Josh Sapienza


"Course Restaurant Guide matches you with restaurants based on your personal taste because when it comes to taste - no opinion matters more than yours."

Genuine Hospitality Is Mass Personalization

"Is personalizing restaurant recommendations without editorialized reviews or score averaging an entirely new concept? No. Not by a long shot. But those who have demonstrated success have built their recommendation platforms with a heavy reliance on "what people tell the world"  (i.e., other people's opinions and social media activity) which may have been groundbreaking 5-10 years ago but that was in the pre-bot browsing era and prior to online social media activity (i.e., likes, shares and comments) being monetized and used as a means to an algorithmic end.

Audience Effect tells us that online public behavior is not a perfect reflection of  genuine interest or feeling. The mere fact that online ratings are for the benefit and attention of others, demonstrably influences those ratings and reviews. That's one of the reasons we’ve found anonymous first-party granular data to be the most reliable, informative and immediately actionable data out there.

3 out of 4 Americans now consider themselves"foodies" and many are willing to adopt a more philanthropic approach to restaurants in exchange for the critic-culture mindset that plagued the industry with challenges that literally endanger millions of independent restaurants that are now also dealing with the hardships associated with 2 year global pandemic and current geopolitical events.

Traditional public rating platforms still encourage guests to issue one-size-fits-all scores to restaurants like food critics but modern technology now affords us the ability to analyze personal taste and help people find more restaurants they'll love without having to build another public review soapbox that serves more as a place for people to air their grievances than it does to build bridges  between customers and restaurants that they'll enjoy.

Using first-party data to predict compatibility is not only doable - it's a more practical approach to making customers happy... and much more restaurant-friendly as well."

Marc Randolph


"What I love about applying data to taste is that I believe it's completely possible and you should be prepared to surrender yourself to the data."

Yelp is Our Blockbuster

"Blockbuster was 1,000 times our size... a $6 billion dollar giant  that dominated the home entertainment business with almost 9,000 rental stores around the world, 60,000 employees and was owned by Viacom, the most valuable media company in the world at the time. 

[Like Yelp] Blockbuster had the brand, power, resources and vision but we had one thing that they didn't have: a culture that valued people over product... Everyone went to Blockbuster. Like lemmings they'd all flock to the most popular gig in town but people also hated Blockbuster - and you always want to compete against someone that everyone hates. 

The problem with Blockbuster was that  they did not believe that they were in the customer service business and instead were reliant on a model they referred to as 'managed dissatisfaction' [ergo a lot like Yelp] - and you definitely want to compete against someone who is in the business of managing dissatisfaction."

Managing one's online reputation takes time and money most independent restaurant operators simply don't have. The fact that Course employs private [instead of public] reviews to fuel it's artificial intelligence engine proves that it's not only possible to serve a customer's interest in finding "the best restaurants near me" but that it can be done without threatening the online reputations of the restaurants that simply might not be compatible with that person's particular taste. 

I think the time has come for a recommendation platform that doesn't cause so much collateral damage or cost restaurant operators more time and money.

Taste Is Personal.

"When it comes to taste no one's opinion matters more than yours."

Potential membership if capturing 1% of the market share held by traditional leaders:


1% of Yelp's 73 Million users*.

Trip Advisor

1% of TripAdvisors 400 Million users*.


1% of OpenTable's 1.572 billion users*.


1% of Zomato's 384 million users*.

Membership Goals & Projected Gross Profit 

2022:         200 Members          ($2,307,000.00)/yr.

2023:       2,000 Members         ($2,196,272.00)/yr.

2024:      25,000 Members        ($2,443,272.00)/yr.

2025:     100,000 Members        $4,397,708.80/yr.

2026:     500,000 Members        $115,017,200.00/yr.

2027:    1,000,000 Members      $229,120,450.00/yr.

Market Availability

"Restaurant reservation platform OpenTable aquired Ness Computing (LikeNess) the personal restaurant recommendations app  in an all cash transaction worth $17.4MM in 2014...Ness used machine learning technology, deriving results from social signals that you provide it from Facebook, Foursquare and Twitter as well as other online social behavior to figure out which places you'll like the most.." aquiring 200,000 users in Year 2 and 300,000 downloads by Year 3.

Tech Crunch

"Happy Cow, a restaurant finder app focusing on and rating only  a restaurant's ability to cater to customers with dietary restrictions, food allergies or specific dietary preferences (and not on the quality of food, service or atmosphere) has attracted over 450,000 members. in 2019*"

Natural Products Global | GreenQueen UK.



