Omnily LLC
Raising $80K to complete MVP and Beta test of patent-pending, industry disruptive personalized AI-based mobile search app.
***Executive Summary, Deck, comprehensive Financial Model and Investor data room access available upon request.
OVERVIEW
OVERVIEW
Omnily is a patent-pending, industry disruptive, online search mobile application that provides a user with an online search method for obtaining unique and personalized recommendations for consumer venues such as restaurants, hotels, fitness centers, hair salons, nightclubs, tourist destinations, retail establishments and sports and entertainment venues. By providing Omnily with some of a user’s demographic and psychographic data, personal preferences and personality traits, Omnily will provide unique search results and recommendations most appropriate for each user.
Searching online to find “what to do” or “where to go” normally involves using multiple search engines and/or apps and sifting through an overwhelming amount of irrelevant search results, wasting valuable time and getting frustrated. With the vast amount of data available online, it should be simple and easy to find exactly what you’re looking for – but it’s not. The problem is that search engines and most apps don’t know anything about you or your preferences, so everyone is treated alike – whether you’re a 22 year-old single female living in rural Arkansas, a 35 year-old married male living in suburban New York or a divorced 59 year-old male living in Charleston, SC. Everyone receives the same results based on the same query (e.g., “Best seafood restaurants in Boston”). Furthermore, search engines are motivated to steer the user to results that will generate advertising revenue for them.
Omnily is designed to provide its users with customized search results because it gets to know each user and learns more about them, the more they use it, by harnessing the power of artificial intelligence.
THE OMNILY APP
Upon downloading Omnily, users will be asked a short list of questions to gain some demographic and psychographic data that will be used to generate micro-targeted results for any category of venue selected. The data can be saved for future use or deleted upon logging off. Users can answer all, any or none of the questions. The more information shared with Omnily, the greater the accuracy of its results.
| Sample questions: Age: Sex: Marital Status: Annual Income Range: Personality Trait: Leisure Preferences: Favorite Brands: | Sample of user’s completed profile: 36 years old Male Divorced $100,000 - $200,000 Extrovert Golf, Reading, Fitness BMW, Kettle One, Rolex, Ritz-Carlton
| | Users then enter detailed query for:
“Restaurants that play live music (in Los Angeles)”
…and Search!
How it Works
Omnily utilizes a patent-pending process that queries an AI-based large language model (LLM) trained system using the user’s inquiry and personalization data, then validates each venue by accessing a first venue information database and then a refined list of suitable venues.
It then provides the five most suitable results or “cards”, complete with easy to review venue images, descriptions, reviews, and direction maps.
REVENUE MODEL
Omnily will generate revenue through multiple streams which include:
· Hotel booking commissions: We estimate that we will receive approximately 20% of a reservation booked through Omnily. The industry average commission is 10%-30% of a hotel reservation booked through an online travel platform or app.
· Restaurant booking commissions: We estimate that we will receive approximately $1.50 for each restaurant reservation booked through Omnily. The industry average commission is approximately $1.75 per restaurant reservation booked through an online restaurant platform or app.
· Advertising revenue: We estimate that once Omnily accumulates 20,000 users, we will begin generating revenue from the sale of Banner Ads, Native Ads and Pop-up Ads to advertisers. The Cost Per Marketing to advertisers ranges from $0.40-$1.00 per search.
· Data monetization: While generating revenue from the sale of permissible user data is typical with many mobile apps, key metrics to sales vary greatly. Therefore, since the amount of revenue generated is currently unclear, we have not included it within our projections.
· Premium subscription:Users will have up to 5 free searches per day after which they will have the option to upgrade to the “Premium” subscription for 99¢ per month. We estimate that 5% of Omnily users will upgrade to the Premium subscription.
MARKET ANALYSIS
The Market
The overall markets Omnily addresses represent an aggregate $11+ trillion, which is comprised of three enormous sectors: Travel & Tourism Market ($9.5 trillion)[1], Hyperlocal Entertainment and Services ($1.5 trillion)[2] and AI-Personalization ($442 billion)[3]
Travel & Tourism CAGR: 5.3%
Hyperlocal Services CAGR: 28.4%
Artificial Intelligence (AI) CAGR: 20.5%
Additionally, the search market, which generates over $200 billion in annual revenue, has not seen any significant improvement in the overall user experience in almost two decades. Other than periodic incremental upgrades in the quality of results, searches today are becoming ever more cluttered with bloggers and other paid advertisers jockeying for first page position on search page results. The overall quality (e.g. relevance) of search results and the user experience have been lost in the pay-for-play universe of search. With the recent momentum of AI and the advent of ChatGPT and Copilot (formerly Bing Chat) over the past several years, the search playing field is rapidly changing. Users will no longer settle for clunky, irrelevant paid-for search results when they can now access more accurate, higher quality results using a search engine incorporated with an AI-infused, LLM.
