Wellim - A Treatise on Synchronized Movement
Wellim - A Treatise on Synchronized Movement
Give travelers a more personalized and memorable trip
Give travelers a more personalized and memorable trip
01 Overview
01 Overview
Wellim is an insights-driven service engineered to expand opportunity within the world of hospitality and wellness. The service addresses the critical market dislocation caused by the diminishing returns of basic, brand-based loyalty programs, which are no longer sufficient to attract and retain the highest echelon of patrons (Dowling & Uncles, 1997; Favier et al., 2024). Wellim provides a modern, data-driven framework to empower unique properties to "treat their best with better." The platform's objective is to forge a more symbiotic relationship between patrons and properties by merging the emerging technological revolution in agentic AI with the timeless spirit of service. At its core, Wellim is a tool for synchronizing complex action models for mutual gain, enabling individuals and organizations to achieve their maximum potential for fluid, unimpeded movement.
Wellim is an insights-driven service engineered to expand opportunity within the world of hospitality and wellness. The service addresses the critical market dislocation caused by the diminishing returns of basic, brand-based loyalty programs, which are no longer sufficient to attract and retain the highest echelon of patrons (Dowling & Uncles, 1997; Favier et al., 2024). Wellim provides a modern, data-driven framework to empower unique properties to "treat their best with better." The platform's objective is to forge a more symbiotic relationship between patrons and properties by merging the emerging technological revolution in agentic AI with the timeless spirit of service. At its core, Wellim is a tool for synchronizing complex action models for mutual gain, enabling individuals and organizations to achieve their maximum potential for fluid, unimpeded movement.
02 Core Idea
02 Core Idea
The central idea of Wellim is to engineer "smooth" experiences by abstracting away unneeded steps and information for patrons, allowing value to be delivered in the background. The platform's technology is defined by "Speed and Accuracy which translates into Smoothness and Serendipity as an experience". Philosophically, Wellim is not concerned with moral discussion but is instead hyper-focused on a "praxis approach to action-reaction" within existing models. It is based on the principle that through a pure computer interface, it is possible to impose one's will on large groups of individuals (such as a hotel staff) without direct communication with a single new human party. This tests an individual's "freedom to move," making Wellim a "spiritual statement about self reflection within a shared system".
The central idea of Wellim is to engineer "smooth" experiences by abstracting away unneeded steps and information for patrons, allowing value to be delivered in the background. The platform's technology is defined by "Speed and Accuracy which translates into Smoothness and Serendipity as an experience". Philosophically, Wellim is not concerned with moral discussion but is instead hyper-focused on a "praxis approach to action-reaction" within existing models. It is based on the principle that through a pure computer interface, it is possible to impose one's will on large groups of individuals (such as a hotel staff) without direct communication with a single new human party. This tests an individual's "freedom to move," making Wellim a "spiritual statement about self reflection within a shared system".
03 Research background & intellectual lineage
03 Research background & intellectual lineage
The intellectual lineage of Wellim is grounded in over a century of sociological and economic thought, beginning with Thorstein Veblen's foundational work on "conspicuous consumption" in The Theory of the Leisure Class (1899). Veblen first articulated how the consumption of goods serves as a powerful signal of social status.
The intellectual lineage of Wellim is grounded in over a century of sociological and economic thought, beginning with Thorstein Veblen's foundational work on "conspicuous consumption" in The Theory of the Leisure Class (1899). Veblen first articulated how the consumption of goods serves as a powerful signal of social status.
This classical understanding is refined by modern consumer psychology, particularly the work of Han, Nunes, and Drèze (2010), which provides a crucial taxonomy of affluent consumers. Their research distinguishes the "Patrician"—a high-wealth individual with a low need for status—who prefers inconspicuous signals recognizable only to other elites. This insight is central to Wellim's philosophy of delivering quiet, high-value recognition that affirms a patron's status within their in-group, rather than broadcasting it to the masses.
This classical understanding is refined by modern consumer psychology, particularly the work of Han, Nunes, and Drèze (2010), which provides a crucial taxonomy of affluent consumers. Their research distinguishes the "Patrician"—a high-wealth individual with a low need for status—who prefers inconspicuous signals recognizable only to other elites. This insight is central to Wellim's philosophy of delivering quiet, high-value recognition that affirms a patron's status within their in-group, rather than broadcasting it to the masses.
