a deal.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Josh holds 10% of the talking time here. How this is scored →
Josh as informed peer 2.5 Guest teaching 1.9 Guest disagreement 0.6 Josh pushing back 1.5
speaking balance: gold is Josh, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 24:50 Al rejects the 5M valuation offer
When Josh asks if Al would take Elizabeth's $5M cap offer, Al immediately pushes back and declines taking that low of a deal on day one.
Hardest push from Josh ▶ 24:01 Elizabeth deconstructs AI branding and valuationElizabeth directly refutes Al's framing of being an AI company, arguing the tech is off-the-shelf and justifies no more than a $5M valuation.
Biggest teaching moment ▶ 8:11 Industry inspection reality checkAl surprises the panel with insider hospitality data, explaining that 45% of hotel rooms never get inspected and that most inspection time is wasted on inventory counts.
Josh holds their own ▶ 26:31 Jenny spots burn rate math mismatchJenny demonstrates technical and financial rigor by pointing out that Al's stated $46k monthly burn does not mathematically align with a 1.4 million dollar raise lasting 18 months.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Josh as informed peer | Guest teaching | Guest disagreement | Josh pushing back | Why |
|---|---|---|---|---|---|---|
| Meet the Investor Panel | 0 | 1 | 0 | 0 | Al gives his opening pitch highlighting his mother's background as a housekeeper and presenting the labor shortage metrics in hospitality. The investor panel and host listen without interruption or technical challenge. | |
| Levy Product Workflow and Demonstration | 2 | 3 | 1 | 1 | The investors ask basic discovery questions regarding how the app works, workflow automation, and pricing. Al explains the $3 per room model and educates them on the 55% inspection rate in the hotel industry. | |
| Market Expansion and Long-Term Vision | 2 | 3 | 0 | 0 | Jesse and Paige ask about long-term market opportunity. Al explains his vision of vertical expansion into procurement, HR payroll KPIs, and real-time room turnaround data. | |
| Enterprise Sales Hustle and Trade Show Strategy | 1 | 2 | 0 | 0 | Al recounts his hands-on enterprise sales strategy, including emailing CTOs and building mock hotel rooms at trade shows via U-Haul and IKEA furniture. | |
| Founder Background and Round Economics | 2 | 1 | 0 | 0 | Elizabeth and Jenny inquire about Al's past career at Amazon and startup experience, along with runway and financial metrics. | |
| Onboarding Experience and Housekeeper Feedback | 3 | 2 | 0 | 2 | Paige and Jenny ask about user pushback and onboarding friction. Charles recuses himself due to portfolio conflict, while Jesse expresses hesitation on operator sales cycles. | |
| AI Capabilities and Technological Evolution | 5 | 2 | 1 | 4 | Jenny probes whether the company is truly an AI play rather than an operations tool. Elizabeth directly challenges the $10-12M valuation cap, stating seed requirements demand more than one customer. | |
| Negotiating the Cap and Pitch Wrap-Up | 5 | 1 | 3 | 5 | Elizabeth offers a $5M valuation valuation cap and outright rejects the AI framing, which Al politely declines. The investors deliberate post-pitch on AI commoditization and financial modeling discrepancies. |
Statements from this episode (24)
Lagunas: US hospitality industry has 248,000 job openings
“In hospitality alone, there's 248,000 job openings.”
Lagunas: Levee converted world's largest hotel brand into an active customer
“Since we launched in July, we launched a paid pilot with the largest hotel brand in the world who is now an active customer.”
Lagunas: Levee has over $30 million in its sales pipeline
“We have over thirty million dollars in our sales pipeline right now from brands that are coming from all over the world to implement our solution.”
Lagunas: Levee verifies hotel room quality in real time via video
“From the beginning, our whole design process is around having one button. One button initiates the app, and what you do is you scan the room number. That room number pulls in the quality control checklist for that room layout, and as they go through the room, …”
Lagunas: Levee charges a base fee of $3 per room monthly
“Three dollars per room per month, and that's our base fee.”
Lagunas: Only about 55% of hotel rooms get inspected in hospitality industry
“Little kind of, you know, no pun intended, dirty fact about the industry is that only about 55% of rooms get inspected in the hotel industry.”
Lagunas: Levee reduced average hotel room inspection time from 8 minutes to 2:51
“With the hotel, like, they were allotted eight minutes to inspect a room. What the hotel didn't know until we were, we got there is that five of those minutes were spent correcting inventory errors, not actually inspecting for cleanliness. So even just from th…”
Lagunas: Most Hotels Aspire to Have Room Inspection Programs but Cannot Afford Them
“A majority of hotels aspire to have a room inspection program. If you're lucky, you can afford to have a room inspection program.”
Doherty targets 'clipboard companies' bringing vertical AI to legacy industries
“One, like, core element of our thesis in the future of work is backing clipboard companies, which is like verticalized AI companies building in industries that haven't seen a lot of innovation in a while.”
Lagunas: Levee's enterprise sales bottleneck is educating hotel middle management
“The challenge that we'll have in going to market is the people at the top get it. The users get it. The people in the middle, they're the ones that we have to educate.”
Lagunas targets $1M+ ARR and 96 hotels for Levee in 18 months
“Million plus ARR, 96 hotels, different brands, different management companies, but that's what we're targeting for over the next 18 months.”
Hudson: Passing on Levee due to potential portfolio conflict
“I have something that I'm trying to figure out if it's too competitive, and I think it might be. So I think I probably can't do this one.”
Lagunas targets a $10M to $12M valuation based on AI startup comparables
“10 to 12 from, like, market comps for post revenue, you know, AI companies.”
Middleton: Hotel operators and executives are finicky and hard to sell to
“I worry what the process looks like in selling the operators of hotels. Like I've known many people who've owned and operated hotels. I've known people at the corporate side. They're all finicky.”
Lagunas claims there is no data left to train desk-worker AI models
“When we look at, you know, AI, there's no more data to train these models on. There's no more data to do anything from like a desk worker perspective.”
Lagunas: Levee's first product was a sensor-equipped smart cleaning bottle
“The first product we had built was a smart cleaning bottle, which was awesome. Would have been a terrible business. Used spatial mapping technology and like IMU sensors to map a surface as you cleaned it.”
Yin: The seed valuation bar requires more than one single customer
“And I think more broadly speaking, between pre-seed and seed, like, I'd say seed, which is kind of where the valuation range is that you're looking at, the bar is higher than landing one customer.”
Fielding: Everywhere Ventures Will Not Invest at $12M Valuation
“So we wouldn't invest at a 12, but as the round comes together, we would be interested.”
Yin: Levee is not a true AI company and lacks unique technology
“Because I think this is not an AI company, and, like, the technology is not unique.”
Yin: AI will benefit end users, but who will actually make money?
“Because I think AI technologies in the long run are going to be great for consumers and end users, but I don't know who makes money.”
Middleton: Object detection on shaky mobile room video is technically difficult
“It's not easy to recognize in shaky video on a cell phone walking around a room if something is out of place.”