Google currently dominates search queries with approximately 85% market share and possesses, what many consider, the best LLM technology. The problem Google faces is that its highly successful revenue model is based primarily on advertising, which delivers low-quality results when now compared with LLM search. Firstly, if Google were to incorporate LLM technology in its search engine, it would do so at the risk of losing advertisers (revenue). Secondly, according to tech industry research publication SemiAnalysis[4], Google could expect to spend $100 billion in capex and another $36 billion each year in inference (LLM output) costs. Therefore, SemiAnalysis suggests, “If ChatGPT-like LLMs are deployed into search, that represents a direct transfer of $30 billion of Google’s profit into the hands of the picks and shovels of the computer industry."
We also believe that search might become fragmented as niche players with LLM engines (i.e. Omnily) begin to come online. For example, there exists the potential for an automobile LLM search engine engineered specifically for auto enthusiasts seeking detailed information about cars, or a history search engine for history buffs. A prime example is the dating app industry, which was dominated in its early years by “generalist” pioneers Match and eHarmony. The industry evolved over the past three decades, fragmenting into numerous niche dating apps serving the dating needs of specific interest groups based upon religion, ethnicity, age, and sexual preference (e.g. JDate, Christian Mingle, Bumble, Tinder, FarmersOnly). Many of these niche apps have since been consolidated by the industry leader (Match Group). We believe that search may follow the same paradigm. However, regardless of this hypothesis, search disruption has arrived.
Target Audience
Any adult between the ages of 20-70 years old is a potential user of Omnily, however, the 30-55year-old group is expected to be the primary targeted audience, estimated to comprise approximately two-thirds of all Omnily users.[5][6][7]
Competition and Differentiation
Omnily provides users with unique and personalized recommendations for consumer venues such as restaurants, hotels, fitness centers, hair salons, nightclubs, tourist destinations, retail establishments and sports and entertainment venues, based upon users’ demographic and psychographic data, personal preferences, and personality traits. The five most suitable results are provided to users, complete with venue images, descriptions, reviews, and direction maps. Results can be saved as “favorites’, “shared” with friends or groups via social media, sync results to their calendars and, most importantly, directly book reservations with the push of a button from Omnily's results “cards”.
None of the existing primary online search competitors currently offer Omnily's full suite of capabilities (see comparative chart below).
Geographic Footprint
While Omnily's technology has the capability to provide accurate and optimal results anywhere in the world, it will initially be launched in Charleston, SC and the United States, followed by Canada, Latin America, Europe, Asia, the Middle East and Africa.
SALES, MARKETING, PROMOTION & PRICING
Sales
Direct sales initiatives with live personnel will be utilized to recruit independent and chain restaurants and hotels to the Omnily platform.
Advertising
Omnily will initially utilize a three-pronged approach for user acquisition:
· Social media: (a) Advertising and retargeting campaigns on Instagram, Facebook, Tik-
Tok and YouTube.
(b) Organic sharing of favorites among existing users.
· Influencers: (c) Sponsored posts, initially utilizing local micro-influencers with high
user engagement, followed later by national influencers.
Promotion
· Sponsored events: (a) Local events and venues such as small music festivals, entertainment
venues, minor league sports teams, followed later by world class events and large venues (e.g. NFL Super Bowl).
· Partnerships/
Collaborations: (b) Cross-platform partnerships with other apps (e.g. dating apps Hinge,
Bumble, Tinder for dating venues).
(c) Strategic Partnerships with major entertainment companies (e.g., Insomniac Promotions offering their attendees access to certain special event locations at a music festival).
Pricing
According to mobile app developer “buildfire”, 98% of global mobile app revenue is generated by free apps. When you combine this with the fact that the entire world is now accustomed to free search, Omnily is free to download and use with certain limitations - five free searches per day unless the user upgrades to the “premium subscription” for 99c per month that permits unlimited searches.
Critical Success Factors
· First or second mover status.
· Sufficient capital to complete MVP, beta testing and go-to-market launch.
· Significant market penetration and achieve critical mass of user adoption.
· Hiring high-level talent with significant industry experience.
· Quick and critical mass enrollment of hotels and restaurants.
SWOT ANALYSIS
Strengths
· Unique, patent-pending technology that powers the app, which currently has no known direct competitors.
· Leadership team with significant development-stage enterprise experience.
· Advisory team with significant industry experience.
· Scalable business model with significant profit margin and limited overhead and infrastructure requirement.
Weaknesses
· Development-stage enterprise with limited resources, industry experience and manpower.
· Brand recognition not yet established.
Opportunities
· $11 trillion addressable market ripe for disruption.
· Rapid user adoption and demonstrated proof of concept within 24 months could make Omnily an attractive acquisition candidate.
Threats
· Large, multinational indirect competitors with significant resources and industry experience could engineer similar mobile applications utilizing a variant technology.
