Finding the Right Model for Your Use Case

Client Situation

A retail vendor was building a Sentiment Analysis application to analyze customer feedback and reviews. While they understood the need for an AI model to extract insights, they faced challenges in:

• Identifying the best-fit AI model for their specific use case.
• Evaluating whether off-the-shelf LLMs or custom-trained models would work better.
• Balancing accuracy, cost, and deployment efficiency for production use.
• Navigating the overwhelming number of available AI models and platforms.

The client needed expert guidance to ensure they selected the optimal model aligned with their business goals.

Our Advisory

Our team provided focused advisory services to help the client identify and evaluate the right model:

• Conducted a requirement assessment to understand data volume, feedback formats, and expected outcomes.
• Evaluated pre-trained LLMs (e.g., OpenAI GPT, Google Gemini) and potential open-source models for sentiment analysis.
• Benchmarked multiple models based on accuracy, response time, and cost efficiency.
• Advised on the feasibility of custom model fine-tuning for specific retail-focused sentiment nuances.
• Delivered a detailed comparison report with recommendations for the best-fit model for production deployment.
  

Outcome

• The client successfully selected a cost-effective, high-accuracy model tailored to their sentiment analysis needs.
• Achieved better sentiment granularity, identifying positive, neutral, and negative feedback with greater precision.
• Reduced time-to-deployment by leveraging pre-trained models while incorporating minor custom fine-tuning.
• Gained confidence in model scalability, enabling future analysis of growing volumes of customer feedback.
• Improved decision-making by transforming unstructured customer feedback into actionable insights.

Client Testimonial

“GenAI Protos helped us cut through the complexity and find the right AI model for our sentiment analysis app. Their expertise saved us weeks of exploration and delivered a clear, practical solution we could implement quickly.”