This focus on nuanced recognition directly addresses a critical failure in the modern hospitality market. Foundational research by Dowling and Uncles (1997) and more recent studies by Boston Consulting Group (Favier et al., 2024) confirm that traditional, points-based loyalty programs are largely ineffective at fostering true brand allegiance, instead creating a transactional "deal loyalty" that is misaligned with the motivations of the affluent.
This focus on nuanced recognition directly addresses a critical failure in the modern hospitality market. Foundational research by Dowling and Uncles (1997) and more recent studies by Boston Consulting Group (Favier et al., 2024) confirm that traditional, points-based loyalty programs are largely ineffective at fostering true brand allegiance, instead creating a transactional "deal loyalty" that is misaligned with the motivations of the affluent.
Wellim's proposed solution—engineering "serendipity"—is scientifically validated by research from Kim et al. (2021). Their work demonstrates that positive, unexpected encounters can generate significantly higher satisfaction than experiences consumers choose for themselves, provided the marketer's role remains invisible. This "Serendipity Effect" is the psychological engine Wellim aims to harness by operating seamlessly in the background.
Wellim's proposed solution—engineering "serendipity"—is scientifically validated by research from Kim et al. (2021). Their work demonstrates that positive, unexpected encounters can generate significantly higher satisfaction than experiences consumers choose for themselves, provided the marketer's role remains invisible. This "Serendipity Effect" is the psychological engine Wellim aims to harness by operating seamlessly in the background.
The ability to engineer such experiences at scale is made possible by a critical technological evolution from Large Language Models (LLMs) to Large Action Models (LAMs), as defined by platforms like Salesforce (n.d.). While LLMs process information, LAMs are designed to execute complex, multi-step actions, providing the technical architecture to translate data-driven insights into a flawlessly orchestrated guest experience.
The ability to engineer such experiences at scale is made possible by a critical technological evolution from Large Language Models (LLMs) to Large Action Models (LAMs), as defined by platforms like Salesforce (n.d.). While LLMs process information, LAMs are designed to execute complex, multi-step actions, providing the technical architecture to translate data-driven insights into a flawlessly orchestrated guest experience.
The platform's "invitation-only" model is informed by sociological studies on elite firms, such as the work of Ashley and Empson (2017). Their research on the "professional project" explains how elite groups operate a process of "social closure" to maximize status and rewards by restricting access. Wellim provides a technological mechanism for enacting this principle, creating a powerful sense of exclusivity and belonging for its patrons.
The platform's "invitation-only" model is informed by sociological studies on elite firms, such as the work of Ashley and Empson (2017). Their research on the "professional project" explains how elite groups operate a process of "social closure" to maximize status and rewards by restricting access. Wellim provides a technological mechanism for enacting this principle, creating a powerful sense of exclusivity and belonging for its patrons.
This entire framework is built upon a conscious acknowledgment of the ethical responsibilities inherent in a data-driven system, including principles of data privacy as outlined by GDPR and the mitigation of algorithmic bias (GDPR.eu, n.d.; IBM, n.d.). The financial viability of this approach is substantiated by case studies demonstrating the significant ROI of hyper-personalization, which can increase revenue by over 50% compared to conventional methods (Kortical, n.d.).
This entire framework is built upon a conscious acknowledgment of the ethical responsibilities inherent in a data-driven system, including principles of data privacy as outlined by GDPR and the mitigation of algorithmic bias (GDPR.eu, n.d.; IBM, n.d.). The financial viability of this approach is substantiated by case studies demonstrating the significant ROI of hyper-personalization, which can increase revenue by over 50% compared to conventional methods (Kortical, n.d.).
04 Process & methodology
04 Process & methodology
Wellim's methodology is executed through a pragmatic, data-centric strategy focused on identifying and acting upon high-value opportunities.
Wellim's methodology is executed through a pragmatic, data-centric strategy focused on identifying and acting upon high-value opportunities.
Research: Patron Value Modeling
Research: Patron Value Modeling
The process begins by researching and finding the historical spend of patrons at the highest-end properties. This data is then broken down and itemized to find patterns in guest spending behavior.
