Client Overview

Aretolabs is an AI-powered moderation software platform dedicated to creating safe, supportive, and engaging spaces for sports communities. They specialize in assisting sports teams, particularly women athletes, who often face higher levels of online abuse compared to their male counterparts. By using cutting-edge AI, Aretolabs aims to protect athletes’ mental health and foster positive fan interactions, ensuring that online communities remain uplifting and empowering.

Challenges

While Aretolabs had a clear vision and valuable data at their disposal, they were facing challenges in scaling and refining their platform’s effectiveness. Some key challenges included:

• Maximizing Data Utilization: Although Aretolabs collected large amounts of data, they were looking to unlock more actionable insights that could drive better decision-making and more efficient moderation.
• Moderation Efficiency: While their team worked hard to manage abusive content, manual moderation processes were time-consuming and diverted focus from higher-priority tasks like fan engagement and community building.
• Escalating Toxicity: During high-stakes events, abuse and toxicity levels would spike, which disrupted fan interactions and detracted from the overall user experience.
• ROI Visibility: Aretolabs needed clearer KPIs to demonstrate the impact of their platform and how it supported their clients’ objectives.

Our Solution

We partnered with Aretolabs to enhance their platform by combining our expertise in data engineering, NLP, and ML to address these challenges and improve the overall user experience.

1. Actionable Insights through Dashboards
   • We helped laying foundations of the interactive dashboards that provided valuable KPIs for Aretolabs. These dashboards helped to track:
     • Volume and types of toxic messages intercepted.
     • Severity levels of abusive content.
     • Engagement patterns, including identifying top fans and trolls.
     • Time savings for social teams and athletes.
   • These insights enabled Aretolabs to better demonstrate the effectiveness of their platform and make data-driven decisions to continuously optimize their services.
2. Advanced NLP for Moderation
   • Using advanced NLP models, we empowered Aretolabs’ moderation tools to operate more accurately and efficiently:
     • Built real-time sentiment analysis and toxicity detection models to classify and assess messages.
     • Developed the Areto Score, a unique metric that quantified platform health and engagement.
     • Implemented multi-layered abuse detection to assess the severity of toxic comments, allowing for targeted interventions.
3. Scalable Data Engineering & Real-Time Processing
   • Designed scalable, real-time data pipelines that allowed Aretolabs to efficiently process high volumes of data from platforms like Facebook, Instagram, and TikTok.
   • Ensured that the raw data was cleaned, normalized, and transformed to fit the needs of the ML models, enabling accurate predictions and quick decision-making.
4. Business Impacts 
   • Time Savings: AI-driven moderation saved 16 hours/day in manual moderation efforts, significantly improving efficiency.
   • Engagement Growth:
     • A sports team achieved 108% growth in fan engagement.
     • The social media team of another sports team saved over 54+ hours/month on moderation tasks.
     • An athlete experienced 300% endorsement growth after reducing toxicity by 65%

The Tech Side

We worked closely with Aretolabs to architect a high-performance, scalable solution, leveraging the latest in data engineering, Natural Language Processing (NLP), and Machine Learning (ML) to elevate their moderation platform. Here’s a breakdown of the technical approach and tools we used to meet Aretolabs’ goals:

1. Data Engineering Excellence

The cornerstone of the solution was building robust data pipelines that could handle large volumes of real-time data efficiently. We implemented scalable architectures to ensure continuous data ingestion, processing, and delivery:

• Data Pipelines: For real-time data streaming and for orchestrating data workflows. This enabled seamless integration with external platforms like Facebook, Instagram, and TikTok, ensuring high data throughput and low-latency processing.
• ETL Process: We built an efficient ETL (Extract, Transform, Load) pipeline to normalize and prepare raw social media data, making it ready for ML model consumption. We used SQL querying and AWS Lambda for distributed data processing to handle massive data volumes.
• Data Storage: Data was stored in a Elastic search warehouse, providing highly scalable storage and advanced querying capabilities for future insights.

