Type: Internal Project (Later sold to Highrise Studios and Academy)

Purpose and Problem Solved

FondLot was designed to address the lack of transparency and fairness in celebrity giveaways. In the entertainment industry, giveaways often lack verifiability, leading to frustration among fans and accusations of bias or fraud. FondLot solved this problem by providing a transparent and automated system where participants answer quiz-based challenges to qualify, and winners are chosen randomly by the system. The platform ensured immediate reward distribution, with funds or gifts sent directly to winners’ pre-linked accounts, removing any chance of human interference or favoritism.

This innovative approach served as a form of escrow for fanbases, guaranteeing integrity and fostering trust between celebrities and their followers.

Client or Ownership

FondLot started as an internal project developed by Haskade Agency. It was later acquired by Highrise Studios and Academy, an entity that recognized its potential for revolutionizing fan engagement.

Challenges and Pain Points Leading to Its Creation

1. Fraud in Giveaways: Fans often participated in giveaways without assurance that winners were genuine. Many celebrities or influencers conducted biased selections, rewarding unqualified participants or associates under the guise of random giveaways.
2. Lack of Verifiability: No public proof of participant qualification or winner selection led to skepticism and reduced fan engagement.
3. Burden of Manual Giveaway Management: For celebrities and their teams, manually tracking participation, scoring quizzes, and transferring prizes to winners was time-intensive and error-prone.
4. Transparency Concerns: Fans wanted to see a clear, publicly verifiable process for participation and winner selection to feel fairly treated.

Additional Context: The platform also sought to prevent situations where giveaways became popularity contests or fell prey to bots manipulating results.

Approach and Solutions

FondLot employed an Agile Development methodology to create an efficient and scalable platform. Key technologies and strategies included:

1. Technologies:
• Backend: Django, Celery, Redis for robust and efficient task management.
• Frontend: ReactJS for an intuitive, responsive user interface.
• Security: RSA encryption to protect sensitive data like participants’ bank account details.
• Machine Learning: Used for scoring participant responses by analyzing textual or descriptive answers to quiz questions and calculating a percentage match to ideal answers.
2. Features:
• Quiz-Based Entry: Participants answered quizzes crafted by the celebrity. Their answers were graded based on correctness or closeness to the expected response.
• Randomization for Fairness: After qualifying through quizzes, finalists were chosen at random to remove any bias in the final selection process.
• Instant Payment Automation: Winners’ linked bank accounts received the prize money or gift immediately after selection, facilitated by escrow-like functionality.
• Public Verifiability: A leaderboard or participant list (with anonymized identifiers) allowed fans to see who qualified and track the fairness of the selection process.
3. Additional Enhancements:
• Rate Limiting and Batch Requests: To handle excessive requests from participants, entries were batched and processed based on timestamps.
• Audit Trails: Every action, from quiz participation to winner selection and payment processing, was logged for accountability.
• Dynamic Quiz Formats: Celebrities could opt for multiple rounds of quizzes to engage fans further or filter participants more rigorously.

Challenges Faced During Development and Solutions

1. Grading Subjective Answers:
• Challenge: Answers to quizzes, especially descriptive ones, varied widely in phrasing and spelling, making it difficult to grade accurately.
• Solution: A machine learning model was trained to calculate a similarity score between participant answers and predefined ideal responses. This allowed for fair grading based on semantic similarity rather than exact matches.
2. Sensitive Data Security:
• Challenge: Storing and processing participants' bank details posed a risk of data breaches and regulatory violations.
• Solution: RSA encryption was implemented to ensure bank details were stored in an unreadable format, accessible only with decryption keys held securely by authorized personnel.
3. Bot and Fraud Prevention:
• Challenge: Preventing bots from spamming entries or exploiting the platform for undue advantage.
• Solution: CAPTCHA was implemented for entry submissions, and unusual activity patterns (e.g., multiple entries from the same IP) were flagged for review.
4. Managing High Traffic During Giveaways:
• Challenge: Popular giveaways attracted massive traffic, risking server slowdowns or crashes.
• Solution: Horizontal scaling, caching with Redis, and asynchronous task handling using Celery ensured the platform could handle large surges in activity smoothly.
5. Public Scrutiny and Skepticism:
• Challenge: Convincing users that the process was genuinely unbiased.
• Solution: By making the participant and qualifier lists public (with anonymization for privacy), users could verify the transparency of the system.

Outcome and Benefits

FondLot gained significant traction, becoming a reliable tool for celebrities and fanbases. It fostered trust by ensuring fairness and transparency, removing biases, and automating the entire giveaway process. The system's escrow-like payment mechanism also simplified reward distribution, reducing manual effort for celebrities and their teams.

However, some individuals with ill intentions or a preference for biased systems avoided adopting FondLot, as it eliminated avenues for manipulation or fraud.

Feedback from Acquisition

Upon its acquisition by Highrise Studios and Academy, FondLot was praised for its innovative approach to fan engagement and transparency. Highrise Studios integrated it into their ecosystem, leveraging it to provide a unique value proposition to clients in the entertainment industry.

Added Value: The project highlighted the power of combining machine learning with transparency-driven solutions in entertainment, further strengthening its reputation.

Role and Contributions

Haskade Agency was responsible for the entire lifecycle of FondLot, from conceptualization to development, testing, and deployment. The team also provided six months of post-acquisition support to ensure a smooth transition and integration with Highrise Studios' systems.

Lessons Learned and Insights Gained

1. Customer Relationship Management: Building a platform for celebrities highlighted the importance of understanding diverse client needs and managing expectations.
2. Compliance: Navigating data security and payment regulations provided valuable lessons in adhering to PCI DSS standards and ensuring secure transactions.
3. Open-Mindedness: Developing a transparent system for a traditionally opaque process required creativity and adaptability.
4. Automation Benefits: Automating critical processes like payment distribution and random selection reduced human error and increased efficiency.