The process begins by researching and finding the historical spend of patrons at the highest-end properties. This data is then broken down and itemized to find patterns in guest spending behavior.
Experimentation: Predictive Return Calculation
Experimentation: Predictive Return Calculation
Using the researched data, a calculation is made to determine an expected financial return for these specific guests, should they choose to stay at a property again, based on a set of conditions. This predictive insight — the expected spend — is then shared with the property's sales team.
Using the researched data, a calculation is made to determine an expected financial return for these specific guests, should they choose to stay at a property again, based on a set of conditions. This predictive insight — the expected spend — is then shared with the property's sales team.
Application: Proactive Engagement & ROI Substantiation
Application: Proactive Engagement & ROI Substantiation
The property's sales team is prompted to make targeted offers to these high-value patrons. These offers are designed to deliver a better experience, justified by the positive expected value (EV) of attracting that patron for a stay. The platform then tracks and provides proof of the revenue generated from bookings created through Wellim and affiliates all additional revenue from sources like F&B, entertainment, and merchandising.
The property's sales team is prompted to make targeted offers to these high-value patrons. These offers are designed to deliver a better experience, justified by the positive expected value (EV) of attracting that patron for a stay. The platform then tracks and provides proof of the revenue generated from bookings created through Wellim and affiliates all additional revenue from sources like F&B, entertainment, and merchandising.
05 UI in practice
05 UI in practice
Home Screen
Home Screen
Search for the best hotels around your favourite destinations around the globe.
Search for the best hotels around your favourite destinations around the globe.
Explore the Globe
Explore the Globe
Navigate our interactive globe to easily locate hotels around your preferred destination.
Navigate our interactive globe to easily locate hotels around your preferred destination.
Property Details Screen
Property Details Screen
View detailed information about the hotel and rooms before reserving them, ensuring total transparency between the guest and the hotel.
View detailed information about the hotel and rooms before reserving them, ensuring total transparency between the guest and the hotel.
Your Profile
Your Profile
In the accounts page, you will be able to see a list of all the bookings you have made with detailed information on all of the bookings.
In the accounts page, you will be able to see a list of all the bookings you have made with detailed information on all of the bookings.
Your Credits
Your Credits
Invite friends and earn credits. Get up to 2% back on every booking they make — helping you travel for free or for less.
Invite friends and earn credits. Get up to 2% back on every booking they make — helping you travel for free or for less.
06 System architecture
06 System architecture
Wellim's architecture is that of an insights-driven service that uses a modern, data-driven approach. Its function is to translate raw data into actionable financial opportunities for hospitality providers.
Wellim's architecture is that of an insights-driven service that uses a modern, data-driven approach. Its function is to translate raw data into actionable financial opportunities for hospitality providers.
Data Model
The core of the data model is built upon the historical and itemized spend of patrons at high-end properties. The system analyzes this data to find patterns and calculate a conditional expected return for each high-value guest.
Data Model
The core of the data model is built upon the historical and itemized spend of patrons at high-end properties. The system analyzes this data to find patterns and calculate a conditional expected return for each high-value guest.
Personalization Framework
Unlike customer-facing personalization, Wellim personalizes opportunities for the property. It matches a patron's spending potential with a property's ability to offer an enhanced experience, creating a positive EV scenario.
Personalization Framework
Unlike customer-facing personalization, Wellim personalizes opportunities for the property. It matches a patron's spending potential with a property's ability to offer an enhanced experience, creating a positive EV scenario.
Key Components
The architecture includes modules for data ingestion (patron spend), predictive analytics (calculating expected return), an offer management interface for sales teams, and ROI tracking dashboards for management.
Key Components
The architecture includes modules for data ingestion (patron spend), predictive analytics (calculating expected return), an offer management interface for sales teams, and ROI tracking dashboards for management.
07 Use cases & proposed pilots
07 Use cases & proposed pilots
Wellim is designed to provide distinct value to each stakeholder in the high-end hospitality ecosystem.
Wellim is designed to provide distinct value to each stakeholder in the high-end hospitality ecosystem.
For Properties
Enables the property to "shine as it always should have" by generating more money that can be reinvested into enhancing the guest experience.
For Properties
Enables the property to "shine as it always should have" by generating more money that can be reinvested into enhancing the guest experience.