2. Natural Language Processing (NLP) & Sentiment Analysis

Aretolabs’ platform required sophisticated NLP capabilities to understand and classify user interactions. Here’s how we tackled it:

• Toxicity Detection: Advanced text classification models using spaCy and Hugging Face Transformers, specifically tuned to detect abusive language, hate speech, and trolls within fan engagement.
• Multi-Level Classification: Using TensorFlow and PyTorch, we built and trained models to classify messages into multiple levels of toxicity and severity. These models learned from Aretolabs’ unique data to provide custom-tailored classifications, ensuring high precision and recall.
• Real-Time Processing: We built the models to operate in real-time, ensuring immediate moderation of user interactions without delays. The models’ performance was continuously improved through active learning based on user feedback and labeled data.
• Sentiment Analysis: To gauge sentiment around athletes and fans, we employed BERT (Bidirectional Encoder Representations from Transformers) and fine-tuned it for domain-specific sentiment analysis tasks.
• Predictive Analytics: The AI system also predicts spikes in toxicity, abuse, and spam (e.g., during major sporting events like playoffs), allowing Aretolabs to preemptively moderate before issues escalate.

3. Scalable Infrastructure & Deployment

We helped build the solution to be highly scalable and production-ready:

• Microservices Architecture: The entire solution was deployed using Docker containers and orchestrated for auto-scaling and high availability. This made the system adaptable to spikes in demand, ensuring seamless user experience.
• CI/CD Pipeline: We set up an automated CI/CD pipeline using Github to streamline code integration, testing, and deployment, ensuring fast iteration and continuous improvement.
• MLOps: MLflow for model performance review and management, enabling easy versioning, tracking, and deployment of models. AWS SageMaker and Google Colabs were used for model deployment and monitoring, ensuring that the models are performant and up-to-date.

4. Proven processes = assured client success

Our collaboration was guided by strong project management practices to ensure timely and efficient delivery:

• Agile Methodology: Using weekly sprints, we were able to quickly adapt to new insights and deliver rapid results.
• Stakeholder Collaboration: We maintained close communication with Aretolabs’ in-house teams, customer-facing teams, and leadership, ensuring alignment with their goals and seamless integration with their existing systems.
• Efficient Execution: We delivered cost-effective, high-value solutions while ensuring comprehensive documentation and easy scalability for future needs.

Continuous Partnership: Throughout multiple phases of the project, we worked hand-in-hand with Aretolabs to continuously improve their platform and meet emerging needs.

Client feedback – report card for TotemX Labs

Key Highlights:

• Client Satisfaction: Aretolabs was highly satisfied with our understanding of their business goals and our ability to deliver solutions that aligned with those goals.
• Communication Excellence: Our transparency and regular updates ensured the client was well-informed throughout the project.
• Impactful Results: The data and ML implementations drove personalization, operational efficiency, and measurable business outcomes.
• Refined Scope: Aretolabs appreciated how we approached their challenges with multiple perspectives, refining the project scope to enhance precision rather than scale.

Next Steps: Advancing with Small Language Models (LLMs)

Looking to continue improving the precision and cost-effectiveness of their platform, we proposed Aretolabs shift to small language models (micro-models). These micro-models offer several advantages:

• Data Privacy: By keeping the data processing in-house, Aretolabs ensures better control over sensitive moderation data.
• Cost Efficiency: Small models reduce reliance on costly third-party APIs, resulting in lower operational costs.
• Customization and Control: Micro-models provide more flexibility, allowing Aretolabs to fine-tune their models for specific use cases like toxicity detection and fan engagement.
• Independence: This approach mitigates risks associated with external model drifts and third-party dependencies.

By adopting this approach, Aretolabs can continue to scale their platform while maintaining full control over their AI models.

Why Choose TotemXLabs

Just as we helped Aretolabs enhance their platform with innovative AI and data solutions, we can help you strategize, design, and execute transformative projects that align with your business goals.

Take the first step—schedule a free consultation with our tech experts today. Let’s turn your vision into measurable success.