AI-powered client engagement and personalization agent

Tasks

• Develop machine learning models to analyze client behavior, segment users, and predict preferences.
• Build a personalized recommendation engine for workout plans, services, and offers.
• Create a scheduling optimization model to align client availability with trainer resources.
• Integrate a large language model as the conversational core of client’s in-app virtual assistant.
• Fine-tune the LLM to reflect the company’s brand tone, gym-specific procedures, and customer support guidelines.
• Build an ETL pipeline to unify data from client’s mobile app, gym systems, and CRM/ERP into a centralized repository.
• Implement secure cloud storage for models, logs, and backups using AWS S3.

Challenges

Solutions

• Client data used for modeling was anonymized using salted one-way hashes; personally identifiable information was excluded from analytics workflows.
• Custom instructions, prompts, and reinforcement feedback were used to shape the assistant’s responses, ensuring brand consistency and accuracy in gym-related queries.
• Built a robust data pipeline to extract, clean, and centralize information from multiple systems, improving data reliability for ML models.
• Combined behavior prediction, segmentation, and recommendations into one agent that could adapt based on real-time user feedback and platform usage patterns.
• The mobile app was updated with a clear privacy policy and tools for users to view, export, or delete their personal data.

Outcomes

• Successfully deployed a secure and compliant AI assistant that recommends content, manages schedules, and engages clients in personalized conversations.
• Improved user satisfaction through more relevant recommendations and faster response times.
• Maintained data privacy and built trust with clients through transparent handling of sensitive information.
• Laid a scalable foundation for ongoing machine learning improvements and LLM updates.

Technologies Used