For Management Groups
Allows management to generate more revenue without complex integrations or unproven spending, making their sales and finance departments appear highly effective to property owners.
For Management Groups
Allows management to generate more revenue without complex integrations or unproven spending, making their sales and finance departments appear highly effective to property owners.
For Brands
Strengthens brand loyalty by ensuring patrons associate the brand with the "best experiences".
For Brands
Strengthens brand loyalty by ensuring patrons associate the brand with the "best experiences".
For Patrons
Patrons receive a "better" experience without having to take any action other than being themselves. The service is positioned as exclusive: "if you're invited- because only the best are invited".
For Patrons
Patrons receive a "better" experience without having to take any action other than being themselves. The service is positioned as exclusive: "if you're invited- because only the best are invited".
08 Closing Insight
08 Closing Insight
Technology's advance to replace human-to-human instruction is accelerating, and service industries are at an inflection point. In this new reality, Wellim operates with a clear directive: to take action and innovate, not to engage in moral discussions. The organization is hyper-focused on a pragmatic, praxis-based approach to the existing models of action and reaction. The ultimate intent is simple: individuals who use Wellim will be more free to "smoothly express the conjunctions surrounding their circumstances," gaining a greater freedom of movement in a world of scarce resources.
Technology's advance to replace human-to-human instruction is accelerating, and service industries are at an inflection point. In this new reality, Wellim operates with a clear directive: to take action and innovate, not to engage in moral discussions. The organization is hyper-focused on a pragmatic, praxis-based approach to the existing models of action and reaction. The ultimate intent is simple: individuals who use Wellim will be more free to "smoothly express the conjunctions surrounding their circumstances," gaining a greater freedom of movement in a world of scarce resources.
× Ashley, L., & Empson, L. (2017). Understanding social exclusion in elite professional service firms: Field level dynamics and the ‘professional project’. Work, Employment and Society, 31(2), 211–229. https://doi.org/10.1177/0950017016632048
× Dowling, G. R., & Uncles, M. (1997). Do customer loyalty programs really work? Sloan Management Review, 38(4), 71–82.
× Favier, J., Portell, A., Tufft, C., & Üstüner, T. (2024, January 17). Loyalty programs are growing—so are customer expectations. Boston Consulting Group. Retrieved October 6, 2025, from https://www.bcg.com/publications/2024/loyalty-programs-customer-expectations-growing
× GDPR.eu. (n.d.). What is GDPR, the EU’s new data protection law? Retrieved October 6, 2025, from https://gdpr.eu/what-is-gdpr/
× Han, Y. J., Nunes, J. C., & Drèze, X. (2010). Signaling status with luxury goods: The role of brand prominence. Journal of Marketing, 74(4), 15–30. https://doi.org/10.1509/jmkg.74.4.15
× IBM. (n.d.). What is algorithmic bias? Retrieved October 6, 2025, from https://www.ibm.com/think/topics/algorithmic-bias
× Kim, A., Affonso, F. M., Laran, J., & Durante, K. M. (2021). Serendipity: Chance encounters in the marketplace enhance consumer satisfaction. Journal of Marketing, 85(4), 141–157. https://doi.org/10.1177/0022242921994564
× Kortical. (n.d.). Increasing revenue by 56% through hyper-personalised offers. Retrieved October 6, 2025, from https://kortical.com/case-studies/ai-powered-marketing
× Salesforce. (n.d.). What are large action models (LAMs)? Agentforce. Retrieved October 6, 2025, from https://www.salesforce.com/agentforce/large-action-models/
× Veblen, T. (1899). The theory of the leisure class: An economic study of institutions. The Macmillan Company.
↳ Stack Results
Wellim - A Treatise on Synchronized Movement
Give travelers a more personalized and memorable trip
01 Overview
Wellim is an insights-driven service engineered to expand opportunity within the world of hospitality and wellness. The service addresses the critical market dislocation caused by the diminishing returns of basic, brand-based loyalty programs, which are no longer sufficient to attract and retain the highest echelon of patrons (Dowling & Uncles, 1997; Favier et al., 2024). Wellim provides a modern, data-driven framework to empower unique properties to "treat their best with better." The platform's objective is to forge a more symbiotic relationship between patrons and properties by merging the emerging technological revolution in agentic AI with the timeless spirit of service. At its core, Wellim is a tool for synchronizing complex action models for mutual gain, enabling individuals and organizations to achieve their maximum potential for fluid, unimpeded movement.