· Failure to gain significant user adoption in a timely manner.
· Failure to enroll hotels and restaurants in a timely manner.
MANAGEMENT AND ORGANIZATION
Executive Team
| Michael Novielli | CEO | Founder, Dutchess Capital, value >$1B. +30 years’ business experience. Investor. Drove strategic growth, including into Europe, Asia, Australia, & Latin America.
| Joseph Zappulla | COO | +30 years’ experience finance & investment banking. American Express, DLJ, worldwide management team for SG Warburg & Co. Restructured Mabon Securities
| Thomas Jacobs | Digital Marketing/ Customer Acquisition | Founder of Ad Spartan digital ad agency. Extensive experience with digital marketing deployment, including algorithmic integration for performance and user acquisition.
| Jeffery Bobby | Business Development, Sales & Social Strategy | Sales representative with Northeast Medical. Business development, marketing, and sales support, Dutchess Capital, Social Outreach & VIP Management Charleston, SC nightclubs.
| Joseph Zappulla | COO | +30 years’ experience finance & investment banking. American Express, DLJ, worldwide management team for SG Warburg & Co. Restructured Mabon Securities
| Thomas Jacobs | Digital Marketing/ Customer Acquisition | Founder of Ad Spartan digital ad agency. Extensive experience with digital marketing deployment, including algorithmic integration for performance and user acquisition.
| Jeffery Bobby | Business Development, Sales & Social Strategy | Sales representative with Northeast Medical. Business development, marketing, and sales support, Dutchess Capital, Social Outreach & VIP Management Charleston, SC nightclubs.
SUMMARY FINANCIAL OBJECTIVES
| | | Months
| | | 1-3 | 4-6 | 7-9 | 10-12 | 1-12 | 13-24 | 25-36
| Revenue | | | | | | |
| | Advertising | 1,480 | 7,400 | 22,300 | 42,800 | 73,980 | 376,000 | 1,056,000
| | Subscription | - | 3,370 | 25,350 | 48,560 | 77,280 | 426,000 | 1,199,000
| | Pay-Per-Use | - | - | - | - | - | - | -
| | Data Monetization | - | - | - | - | - | - | -
| | Booking Fees | - | - | - | - | - | 10,339,000 | 29,058,000
| | Total | 1,480 | 10,770 | 47,650 | 91,360 | 151,260 | 11,141,000 | 31,313,000
| Revenue Costs | 108,700 | 109,700 | 252,000 | 373,100 | 843,500 | 1,636,000 | 1,608,000
| Gross Profit | (107,220) | (98,930) | (204,350) | (281,740) | (692,240) | 9,505,000 | 29,705,000
| Operating Costs | 53,530 | 78,460 | 219,000 | 219,870 | 570,860 | 2,411,000 | 5,274,000
| Operating P&(L) | (160,750) | (177,390) | (423,350) | (501,610) | (1,263,100) | 7,094,000 | 24,431,000
| | | 1-3 | 4-6 | 7-9 | 10-12 | 1-12 | 13-24 | 25-36
| Revenue | | | | | | |
| | Advertising | 1,480 | 7,400 | 22,300 | 42,800 | 73,980 | 376,000 | 1,056,000
| | Subscription | - | 3,370 | 25,350 | 48,560 | 77,280 | 426,000 | 1,199,000
| | Pay-Per-Use | - | - | - | - | - | - | -
| | Data Monetization | - | - | - | - | - | - | -
| | Booking Fees | - | - | - | - | - | 10,339,000 | 29,058,000
| | Total | 1,480 | 10,770 | 47,650 | 91,360 | 151,260 | 11,141,000 | 31,313,000
| Revenue Costs | 108,700 | 109,700 | 252,000 | 373,100 | 843,500 | 1,636,000 | 1,608,000
| Gross Profit | (107,220) | (98,930) | (204,350) | (281,740) | (692,240) | 9,505,000 | 29,705,000
| Operating Costs | 53,530 | 78,460 | 219,000 | 219,870 | 570,860 | 2,411,000 | 5,274,000
| Operating P&(L) | (160,750) | (177,390) | (423,350) | (501,610) | (1,263,100) | 7,094,000 | 24,431,000
[1] News Article | World Travel & Tourism Council (WTTC)[2] Hyperlocal Services Market Size, Share | Industry Report, 2031 (alliedmarketresearch.com)[3] Artificial Intelligence (AI) based Personalization Market (globenewswire.com) [4] SemiAnalysis: The Inference Cost of Search Disruption – Large Language Model Cost Analysis [5] Travel Statistics By Gender: 19 Facts Trends To Consider (Explained) (rvandplaya.com)[6] Frequency of eating at restaurants by age US 2022 | Statista[7] Frequency of eating at restaurants by gender US 2022 | Statista
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