Chip Conley


Synergy With The Lodging Sector

"You could have your best or your worst experience at an Airbnb and so  - with a more adventurous traveling public, what comes with it is more of a willingness to try out a boutique hotel or to try out an Airbnb experience because if there’s a high likelihood of it being your best experience...and you can sorta figure out how to make sure it’s not your worst experience, then it’s worth trying.”

That’s precisely what we’re doing with Course: We're increasing the likelihood  that the next hospitality-based experience you try is a positive one... so that you'll try something new again.  

Course does this simply by matching people with restaurants that complement their own individual taste & unique personal preferences... regardless of whether they're staying at a boutique hotel, Airbnb or at home. 

Because a big part of living like a local is eating like a local. 

Funding Rounds

Detailed Plans & Financials Avail Upon Request by Financially Qualified Parties. 
Open to Traditional Equity, Convertible Debt & Tranche Investment.

Seed Round


Advertising & Mktg 


$1.32MM x 3
Acctng, Legal HR & Insur
$123k x 3

$850k (13.5%)

 Series A Round


Advertising & Mktg


Acctg, Legal, HR & Insur 


 Y4/5 Payroll 


We're thrilled to announce that we've successfully self-funded our MVP, Pre-Seed Round of financing and launched the beta version of Course Restaurant Guide for PWA, Android and Apple. We are now seeking angel investors to assist with our Seed round.

Hawser's unique approach of merging machine learning with private restaurant ratings yields an incredibly intuitive and industry-friendly restaurant concierge service that has been well received among industry professionals and ardent supporters of independent restaurants. This is evidenced by the growth of and membership in our focus groups that presently include chapters of collaborators in Pennsylvania, New Jersey, New York & Florida.

With the completion of our Seed Round, we anticipate self-funding our Series A round primarily from revenue generated by advertising within the app and in-app purchases offered in 2023.

Course is committed to helping people find more of what they love and our Enterprise Members will enjoy unlimited access to exclusive and robust STR-type data for their outlet(s). 

Is it an entirely new approach? No. Not by a long shot but those who have previously demonstrated success, via lucrative exits, have built their recommendation platforms with a heavy reliance on what "people tell the world" (i.e., other people's opinions and social media activity) which may have been groundbreaking 5-10 years ago but  that was in the pre-bot era and prior to online social media activity (e.g., Likes, shares and comments) being monetized and traded as currency on the open market.

Course's artificial intelligence does not rely on behavior that may or may not accurately reflect genuine opinions, biases or feelings (see: Audience Effect) or require "following" other members who are deemed "experts" or "trusted sources". Course understands that taste is personal and anonymous first hand data is the most reliable data available now and in the foreseeable future. 

As a service provided by a Public Benefit Company, Course is not designed to be as profitable as possible. That's why we're powered by Google instead of allocating time, money and resources to collecting and owning restaurant data. Course is designed to be as practical and beneficial as possible by employing humane technology and treating our members like valued customers and guests instead of "users". Their data is the data we rely upon and are committed to protecting.

Our purpose is simple: facilitating offline community building by making culturally rewarding experiences more accessible and supporting independent businesses with our hospitality infused technology.

We believe our values are our vertebrae and that by keeping our members' data anonymous, agnostic and uncompromised; we ensure both the sustainability and adaptability of our platform.

The non-identifying data we collect and present to Enterprise Members via easy-to-read customizable dashboards will offer unprecedented insight into any market segment's dining patterns by revealing what, why, when and where guests are eating; but our member's personally identifying information is not for sale.

Following Course 3.0 and the introduction of an Enterprise version, Hawser plans to expand Course into multiple markets and various industry applications (e.g., Meta & Web3) starting with the lodging industry. 

With Course 3.0, members will be able to privately rate lodging accommodations and view their compatibility with other STR & LTR options throughout the world....especially those strategically located to serve as a home base for culinary excursions that enable a member to check off as many restaurants on a particular bucket as possible - in just one stay.

The lodging algorithm we're currently developing is based on the same scaffold as our restaurant & bar A.I. - the main differences being the nine elements that members privately rate....namely: 


With sufficient investment, we project (and are committed to) driving gross annual revenue upwards of over $20MM and an IRR of between 32 and 33% by Year 5. In order to meet these goals, we must ensure that Course doesn't become just another great idea that no one's ever heard of.  

So, while growing our teams and our server's bandwidth is imperative, aligning ourselves with the right capital partner who recognizes the importance of aggressive advertising/marketing at this stage is paramount.

Our ideal angel investor is a family or firm with existing holdings or experience within the hospitality, travel or technology sector(s) and whose primary business practices are consistent with the core tenets of hospitality.  

     Josh Sapienza  |  Hawser LLC.

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