02 Core Idea
The central idea of Wellim is to engineer "smooth" experiences by abstracting away unneeded steps and information for patrons, allowing value to be delivered in the background. The platform's technology is defined by "Speed and Accuracy which translates into Smoothness and Serendipity as an experience". Philosophically, Wellim is not concerned with moral discussion but is instead hyper-focused on a "praxis approach to action-reaction" within existing models. It is based on the principle that through a pure computer interface, it is possible to impose one's will on large groups of individuals (such as a hotel staff) without direct communication with a single new human party. This tests an individual's "freedom to move," making Wellim a "spiritual statement about self reflection within a shared system".
03 Research background & intellectual lineage
The intellectual lineage of Wellim is grounded in over a century of sociological and economic thought, beginning with Thorstein Veblen's foundational work on "conspicuous consumption" in The Theory of the Leisure Class (1899). Veblen first articulated how the consumption of goods serves as a powerful signal of social status.
This classical understanding is refined by modern consumer psychology, particularly the work of Han, Nunes, and Drèze (2010), which provides a crucial taxonomy of affluent consumers. Their research distinguishes the "Patrician"—a high-wealth individual with a low need for status—who prefers inconspicuous signals recognizable only to other elites. This insight is central to Wellim's philosophy of delivering quiet, high-value recognition that affirms a patron's status within their in-group, rather than broadcasting it to the masses.
This focus on nuanced recognition directly addresses a critical failure in the modern hospitality market. Foundational research by Dowling and Uncles (1997) and more recent studies by Boston Consulting Group (Favier et al., 2024) confirm that traditional, points-based loyalty programs are largely ineffective at fostering true brand allegiance, instead creating a transactional "deal loyalty" that is misaligned with the motivations of the affluent.
Wellim's proposed solution—engineering "serendipity"—is scientifically validated by research from Kim et al. (2021). Their work demonstrates that positive, unexpected encounters can generate significantly higher satisfaction than experiences consumers choose for themselves, provided the marketer's role remains invisible. This "Serendipity Effect" is the psychological engine Wellim aims to harness by operating seamlessly in the background.
The ability to engineer such experiences at scale is made possible by a critical technological evolution from Large Language Models (LLMs) to Large Action Models (LAMs), as defined by platforms like Salesforce (n.d.). While LLMs process information, LAMs are designed to execute complex, multi-step actions, providing the technical architecture to translate data-driven insights into a flawlessly orchestrated guest experience.
The platform's "invitation-only" model is informed by sociological studies on elite firms, such as the work of Ashley and Empson (2017). Their research on the "professional project" explains how elite groups operate a process of "social closure" to maximize status and rewards by restricting access. Wellim provides a technological mechanism for enacting this principle, creating a powerful sense of exclusivity and belonging for its patrons.
This entire framework is built upon a conscious acknowledgment of the ethical responsibilities inherent in a data-driven system, including principles of data privacy as outlined by GDPR and the mitigation of algorithmic bias (GDPR.eu, n.d.; IBM, n.d.). The financial viability of this approach is substantiated by case studies demonstrating the significant ROI of hyper-personalization, which can increase revenue by over 50% compared to conventional methods (Kortical, n.d.).
04 Process & methodology
Wellim's methodology is executed through a pragmatic, data-centric strategy focused on identifying and acting upon high-value opportunities.
Research: Patron Value Modeling
The process begins by researching and finding the historical spend of patrons at the highest-end properties. This data is then broken down and itemized to find patterns in guest spending behavior.
Experimentation: Predictive Return Calculation
Using the researched data, a calculation is made to determine an expected financial return for these specific guests, should they choose to stay at a property again, based on a set of conditions. This predictive insight — the expected spend — is then shared with the property's sales team.
Application: Proactive Engagement & ROI Substantiation
The property's sales team is prompted to make targeted offers to these high-value patrons. These offers are designed to deliver a better experience, justified by the positive expected value (EV) of attracting that patron for a stay. The platform then tracks and provides proof of the revenue generated from bookings created through Wellim and affiliates all additional revenue from sources like F&B, entertainment, and merchandising.
05 UI in practice
Home Screen
Search for the best hotels around your favourite destinations around the globe.
Explore the Globe
Navigate our interactive globe to easily locate hotels around your preferred destination.
Property Details Screen
View detailed information about the hotel and rooms before reserving them, ensuring total transparency between the guest and the hotel.
Your Profile
In the accounts page, you will be able to see a list of all the bookings you have made with detailed information on all of the bookings.
Your Credits
Invite friends and earn credits. Get up to 2% back on every booking they make — helping you travel for free or for less.
06 System architecture
Wellim's architecture is that of an insights-driven service that uses a modern, data-driven approach. Its function is to translate raw data into actionable financial opportunities for hospitality providers.
Data Model
The core of the data model is built upon the historical and itemized spend of patrons at high-end properties. The system analyzes this data to find patterns and calculate a conditional expected return for each high-value guest.
Personalization Framework
Unlike customer-facing personalization, Wellim personalizes opportunities for the property. It matches a patron's spending potential with a property's ability to offer an enhanced experience, creating a positive EV scenario.
Key Components
The architecture includes modules for data ingestion (patron spend), predictive analytics (calculating expected return), an offer management interface for sales teams, and ROI tracking dashboards for management.
07 Use cases & proposed pilots
Wellim is designed to provide distinct value to each stakeholder in the high-end hospitality ecosystem.
For Properties
Enables the property to "shine as it always should have" by generating more money that can be reinvested into enhancing the guest experience.
For Management Groups
Allows management to generate more revenue without complex integrations or unproven spending, making their sales and finance departments appear highly effective to property owners.
For Brands
Strengthens brand loyalty by ensuring patrons associate the brand with the "best experiences".
For Patrons
Patrons receive a "better" experience without having to take any action other than being themselves. The service is positioned as exclusive: "if you're invited- because only the best are invited".
08 Closing Insight
Technology's advance to replace human-to-human instruction is accelerating, and service industries are at an inflection point. In this new reality, Wellim operates with a clear directive: to take action and innovate, not to engage in moral discussions. The organization is hyper-focused on a pragmatic, praxis-based approach to the existing models of action and reaction. The ultimate intent is simple: individuals who use Wellim will be more free to "smoothly express the conjunctions surrounding their circumstances," gaining a greater freedom of movement in a world of scarce resources.
× Ashley, L., & Empson, L. (2017). Understanding social exclusion in elite professional service firms: Field level dynamics and the ‘professional project’. Work, Employment and Society, 31(2), 211–229. https://doi.org/10.1177/0950017016632048
× Dowling, G. R., & Uncles, M. (1997). Do customer loyalty programs really work? Sloan Management Review, 38(4), 71–82.
× Favier, J., Portell, A., Tufft, C., & Üstüner, T. (2024, January 17). Loyalty programs are growing—so are customer expectations. Boston Consulting Group. Retrieved October 6, 2025, from https://www.bcg.com/publications/2024/loyalty-programs-customer-expectations-growing
× GDPR.eu. (n.d.). What is GDPR, the EU’s new data protection law? Retrieved October 6, 2025, from https://gdpr.eu/what-is-gdpr/
× Han, Y. J., Nunes, J. C., & Drèze, X. (2010). Signaling status with luxury goods: The role of brand prominence. Journal of Marketing, 74(4), 15–30. https://doi.org/10.1509/jmkg.74.4.15
× IBM. (n.d.). What is algorithmic bias? Retrieved October 6, 2025, from https://www.ibm.com/think/topics/algorithmic-bias
× Kim, A., Affonso, F. M., Laran, J., & Durante, K. M. (2021). Serendipity: Chance encounters in the marketplace enhance consumer satisfaction. Journal of Marketing, 85(4), 141–157. https://doi.org/10.1177/0022242921994564
× Kortical. (n.d.). Increasing revenue by 56% through hyper-personalised offers. Retrieved October 6, 2025, from https://kortical.com/case-studies/ai-powered-marketing
× Salesforce. (n.d.). What are large action models (LAMs)? Agentforce. Retrieved October 6, 2025, from https://www.salesforce.com/agentforce/large-action-models/
× Veblen, T. (1899). The theory of the leisure class: An economic study of institutions. The Macmillan Company.
↳ Stack Results